Author: ijps

Phytochemical and Comparative Assessment of Bark and Root Extracts of Ixora barbata Roxb. ex. Sm. and Ixora coccinea L. Using High-Performance Thin Layer Chromatography Assay

June 24, 2022 Published by


Dipanwita Suryadipta Ghoshal, S. N. Kharat1 and Sangeeta Atul Godbole*

Department of Botany, Jai Hind College, Churchgate, Mumbai, Maharashtra 400020, 1Department of Botany, Svargiya Sanjhibhai Rupjhibai College of Arts, Science and Commerce, Silvassa (Union Territory of Dadra and Nagar Haveli and Daman and Diu) 396230, India

*Corresponding Author:

Sangeeta Atul Godbole

Department of Botany,

Jai Hind College,

Churchgate,

Mumbai,

Maharashtra 400020,

India

E-mail: [email protected]







Date of Received 04 December 2020
Date of Revision 02 November 2021
Date of Acceptance 14 May 2022
Indian J Pharm Sci 2022;84(3):604-616  

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DOI: 10.36468/pharmaceutical-sciences.956

Abstract

The present study deals with the extraction and quantification of phytochemicals, which play a key role in the pharmaceutical world. In this article, we focused on the study of plant secondary metabolites and analyzed ethanolic and methanolic extracts of bark and roots of two species of Ixora using the highperformance thin layer chromatography technique. Alcoholic extracts of bark and root of both the plants revealed the presence of alkaloids, terpenoids, glycosides, phenols, saponins, sterols, tannins, etc., but both extracts were devoid of flavonoids and coumarins. Analysis of alkaloid content revealed the highest amount of total alkaloid content in the methanolic extract of roots of Ixora coccinea (146 mg/ gm) followed by the methanolic extract of bark of Ixora coccinea (138 mg/g). The lowest total alkaloid content was observed in the methanolic extract of bark of Ixora barbata (64 mg/g). Both ethanolic, as well as methanolic extracts of Ixora coccinea, showed higher amounts of alkaloid content when compared to Ixora barbata in similar solvents. As per high-performance thin layer chromatography analysis, the highest amount of camptothecin was recorded in ethanolic extract of root of Ixora coccinea (117 μg/ml) followed by ethanolic extract of bark of Ixora barbata (64.43 μg/ml). The lowest amount of camptothecin was observed in methanolic extract of bark of Ixora coccinea (12.64 μg/ml). The results obtained from the present high-performance thin layer chromatography analysis also revealed that the above-mentioned selected plants are a source of a number of other phytoconstituents. This is the first reported study on the high-performance thin layer chromatography analysis of Ixora barbata. The study includes quantification of camptothecin compound in both methanolic and ethanolic extracts of Ixora barbata for which there are no previously published reports.

Keywords

Ixora species, phytochemicals, camptothecin, high-performance thin layer chromatography

Basic research is mainly done on plants, as they have
a huge variety of chemical compounds stored in
them. Plants play a vital role in each and every aspect
of life such as food, clothing, shelter, medicines, etc.
Medicines derived from plants help to serve against
varied ailments. Developing countries depend on
medicinal plants for maintaining the good health of
their population. Scientists throughout the world are
trying their best to explore more and more precious
assets of medicinal plants to help humanity. More
than 30 % of the pharmaceutical preparations,
available all over the world are based on plants[1].

The genus Ixora belonging to the family Rubiaceae
consists of about 400 species[2] of which 28 are
cultivated species[3]. About 30 species occur in India,
a large number of exotics are cultivated in gardens.

There exist several species of Ixora which have been
used as traditional medicine to treat a wide range of
diseases[1,4,5]. Almost all parts of the Ixora plant i.e.
the roots, leaves, bark as well as flowers are shown to
contain various active phytochemicals. Specific plant
parts of various species of Ixora are used for some
specific pharmacological actions[6]. Decoction of bark
is used as a tonic in anaemia, general debility and
treatment of sores. Dried, powdered flowers are used
for whooping cough. In some species of Ixora , flowers are used in dysentery, dysmenorrhoea, leucorrhoea,
haemoptysis and catarrhal bronchitis. Decoction
of flowers is also useful in ophthalmic conditions
as well as a source to treat ulcers. Fresh flowers
of some Ixora species are considered beneficial in
tuberculosis and hemorrhages[7]. Different species of Ixora exhibit different pharmacological actions and
they were used widely for many different ailments.
This suggests variations amongst different species of Ixora , in their phytochemical content, with respect to
quality as well as quantity. Thus, work on different
species of Ixora may reveal presence of some
novel phytoconstituents and quantities of important
phytoconstituents may be compared amongst
all available species to select the best source of
pharmacologically useful phytoconstituents. There
is a present need to screen various species of Ixora in
order to identify species with possibly larger amounts
of pharmacologically active phytoconstituents as
well as to check for presence of novel variations in
phytochemical content.

Ixora coccinea Linn. (I. coccinea) is also known as
Scarlet Ixora. It is a compact shrub or small glabrous
tree. The plant flowers practically throughout the
year, but it is at its best during the rainy season.
It is native to the Western Peninsula, now widely
cultivated throughout the tropics. This plant has
been known in India since ancient times and the root
has some repute in native medicine[8]. I. coccinea is
known to contain wide varieties of phytochemicals. I. coccinea flowers show cytotoxicity and antitumor
activity[9,10]. This activity is believed to be due to the
presence of some quinoline alkaloid[11]. Leaves are
used in diarrhoea, stomachache and febrifuge. Roots
are used as sedative, stomachic, gonorrhea, loss of
appetite and shown to stimulate gastric secretion
of bile, relieve abdominal pain, chronic ulcers and
in headache treatment[7,12,13]. Roots are known to
act as a cholagogue and to give relief from pain to
those suffering from dysentery[8]. I. coccinea is thus
well studied with respect to the presence of various
phytoconstituents. Some important phytoconstituents
have been isolated and these have been proved to
exert specific pharmacological effects, some of which
are mentioned above. Although a large amount of
literature is available on phytoconstituents present in I. coccinea, other species of Ixora still remain to be
screened for the presence of similar or novel active
phytoconstituents.

Ixora barbata (I. barbata) is a large glabrous shrub, introduced into the Kolkata Garden before
Roxburgh’s time, but its native country was unknown
till Kurz found it in the Andamans[4]. It is also known
as Bearded Ixora because barbata in Latin means
‘bearded’ and refers to the woolly mouth of the
corolla. Flowers are white and fragnant, and it blooms
in the month of April to June. The plant is commonly
cultivated in Kolkata and elsewhere in India[8]. As
per literature survey, no report is available for the
phytochemical constituents of the I. barbata, hence
there is an urge to check presence and quantities
of important phytochemical constituents as well
as screen for presence of novel phytoconstituents
or drugs present in this plant, comparable to its
pharmacological properties. On the other hand, I. coccinea is well studied with respect to the presence
of various phytoconstituents. Some important
phytoconstituents have also been isolated and these
have been proved to exert specific pharmacological
effects. Thus a comparative study was initiated to
investigate the presence as well as quantification of
important phytoconstituents in the above two species.

The anticancer activity of the leaves of I. coccinea was found principally due to the known alkaloid,
Camptothecin (CPT)[10]. CPT, is an isoquinoline
alkaloid and is one of the most promising anti-cancer
drugs of the 21st century[14]. The global demand for
CPT in 2002 was valued at United States (US) $
4045 million. Several water-soluble derivatives of
CPT are currently being used for treating ovarian and
colorectal cancer[15]. CPT was first discovered in the
Chinese deciduous tree as Camptotheca acuminata,
family Nyssaceae[16]. Later, the alkaloid has been
reported from several plant species, Nothapodytes nimmoniana[17]. Presence of CPT has also been
reported in the members of Icacinaceae, Olacaceae,
Rubiaceae and Apocynaceae[18]. The worldwide
market size for CPT derivatives (eg. topotecan and
irinotecan) reached 1.5 billion US $ in 2002[19]. The
main sources of the alkaloids are bark and roots
of Camptotheca acuminata[20] and Nothapodytes nimmoniana[21].

Recent interest in the possible anti-cancer effects
has focused the need for the extensive study of the
bioactive compound CPT and also there is an urgent
need to find the alternative sources of CPT from
plants so as to cater to the demands of the pharma
industry. Although I. coccinea shows leaves as the
main source of this drug, the main sources of the
alkaloids are bark and roots in case of Camptotheca acuminata[20] and Nothapodytes nimmoniana[21].
Thus, presence and quantity of alkaloid may vary in
different parts of the plant body as per plant species
or genus selected[18]. Preliminary phytochemical
analysis revealed the presence of alkaloids only in
the roots and bark of I. barbata and not in its flowers
or leaves in the current study. Thus, roots and bark
of I. barbata were selected for quantification and
comparison of alkaloid content with root and bark
of I. coccinea plant. The present study aims to be
a comparative phytochemical analysis and High-
Performance Thin Layer Chromatography (HPTLC)
assay of I. coccinea and I. barbata growing in the
natural habitat.

Materials and Methods

Collection and authentication of plant:

Plants were collected from Acharya Jagadish Chandra
Bose Indian Botanic Garden, Botanical Survey of
India, Central National Herbarium, Kolkata, India.
Identification and authentication was done at the centre
itself. A herbarium sheet of these plants have been kept
in the Botany research laboratory of Jai Hind College,
Mumbai. The reference No.: CNH/Tech.II/2016/27, Sp.
No. DG-01 and DG-02.

Preparation of the extract:

The collected plant specimens of bark and root were
separated, washed and dried in shade. The dried
material was then powdered using a grinder and
made into coarse powder which was stored in amber
coloured bottles. 1 g of dried powder of the plant
material i.e. the bark and root was then separately
extracted in 10 ml of 60 % methanol and in 10
ml of 60 % ethanol of High-Performance Liquid
Chromatography (HPLC) grade separately for 8 h in
a shaker. Extracts were cooled at room temperature,
then passed through Whatman’s filter paper No. 1 and
centrifuged at 10 000 rpm for 15 min (Remi, India)
[22]. The supernatant was then concentrated on a water
bath until a semi solid mass was obtained. This semi
solid mass was then used for further experimental
analysis.

Phytochemical analysis of the extracts:

Both the root and bark extracts were tested for the
presence of alkaloids, flavonoids, saponin, tannin,
steroid, triterpenoid, coumarin, carbohydrate,
glycosides, phenolic compounds so as to obtain its
chemical composition profile. Following standard procedures were used[23,24].

Test for alkaloids: Dragendorff’s test-In a test tube to
1 ml of the extract, add a few drops of Dragendorff’s
reagent (solution of potassium bismuth iodide) and
the colour obtained was noticed. Appearance of
orange colour indicates the presence of alkaloids.

Wagner’s test-2 ml of Wagner’s reagent (iodine
in potassium iodide) was added to the extract, the
formation of the reddish brown precipitate indicates
the presence of alkaloids.

Mayer’s test-To the extract, 2 ml of Mayer’s reagent
(potassium mercuric iodide) was added, a dull white
precipitate revealed the presence of alkaloids.

Hager’s test-3 ml of Hager’s reagent (saturated picric
acid solution) was added to the extract, formation
of the yellow precipitate confirms the presence of
alkaloids.

Test for flavonoids: Ferric chloride test-To the
extract when few drops of ferric chloride solution
was added then the formation of blackish red colour
indicates the presence of flavonoids.

Alkaline reagent test-When the extract was treated
with Sodium hydroxide (NaOH) solution, it showed
an increase in the intensity of yellow colour which
would become colourless in addition to a few drops
of dilute hydrochloric acid, indicating presence of
flavonoids.

Lead acetate solution test-Few drops of lead acetate
(10 %) solution when treated with the extract,
formation of yellow precipitate indicates the presence
of flavonoids.

Shinoda test-To the extract when few turnings
of magnesium and 1-2 drops of concentrated
Hydrochloric acid (HCl) were added, formation of
red/pink colour indicates the presence of flavones.

Potassium hydroxide (KOH) (1 %)-To the test
solution when KOH (1 %) were added then formation
of yellow colour indicates the presence of flavonoid.

Test for flavanones-To the solution, 10 % NaOH was
added; colour changes from yellow to orange shows
the presence of flavanones.

To the solution, when concentrated sulphuric acid
was added, orange to crimson red colour confirms
the presence of flavanones.

Test for saponins: Foam test-The extract was mixed
with 2 ml of water and shaken vigorously. Formation of foam persisting for 10 min indicates the presence
of saponins.

Test for tannins: Ferric chloride test-To the test
solution when ferric chloride was added, formation
of a dark blue or greenish black colour indicates the
presence of tannins.

Gelatin test-To the extract when 1 % gelatin solution
containing sodium chloride was added; formation of
white precipitates indicates the presence of tannins.

Test for steroids and triterpenoids: Liebermann
Burchard test-The crude extract was mixed with a
few drops of acetic anhydride, boiled and cooled.
Concentrated sulphuric acid was then added from the
sides of the test tube and observed for a brown ring
at the junction of two layers. Green colouration of
the upper layer and the formation of deep red colour
in the lower layer would indicate a positive test for
steroids and triterpenoids respectively.

Test for coumarin: 1 ml of 10 % NaOH was added
to the 1 ml of extract. Formation of yellow indicates
the presence of coumarins.

Test for glycosides: Keller Killiani Test-To the test
solution, few drops of glacial acetic acid and ferric
chloride solution was mixed. Concentrated sulphuric
acid was added and observed for the formation of two
layers. Lower reddish brown layer and upper acetic
acid layer which turns bluish green would indicate a
positive test for glycosides.

Bromine water test-Test solution was dissolved in
bromine water and was observed for the formation
of yellow precipitate to show a positive result for the
presence of glycosides.

Test for carbohydrates: Benedict’s test-To the
extract 5 ml of Benedict’s reagent was added and
boiled for 2 min and cooled. Formation of a red
precipitate showed the presence of carbohydrates.

Test for phenolic compounds: Ferric chloride test-
Extracts were treated with 3-4 drops of ferric chloride
solution; formation of bluish black colour indicates
the presence of phenols.

Test for proteins: Biuret test-Test solution was
treated with 10 % NaOH solution and two drops of
0.1 % of copper sulphate solution was added and
observed. Formation of violet/pink colour indicates
the presence of proteins.

Test for free amino acids: Ninhydrin test-Test
solution when boiled with 0.25 ml solution of ninhydrin which results in the formation of purple
colour suggesting the presence of free amino acids.

Determination of total alkaloid content by
Bromocresol Green (BCG) and phosphate buffer
test[25,26]:

Preparation of solutions: BCG solution (1×10-4)
was prepared by heating 69.8 mg BCG with 3 ml of 2
N NaOH and 5 ml distilled water until it is completely
dissolved, and the solution was diluted to 1000 ml
with distilled water. Phosphate buffer solution (pH
4.7) was prepared by adjusting the pH of 2 M sodium
phosphate (71.6 g Sodium dihydrogen phosphate
(Na2HPO4) in 1 l distilled water) to 4.7 with 0.2 M
citric acid (42.02 g citric acid in 1 l distilled water).
Atropine standard solution was made by dissolving
10 mg pure atropine (Sigma Chemical, USA) in 10
ml of distilled water.

Preparation of standard curve:

Accurately measure aliquots (0.2, 0.4, 0.6, 0.8, 1,
1.2, 1.4, 1.6 and 1.8 ml) of atropine standard solution
and transfer each to different test tubes. Then add 5
ml pH 4.7 phosphate buffer and 5 ml BCG solution
and shake the mixture. The complex formed was
extracted with 1, 2, 3 and 4 ml of chloroform by
vigorous shaking. The extracts were collected in a 10
ml volumetric flask and then diluted to adjust volume
with chloroform. The absorbance of the complex in
chloroform was measured at 470 nm against blank
prepared as above but without atropine.

Extraction:

Concentrated supernatant (1 g in 10 ml of ethanolic
(60 %) and methanolic (60 %) solution) was taken
and was dissolved with 2 N HCl and filtered.
From this, 1 ml of the solution was transferred in
a separatory funnel and washed with 10 ml of
chloroform (3 times). The pH was adjusted to neutral
with 0.1 N NaOH. Then 5 ml of BCG solution and 5
ml of phosphate buffer were added to this solution.
The mixture was shaken and the complex formed
was extracted with 1, 2, 3 and 4 ml of chloroform by
vigorous shaking. The extracts were collected in a 10
ml volumetric flask and then diluted to adjust volume
with chloroform. The absorbance of the complex in
chloroform was measured at 470 nm.

Total alkaloid content was expressed as mg of
atropine equivalents/g of extract.

Separation of alkaloid by HPTLC method[27,28]:

Preparation of plant extract and chemicals used: 1 g powder in 10 ml ethanolic and methanolic (60 %)
concentrated supernatant was taken for the analysis.
From obtained extract 20 mg of extract was dissolved
in 1 ml of HPLC grade ethanol and methanol and
sonicated for 5 min in Sonics-Vibra Cell, VCX-130,
an instrument was used. The standard compound CPT
was procured from Sigma Aldrich and the mobile
phases, ethyl acetate, methanol (SD-Fine) and HPLC
grade water was used for the present analysis.

Preparation of standard solution and linearity: The standard stock solution of CPT was prepared
by dissolving 5 mg standard compound powder in
5 ml of ethyl acetate:chloroform in the ratio 1:1 v/v
and sonicated for 5 min. From this stock (1 mg/ml),
seven different concentrations (100-700 μg/ml) of
each standard were prepared. The linearity of each
standard compound was determined by applying
standard solutions of different concentrations ranging
from 0.5-3.0 μg/spot. All the solvents used in the
analysis were of HPLC grade.

Chromatographic conditions: Chromatography
was performed on pre-activated (at 1100°) silica gel
60 F254 HPTLC plates (20×10 cm). Both, sample
and standard (10 μl each) compounds were applied
to the layer as 6.0 mm wide bands, positioned 8.0
mm from the bottom of the plate, using an automated
CAMAG Linomat 5, Thin Layer Chromatography
(TLC) applicator instrument with nitrogen flow
providing the delivery by 100 μl Hamilton syringe.

Detection and quantification of compound: TLC was performed on 20×10 cm HPTLC plates
using a sample applicator. The response for CPT
was measured for each band at 254 nm and 366
nm wavelengths, using CAMAG TLC scanner and
WinCat software. The compounds were investigated
according to their Retention factor (Rf) values
with the corresponding standards. Calculation of
percentage was done considering standard and
sample Rf, Area Under the Curve (AUC) and dilution
factor. For validation of the method, the calibration
curve was obtained by plotting the peak area against
the concentration of CPT, the spectrum obtained
from the samples was correlated to the standard
compound used. The percentage of CPT present in
ethanolic and methanolic extract was calculated by
comparison of the areas measured for the standard
solution. The peak area of CPT was obtained by
plotting a graph of peak vs. applied concentrations of studied constituents.

Chromatogram development: The sample loaded
on TLC plate was placed in glass-twin trough
developing chambers (10 mm ×10 mm, with metal lid)
previously saturated with solvent vapor with mobile
phase ethyl acetate:methanol:water (100:11:10 v/v/
v/v), for 30 min, at room temperature (24°±1°).

Photo-documentation: The developed plate was
dried to evaporate the solvents from the plate
by hot air dryer. The plate was kept in the photodocumentation
chamber (CAMAG Reprostar-3) and
the images were captured at Ultraviolet (UV) 254 nm
and 366 nm at daylight mode.

Derivatization: To derivatize, the plate was sprayed
with spraying reagent i.e. 20 ml of concentrated
sulfuric acid in 180 ml of cold methanol and it was
dried at 110° for 15 min on the hot plate. Immediately
after drying the plate was photo-documented in UV-
254 nm and UV-366 nm in daylight mode using
CAMAG-TLC equipment.

Statistical analysis:

All the experiments were performed in triplicate and
the data represented as the mean±standard deviation.

Results and Discussion

The curative properties of medicinal plants are perhaps
due to the presence of various secondary metabolites
such as alkaloids, flavonoids, terpenoids, glycosides,
phenols, saponins, sterols etc. Table 1 and Table 2,
shows the result of phytochemical screening of I. coccinea and I. barbata root and bark extracts in
ethanolic and methanolic solvents. These plant extracts
are rich in phytochemical compounds such as alkaloids,
terpenoids, glycosides, phenols, saponins, sterols,
tannins, etc., but devoid of flavonoids and coumarin.
Thus, making it useful in the detection of the bioactive
principles and subsequently it may lead to the drug
discovery and development and further these tests
facilitate their quantitative and qualitative separation of
pharmacologically active chemical compounds.




































S. No Chemical tests I. coccinea I. barbata
Ethanolic extract Methanolic extract Ethanolic extract Methanolic extract
1 Test for alkaloids        
  Dragendorff’s test +++ +++ ++ +++
  Wagner’s test +++ +++ +++ ++
  Mayer’s test – +++ ++ +
  Hager’s test +++ – ++ +
2 Test for flavonoids        
  Ferric chloride test – – – –
  Alkaline reagent test – – – –
  Lead acetate solution test – – – –
  Shinoda test – – – –
  KOH (1 %) test – – – –
  Test for flavanones – – – –
3 Test for saponin        
  Foam test – +++ ++ +++
4 Test for tannins        
  Ferric chloride test +++ +++ ++ ++
  Gelatin test ++ ++ ++ ++
5 Test for steroids and triterpenoids        
  Liebermann-Burchard test ++ ++ +++ ++
6 Test for coumarin – – – –
7 Test for glycosides        
  Keller-Killiani test – ++ + +
  Bromine water test – +++ + ++
8 Test for carbohydrates        
  Benedict’s test – +++ – +
9 Test for phenolic compounds        
  Ferric chloride test +++ – + –
10 Test for proteins        
  Biuret test + – – –
11 Test for free amino acids        
  Ninhydrin test ++ – + –

Table 1: Phytochemical Screening of the Bark Extracts of I. coccinea and I. barbata.




































S. No Chemical tests I. coccinea I. barbata
Ethanolic extract Methanolic extract Ethanolic extract Methanolic extract
1 Test for alkaloids        
  Dragendorff’s test ++ +++ +++ +++
  Wagner’s test +++ +++ ++ +++
  Mayer’s test + +++ ++ ++
  Hager’s test ++ ++ ++ ++
2 Test for flavonoids        
  Ferric chloride test – – – –
  Alkaline reagent test – – – –
  Lead acetate solution test – – – –
  Shinoda test – – – –
  KOH (1 %) test – – – –
  Test for flavanones – – – –
3 Test for saponin        
  Foam test – +++ +++ +++
4 Test for tannins        
  Ferric chloride test ++ ++ +++ ++
  Gelatin test ++ +++ ++ ++
5 Test for steroids and triterpenoids        
  Liebermann-Burchard test ++ – ++ ++
6 Test for coumarin – – – –
7 Test for glycosides        
  Keller-Killiani test ++ +++ + –
  Bromine water test +++ +++ + –
8 Test for carbohydrates        
  Benedict’s test +++ +++ – –
9 Test for phenolic compounds        
  Ferric chloride test + – + +
10 Test for proteins        
  Biuret test + – – –
11 Test for free amino acids        
  Ninhydrin test + – – –

Table 2: Phytochemical Screening of the Root Extracts of I. coccinea and I. barbata.

In the present study, the total alkaloid content of the
plant extracts were determined by BCG and phosphate
buffer test. A calibration curve was plotted for various
concentrations of atropine (fig. 1). It was observed that
methanol extracts gave better results for I. coccinea than
that of ethanol extracts and ethanol extracts gave better
results for I. barbata than that of methanol extracts.

Pharmaceutical-Sciences-Calibration

Fig. 1: Calibration curve of atropine.

The highest amount of alkaloid content was found in
the root extract of I. coccinea in methanolic solvent
146 mg/g followed by the bark extraction in methanolic
solvent 138 mg/g whereas the least amount was
observed in the bark extract of I. barbata in methanolic
solvent 64 mg/g (Table 3). I. coccinea gave a higher
amount of alkaloid content as compared to I. barbata.







S. No Parts used I. coccinea I. barbata
Ethanolic extract (mg/g) Methanolic extract (mg/g) Ethanolic extract (mg/g) Methanolic extract (mg/g)
1 Bark 114 138 88 64
2 Root 124 146 96 80

Table 3: Determination of Alkaloid Content of the Total Extracts of I. coccinea and I. barbata.

The calibration curve was prepared by plotting the
concentration of CPT vs. average area of the peak and
its linearity was in the range of 50-250 μg/ml for CPT
(fig. 2). The correlation coefficient was found to be
0.954. The calibration curve of CPT was obtained by
spotting CPT on HPTLC plate. After derivatization the
plate was scanned densitometrically at 366 nm, CPT
showed a single peak in HPTLC chromatogram at 0.41 Rf (fig. 3). The experiment was performed in triplicate
for reproducibility and accuracy, and was found to be
correct. The obtained data was analysed statistically.
The results obtained of CPT content in plant extract of
bark and root of I. barbata and I. coccinea in ethanolic
and methanolic solvents are as discussed below.

Pharmaceutical-Sciences-curve

Fig. 2: Calibration curve of standard CPT for HPTLC analysis.

densitogram

Fig. 3: HPTLC densitogram for the standard CPT.

HPTLC analysis in the root and bark extracts of I. barbata and I. coccinea in ethanolic and methanolic
solvents gave better indication of occurrence of the CPT
constituent in the plant. The highest amount of CPT
was recorded in ethanolic extract of root of I. coccinea (117 μg/ml) followed by bark of I. barbata (64.43 μg/
ml) and the lowest were observed in methanolic extract
of bark of I. coccinea (12.64 μg/ml) (Table 4).







S. No Parts used I. coccinea I. barbata
Ethanolic extract (µg/ml) Methanolic extract (µg/ml) Ethanolic extract (µg/ml) Methanolic extract (µg/ml)
1 Bark 54.60 12.64 64.43 36.41
2 Root 117 63.10 26.59 13.83

Table 4: Determination of CPT Content in the Extracts of I. coccinea and I. barbata by HPTLC Analysis (µg/ml).

The Rf value and retention area of CPT was found to
be 0.41 and area 5821 (fig. 3). The ethanolic extract of
the I. coccinea root showed 13 peaks and the 7th peak
with Rf value 0.36 and retention area 5913.7 (fig. 4)
was homologous to the standard CPT. The ethanolic
extract of the I. coccinea bark showed 10 peaks and the
3rd peak with Rf value 0.37 and retention area 2760.2
(fig. 5) was coinciding with the standard CPT. The
ethanolic extract of the I. barbata bark showed 8 peaks
and the 4th peak with Rf value 0.38 and retention area
3256.8 (fig. 6) was homologous to the standard CPT.
The ethanolic extract of the I. barbata root showed 9
peaks and the 3rd peak with Rf value 0.36 and retention
area 1344.2 (fig. 7) showed homology to the standard
CPT. Whereas the methanolic extract of the I. coccinea bark showed 10 peaks and the 5th peak with Rf value
0.39 and retention area 639.2 (fig. 8) was coinciding
with the standard CPT. The methanolic extract of the I. coccinea root showed 12 peaks and the 9th peak
with Rf value 0.40 and retention area 3189.8 (fig. 9)
was homologous to the standard CPT. The methanolic
extract of the I. barbata bark showed 8 peaks and the
5th peak with Rf value 0.36 and retention area 1840.7
(fig. 10) was homologous to the standard CPT. The
methanolic extract of the I. barbata root showed 7 peaks
and the 3rd peak with Rf value 0.35 and retention area
698.7 (fig. 11) was homologous to the standard CPT.
HPTLC densitogram of all plant extract exhibited the
presence of total 13 types of phytoconstituents when scanned at 366 nm with Rf values ranging 0.00 to 0.87
(fig. 4-fig. 11). There is marked variation observed in
the presence of CPT content in the alcoholic extracts
of root and bark of I. coccinea and I. barbata (fig. 12).
There is marked variation observed in the presence
of CPT content as observed and compared in the
alcoholic extracts of root and bark of I. coccinea and I. barbata. Although I. barbata has lower CPT content,
it still remains a very important potential candidate
for extraction of CPT. The variation, whether can be
related to any observed pharmacological effects of these
plants, especially when used for particular ailments,
needs to be accessed. At times, higher or lower amounts
of various phytoconstituents or change in proportions
of various phytoconstituents to each other, may also
relate to better pharmacological properties. As these
drugs are used in traditional medicine as a composite
preparation without purifying the active medicinal
constituent, the medicinal effect probably lies in the
holistic composition i.e. variation in the ratio of various
phytoconstituents to each other in different species of
the same genera. Various genera of Ixora need to be
evaluated to determine the effect, if any, of variability
in proportion of phytoconstituents on their respective
reported pharmacological actions. The presence and
quantities of important medicinal phytoconstituents
like CPT need to be evaluated in various species of Ixora. This will help to find many better species as
sources, for extraction of important medicinal alkaloids like CPT.

ethanolic

Fig. 4: HPTLC densitogram for CPT in the root of I. coccinea in ethanolic extract.

bark

Fig. 5: HPTLC densitogram for CPT in the bark of I. coccinea in ethanolic extract.

extract

Fig. 6: HPTLC densitogram for CPT in the bark of I. barbata in ethanolic extract.

barbata

Fig. 7: HPTLC densitogram for CPT in the root of I. barbata in ethanolic extract.

coccinea

Fig. 8: HPTLC densitogram for CPT in the bark of I. coccinea in methanolic extract.

methanolic

Fig. 9: HPTLC densitogram for CPT in the root of I. coccinea in methanolic extract.

HPTLC

Fig. 10: HPTLC densitogram for CPT in the bark of I. barbata in methanolic extract.

root

Fig. 11: HPTLC densitogram for CPT in the root of I. barbata in methanolic extract.

solvents

Fig. 12: CPT content in the root and bark extracts of I. coccinea and I. barbata in ethanolic and methanolic solvents.

In the present study, phytochemical investigation of
root and bark extracts of I. barbata and I. coccinea revealed the presence of bioactive compounds such
as alkaloids, terpenoids, saponins, etc. This study also
leads us to identify and isolate CPT from the extracts
of these plants using HPTLC assay. The alkaloid CPT
was found in large quantities in the root extract of I. coccinea in ethanolic solvent followed by in the bark
extract of I. barbata in ethanolic solvent whereas the
least quantity was observed in the bark extract of I.
coccinea in methanolic solvent. This also suggests
that the ethanolic solvent gave better yield than that of
methanolic solvent. This paper is also the first to quote
for the quantification of CPT compound in the extracts
of I. barbata. CPT is known to be used as an anti-cancer
compound and hence these plant extracts can be further
studied as a potential source of anticancer drugs.

Acknowledgements:

The authors are thankful to the Department of Botany,
Jai Hind College for the practical lab facilities provided
for this research work. Authors are also thankful to the
Institute of Science, Mumbai, for analytical (HPTLC)
facilities provided for carrying out this research work.

Conflict of interests:

The authors declared no conflict of interest.

References



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An Integrated Strategy Designed To Explore the Common and Diverging Mechanisms of Potential Drugs against the Novel Coronavirus Pneumonia Based on Network Pharmacology and In Silico Molecular Docking Technology

June 24, 2022 Published by


Q. Zhao1, G. H. Li, B. Zheng, P. Feng, P. Wang1 and X. Li2*

Department of Pharmacy, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang 310012, 1Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, Zhejiang 310007, 2School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, Zhejiang 310053, China

*Corresponding Author:

X. Li

School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, Zhejiang 310053, China

E-mail: [email protected]







Date of Received 10 January 2021
Date of Revision 24 October 2021
Date of Acceptance 16 May 2022
Indian J Pharm Sci 2022;84(3):617-630  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.957

Abstract

Drug repositioning may be a promising way to find potential therapies against coronavirus disease 2019. Although chloroquine and hydroxychloroquine showed controversial results against the coronavirus disease 2019 disease, the potential common and diverging mechanisms of action are not reported and need to be dissected for better understanding them. An integrated strategy was proposed to systematically decipher the common and diverging aspects of mechanism of chloroquine and hydroxychloroquine against coronavirus disease 2019-disease network based on network pharmacology and in silico molecular docking. Potential targets of the two drugs and coronavirus disease 2019 related genes were collected from online public databases. Target function enrichment analysis, tissue enrichment maps and molecular docking analysis were carried out to facilitate the systematic understanding of common and diverging mechanisms of the two drugs. Our results showed that 51 chloroquine targets and 47 hydroxychloroquine targets were associated with coronavirus disease 2019. The core targets include tumor necrosis factor, glyceraldehyde 3-phosphate dehydrogenase, lymphocyte-specific protein-tyrosine kinase, beta-2 microglobulin, nuclear receptor coactivator 1, peroxisome proliferator-activated receptor gamma and glutathione disulfide reductase. Both chloroquine and hydroxychloroquine had good binding affinity towards tumor necrosis factor (affinity=-8.6 and -8.4 kcal/mol, respectively) and glyceraldehyde 3-phosphate dehydrogenase (-7.5 and -7.5 kcal/mol). Chloroquine and hydroxychloroquine both had good affinity with angiotensinconverting enzyme 2, 3-chymotrypsin-like protease and transmembrane serine protease 2. However, hydroxychloroquine manifested better binding affinity with the three proteins comparing with that of chloroquine. Chloroquine and hydroxychloroquine could have potential to inhibit over-activated immunity and inflammation. The potential tissue-specific regulation of the two drugs against severe acute respiratory syndrome coronavirus 2 infection may related with the lung, liver, brain, placenta, kidney, blood, eye, et al. In conclusion, our data systematically demonstrated chloroquine and hydroxychloroquine may have potential regulatory effects on coronavirus disease 2019 disease network, which may affect multiple organs, protein targets and pathways. Routine measurements of the chloroquine and hydroxychloroquine blood concentrations and tailored therapy regimen may be essential. But, further rigorous and high quality randomized controlled clinical trials are warranted to validate the antiviral effects of chloroquine and hydroxychloroquine against severe acute respiratory syndrome coronavirus 2. Our proposed strategy could facilitate the drug repurposing efforts for coronavirus disease 2019 treatment.

Keywords

Coronavirus disease 2019, chloroquine, hydroxychloroquine, network pharmacology, molecular docking, blood concentration

Coronavirus Disease 2019 (COVID-19), the novel coronavirus pneumonia, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has devastated the world for nearly 2 y. The World Health Organization (WHO) declared COVID-19 as a pandemic on 11th March, 2020. According to the Johns Hopkins University, there are more than 243 million cases globally and over 4.9 million reported deaths in the world by 23rd October, 2021. Whereas there are huge investments towards the fight against COVID-19, there are still no specific drugs available against COVID-19. Veklury (remdesivir) was approved by the Food and Drug Administration (FDA) to treat COVID-19. Therefore, research towards the development of anti-COVID-19 drugs is still important.

Drug repositioning may be a promise way to find potential therapies against COVID-19. According to DrugBank database, Chloroquine (CQ) was first developed in 1940s for the treatment of malaria. Its current clinical indications include human immunodeficiency virus, systemic lupus erythematosus and rheumatoid arthritis. Hydroxychloroquine (HCQ), a CQ derivative, is a prescribed drug against uncomplicated malaria, rheumatoid arthritis, chronic discoid lupus erythematosus or systemic lupus erythematosus. Both CQ and HCQ are under investigation as potential treatment options for COVID-19. A multinational registry analysis failed to confirm the benefits conferred by CQ or HCQ in the treatment of COVID-19[1]. Besides, the WHO has halted the CQ or HCQ trials against COVID-19.

On the other hand, China has achieved first stage success on the combat with SARS-CoV-2 under the great efforts of the nation. The China plan, combining with Western medicine and traditional Chinese medicine, has played a major role in combating COVID-19[2]. The latest diagnosis and treatment guidelines on COVID-19 (8th version, 18th August, 2020) in China[3] recommended CQ phosphate trials while evaluating its efficacy. In addition, our study highlighted contradicting data concerning the clinical efficacies of CQ and HCQ against COVID-19 patients. We noted that CQ or HCQ serum or blood concentrations need further investigation, as the post-treatment serum drug concentration is directly associated with antiviral efficacy. Studies showed that the steady-state whole blood concentration of CQ was at least 16 μM, which should suffice in its antiviral effects with minimal toxicity[4]. “Expert consensus on CQ phosphate for the treatment of novel coronavirus pneumonia of China” recommended a therapy regimen of 500 mg twice daily for 7 d for patients weighing over 50 kg and 500 mg twice a day for the first 2 d and once daily for the rest of the 5 d for patients weighing ≤50 kg[5]. Besides, HCQ shows a wide variability among individuals[6], in terms of the efficacy and side effects.

Therefore, whereas CQ or HCQ may still harbor potential efficacies against the COVID-19 disease, their mechanisms of action need to be dissected.

However, the interactions between drug and disease are often non-linear and complex, traditional pharmacology investigations could not meet the relationships deciphering requirement. Network pharmacology[7], the next paradigm in drug discovery, can be applied to visualize and analyse the intrinsic information contained in drug-disease interaction network. Herein, an integrated strategy was proposed to explore the mechanism of CQ and HCQ action against COVID-19 based on both network pharmacology and in silico molecular docking. We applied network pharmacology to construct drug (CQ or HCQ)-target networks while network analysis was used to obtain functional sub-networks, then Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained with gene function enrichment analysis. Finally, the top ranked target genes were obtained and drug-target molecular docking was conducted. In addition, SARS-CoV-2 infection related target proteins such as Angiotensin- Converting Enzyme 2 (ACE2), 3-Chymotrypsin- Like protease (3CLpro) and Transmembrane Serine Protease 2 (TMPRSS2) were also used to carry out molecular docking with CQ and HCQ.

In summary, this study proposed an integrated strategy to systematically decipher the modes of action of CQ and HCQ from the molecular network view and explored the common and diverging aspects of these two drugs on the modulation of COVID-19 disease network. Our study provides an informative basis for further design and interrogation of CQ and HCQ on the treatment of COVID-19. However, more rigorous and high quality randomized controlled clinical trials are needed to provide definitive and reliable data. The strategy proposed herein could facilitate drug repurposing studies for COVID-19 treatment.

Materials and Methods

Collecting and sorting of drug targets:

Online public databases such as Drugbank (https:// www.drugbank.ca/), Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/), TargetNet (http:// targetnet.scbdd.com/), Data Recovery and Repair- Cloud Provider Interface (DRAR-CPI) (https://cpi. bio-x.cn/drar/), SwissTargetPrediction (http://www. swisstargetprediction.ch/) or PharmMapper (http:// lilab-ecust.cn/pharmmapper/) were used to collect and identify potential CQ or HCQ targets. We used an Area Under Curve (AUC) ≥0.7 and probability >0.9 as a target selection criteria in TargetNet. For DRAR- CPI, SwissTargetPrediction and PharmMapper, we used Z-score <-0.5, probability=1 and fit value >3.8 as the criteria. UniProt (https://www.uniprot.org/) and Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) were used to convert and unify the target gene names. We filtered and removed duplicate genes and then used the rest to construct networks for further analysis.

Target network construction and analysis:

Coronavirus pneumonia was used as a key word to retrieve disease related genes and adopted databases such as PubMed Gene (https://www.ncbi.nlm.nih. gov/gene/), GeneCards (https://www.genecards. org/), Coats Disease (CTD) (http://ctdbase.org/) and Online Mendelian Inheritance in Man (OMIM) (https://omim.org/). Co-expressed ACE2 genes were downloaded from paper of Wang et al.[8] and GeneMANIA prediction server. The collected COVID-19 associated genes were standardized and converted into official gene symbols in Homo sapiens species. The common genes between COVID-19 and CQ targets were identified and then used String (https://string-db.org/) to retrieve Protein-Protein Interaction (PPI) networks. Cytoscape 3.7.2 was applied to construct and analyse CQ regulation networks. The same procedure was applied for HCQ.

Target function enrichment analysis:

DAVID was used to conduct functional enrichment analysis on common drug and disease targets and KEGG pathways with p<0.05 were obtained. We then applied the grammar of graphics plot2 (ggplot2) in R 3.6.1 to draw bubble diagrams.

Tissue enrichment maps:

The 51 CQ target genes which overlapped with COVID-19 genes were imported to DAVID 6.8 to carry out tissue enrichment analysis. We applied the function of “Tissue_Expression (UP_TISSUE) and Functional Annotation Chart” (p<0.05) with the background set as Homo sapiens. Similarly, the 47 HCQ target genes were conducted following the same protocol of CQ.

Molecular docking analysis:

In order to validate the potential CQ or HCQ target genes and explore their potential modes of action against COVID-19 disease, we employed AutoDock Vina (http://autodock.scripps.edu/) to verify and dissect the potential interaction modes. We retrieved the CQ (PubChem CID: 2719) or HCQ (PubChem CID: 3652) structures from PubChem (https:// pubchem.ncbi.nlm.nih.gov/). On the other hand, we downloaded the three-dimensional structures of the target proteins from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) database (https://www.rcsb.org/). The top ranked CQ or HCQ target proteins and the proteins associated with SARS-CoV-2 infection, including ACE2 (PDB: 1R42, X-ray diffraction, 2.20 Å resolution) and 3CLpro (PDB: 6LU7, X-ray diffraction, 2.16 Å resolution), were obtained from the RCSB PDB database. We employed SWISS-MODEL database (https://swissmodel.expasy.org/repository/ uniprot/O15393?csm=C05B5531C8A311C7) to obtain the HUMAN_Homology model TMPRSS2. Docking energy scores were used to evaluate the binding affinity between the drugs and the targets.

Results and Discussion

Our analysis showed a total of 153 and 116 targets for CQ and HCQ, respectively. 5713 genes were associated with COVID-19. 51 CQ and 47 HCQ target genes were related with COVID-19 disease.

Using STRING, we obtained the PPIs for the drug- disease common genes, with which the COVID-19 disease regulation networks for CQ or HCQ were constructed (Cytoscape 3.7.2; fig. 1). CQ-target network contained 46 nodes and 95 edges with an average node degree of 4.13 (16 key targets degree >4.13). HCQ- target network had 42 nodes and 76 edges with the average node degree of 3.62 (18 key targets degree >3.62) as shown in Table 1.

IJPS-proportion

Figure 1: Target network for (A) CQ and (B) Hydroxychloroquine

Note: The node size and color are in proportion with node degrees. The bigger the node degree, the larger the size and deeper the color of the node






















CQ targets Degree HCQ targets Degree
TNF 18 TNF 18
GAPDH 17 NCOA1 8
LCK 10 B2M 7
B2M 9 PPARG 6
NCOA1 7 LCK 6
PPARG 7 GSR 6
GSR 7 RARA 5
PRKACA 6 VDR 5
VDR 6 TRIM21 5
RAC1 6 TLR7 5
CTSD 6 CYP2C9 5
HLA-E 5 ITGAL 5
CSNK2A1 5 HLA-E 4
ITGAL 5 CTSD 4
NOS3 5 HCK 4
TTR 5 POR 4
    HMOX1 4
    TTR 4

Table 1: The Targets and Node Degree Values for CQ and Hydroxychloroquine

The Molecular Complex Detection (MCODE) analysis was conducted to analyze clusters. CQ had 2 clusters, while HCQ had 3 clusters as shown in fig. 2.

IJPS-hydroxychloroquine

Figure 2: MCODE analysis of clusters from the (A) CQ or (B) Hydroxychloroquine target networks

Note: The node size and color are in proportion with node degrees, i.e. the bigger of node degree, the larger the size and deeper the color of the node

The common targets for CQ and COVID-19, and those for HCQ and COVID-19 were imported into DAVID, and then the GO enrichment and KEGG pathway analyses were conducted. A total of 14 KEGG pathways were enriched from CQ-target network as shown in fig. 3A (p<0.05), while 10 CQ pathways were retrieved from cluster 1 as shown in fig. 3B. Most of the retrieved pathways were involved in immunity and inflammation, virus infection, lipid metabolism and hematologic tumour development. On the other hand, there were 5 pathways enriched for HCQ as shown in fig. 3C, while 7 pathways were enriched from cluster 1 as shown in fig. 3D. The HCQ pathways were involved in immunity and inflammation, viral infection, synapse function and hematologic tumour development.

IJPS-treatment

Figure 3: KEGG pathway analysis for the CQ and hydroxychloroquine targets in the treatment of COVID-19, (A): CQ and COVID-19 co-targets; (B): The top 1 cluster for CQ; (C): Hydroxychloroquine and COVID-19 co-targets and (D) Top 1 cluster for hydroxychloroquine

The 51 CQ-COVID-19 associated target genes were mainly enriched in the lung, liver, brain, placenta, kidney, blood, platelet, testis, prostate, eye, epithelium, bone marrow, lymph, pancreas and skin as shown in fig. 4A. On the other hand, the 47 HCQ-COVID-19 associated target genes were enriched in the lung, liver, brain, placenta, blood, platelet, kidney, testis, epithelium, bone marrow, prostate, eye, heart and fetal brain as shown in fig. 4B. These data may infer the potential tissue-specific regulation of CQ and HCQ against SARS-CoV-2 infection.

IJPS-enrichment

Figure 4: Tissue enrichment maps for COVID-19 associated CQ and hydroxychloroquine gene targets, (A): 51 CQ-COVID-19 associated target genes and (B): 47 HCQ-COVID-19 associated target gene

To establish and validate the binding affinity and modes between the drugs and the targets, we employed molecular docking experiments. The core target genes for CQ were Tumor Necrosis Factor (TNF), Glyceraldehyde 3-Phosphate Dehydrogenase (GAPDH), Lymphocyte-Specific Protein-Tyrosine Kinase (LCK), Beta-2 Microglobulin (B2M), Nuclear Receptor Coactivator 1 (NCOA1), Peroxisome Proliferator-Activated Receptor gamma (PPARγ) and Glutathione Disulfide Reductase (GSR). And the core target genes for HCQ included TNF, NCOA1, B2M, PPARG, LCK and GSR as shown in Table 1. Protein structures were obtained from PDB database: TNF (PDB:6OP0, X-ray diffraction, 2.55 Å resolution), NCOA1 (PDB:6GEV, X-ray diffraction, 1.54 Å resolution), LCK (PDB:1QPC, X-ray diffraction, 1.60 Å resolution), B2M (PDB:6TDQ, X-ray diffraction, 1.60 Å resolution), PPARG (PDB: 3ET3, X-ray diffraction, 1.95 Å resolution ), GAPDH (PDB: 6IQ6, X-ray diffraction, 2.29 Å resolution) and GSR (PDB: 3DK9, X-ray diffraction, 0.95 Å resolution).

As shown in Table 2 and fig. 5-fig. 7, both CQ and HCQ had good binding affinity towards TNF (affinity=-8.6 and -8.4 kcal/mol, respectively) and GAPDH (affinity=-7.5 and -7.5 kcal/mol), but HCQ showed a better binding affinity towards GSR (affinity=-7.2 kcal/ mol). Both drugs showed marginal binding with LCK, B2M, PPARG, ACE2, 3CLpro and TMPRSS2 (affinity ranged from -7.0 to -5.0 kcal/mol). However, both drugs had a binding energy of more than -5 kcal/mol with NCOA1.

IJPS-docking

Figure 5: Molecular docking results showing the affinity of CQ and HCQ towards key protein targets

Note: The binding energies in kcal/mol were calculated by AutoDock Vina software, EquationEquation

IJPS-binding

Figure 6: Docking results showing the interaction modes of CQ and HCQ with key targets

Note: A, C, E, G, I and K indicate the binding of CQ with TNF, GAPDH, LCK, B2M, PPARG or GSR, respectively and B, D, F, H, J and L represent the interaction of HCQ with TNF, GAPDH, LCK, B2M, PPARG or GSR, respectively

IJPS-infection

Figure 7: Interaction modes of CQ and HCQ with SARS-CoV-2 infection related targets using AutoDock Vina software

Note: A, C and E indicate the CQ binding with ACE2, 3Clpro and TMPRSS2, respectively. While, B, D and F shows the interaction of HCQ with ACE2, 3CLpro and TMPRSS2, respectively







Drugs Affinity (kcal/mol)
ACE2 3CLpro TMPRSS2
CQ -6.5 -5.4 -5.2
Hydroxychloroquine -6.6 -6.1 -6.1

Table 2: Binding Affinity of CQ and HCQ with Proteins Associated with the SARS-COV-2 Infection

To elucidate the potential mechanisms underlying the binding differences between CQ and HCQ with their targets, the BIOVIA Discovery Studio v.16.1 was applied to assess the receptor-ligand interactions. Two- Dimensional (2D) diagram in the Discovery Studio was used to visualize and analyze the interaction modes of CQ and HCQ with TNF, GAPDH, LCK as shown in fig. 8, B2M, PPARG and GSR as shown in fig. 9. The detailed interaction types and sites are as listed in Table 3.

IJPS-hydrogen

Figure 8: 2D diagram showing the binding modes between CQ or HCQ with TNF, GAPDH and LCK

Note: A, B and C shows the interactions with CQ; while D, E and F indicates the binding modes with HCQ, EquationEquationEquation

IJPS-sulfur

Figure 9: Visualization of the binding modes between CQ and HCQ with B2M, PPARG and GSR using the Discovery Studio

Note: A, B and C indicates the interactions with CQ; while D, E and F shows the binding modes with HCQ, EquationEquationEquationand pi-pi stacked












































Targets Drug Interaction Types Sites
TNF CQ H-bond Chain B: TYR 119
Pi-pi T-shaped interaction Chain A: TYR 59
Pi-cation Chain C: TYR 59
Pi-alkyl and pi-sigma Chain A: LEU 57, TYR119; Chain C: TYR119
HCQ H-bond Chain C: TYR 151
Pi-pi stacked Chain C: TYR 59
Pi-sigma Chain A: TYR 59, LEU 57
GAPDH CQ H-bond Chain A: ALA 238
Pi-alkyl Chain H: LEU 203
HCQ H-bonds Chain A: ALA 238; Chain F: GLN 204
C−H bonds Chain A: ASN 239; Chain G: THR 52
Pi-alkyl Chain A: ALA 238
LCK CQ H-bond Chain A: THR 316
Pi-alkyl Chain A: LEU 251, ALA 271, LEU 371
Unfavorable positive-positive Chain A: LYS 273
HCQ C−H bond Chain A: ASP 382
Pi-pi stacked Chain A: TYR 318
Pi-alkyl interactions Chain A: LEU 251, ALA 271, LEU 371
B2M CQ H-bonds Chain D: ARG 98 and TRP 96
Salt-bridge Chain D: GLU 78
Pi-alkyl interactions Chain D: ARG 98
HCQ H-bonds Chain D: LYS 42, GLU 48, THR 72 and ASP 77
Pi-alkyl interactions Chain D: ILE 47 and LYS 42
PPARG CQ H-bond Chain A: SER 342
Pi-alkyl Chain A: ILE 262
HCQ C−H bond Chain A: CYS 285
Pi-donor H-bond Chain A: CYS 285
Pi-cation Chain A: HIS 449
Pi-sulfur Chain A: MET 364
Pi-pi stacked Chain A: PHE 363
Pi-pi T-shaped Chain A: PHE 363
Pi-alkyl Chain A: CYS 285
GSR CQ Pi-pi stacked interaction Chain A: PHE 78
Unfavorable donor-donor interaction Chain A: TYR 407
HCQ H-bond Chain A: LYS 93
C−H bond Chain A: SER 76
Pi-cation Chain A: HIS 75
Pi-pi T-shaped Chain A: PHE 94
Amide-pi stacked Chain A: LYS 93
Pi-alkyl Chain A: PHE 94

Table 3: CQ and HCQ Interaction Types and Modes With TNF, GAPDH, LCK, B2M, PPARG OR GSR

In order to understand the modes of binding between CQ or HCQ with ACE2, 3CLpro and TMPRSS2, we employed LigPlot+ v.2.2 software. HCQ had H-bond interactions with ACE2 (Chain A: Glu208), 3CLpro (Chain A: Thr 190 and Gln 192) and TMPRSS2 (Chain A: Arg 240) (Green dashed lines in fig. 10). Both CQ and HCQ shared most part of hydrophobic residues and the common residues are highlighted in red. Both CQ and HCQ were in the hydrophobic pockets which consisted of hydrophobic interactions between residues and drugs.

IJPS-residues

Figure 10: Analysis of the interaction modes of CQ or HCQ with ACE2, 3CLpro and TMPRSS2 by the use of LigPlot+ v.2.2 software

Note: The green dashed lines represent H-bonds, while hydrophobic interactions between the drugs and residues are shown in brick red arcs. The common residues are indicated in red and show hydrophobic interactions with CQ or HCQ

This study aimed to elucidate the common and diverging mechanisms of CQ and HCQ against COVID-19. We employed network pharmacology and in silico molecular docking tools to dissect the common and different pathways affected by the drugs. This study could provide more insights into the modes of action and may facilitate a better understanding of CQ and HCQ with their potential regulation effects against SARS-CoV-2 infection.

The study analyzed the top 6 target genes as shown in Table 1 and gene functions were retrieved from GeneCards. The main targets for CQ are TNF, GAPDH, LCK, B2M, NCOA1 and PPARG. On the other hand, TNF, NCOA1, B2M, PPARG, LCK and GSR are the key targets for HCQ. Briefly, TNF is a pro-inflammatory cytokine secreted by macrophages, which participates in the immune and inflammatory responses. GAPDH is involved in glycolysis, transcription, RNA transport, DNA replication and apoptosis. LCK plays an important role in the development and maturation of T cells and participates in signal transduction related to T-cell antigen receptors. B2M is a component of histocompatibility complex and is involved in the expression of peptide antigen to the immune system. NCOA1 is a nuclear receptor-assisted activator and participates in transcription activity in a hormone- dependent manner. PPARG is an important regulator of adipocyte differentiation and glucose status, and is involved in the regulation of energy metabolism. GSR is a key enzyme in the cellular antioxidant defense system.

Through network and functional enrichment analysis, our data showed that the CQ targets participate in the regulation of signalling pathways associated with viral infection, immune inflammation, bacterial infection, steroid metabolism, amino acid metabolism or drug metabolism enzymes. On the other hand, HCQ mainly involved pathways such as immune inflammation, drug metabolism and serotonergic nerve synaptic function. The regulation of immune inflammation and drug metabolism enzymes was common with CQ and HCQ. However, the drugs showed some divergence which may be related to their structures. CQ is more liposoluble, while HCQ shows regulation effect on nerve synapse.

Both CQ and HCQ had good binding affinity towards TNF (affinity=-8.6 and -8.4 kcal/mol, respectively) and GAPDH (affinity=-7.5 and -7.5 kcal/mol). However, HCQ showed a better binding affinity towards GSR (affinity=-7.2 kcal/mol), because HCQ had more H-bonds and binding interactions than CQ as shown in Table 3.

SARS-CoV-2 infection related target proteins, ACE2, 3CLpro and TMPRSS2 were also used to carry out molecular docking with CQ and HCQ. Our results showed that CQ and HCQ both had good affinity with ACE2, 3CLpro and TMPRSS2. However, HCQ manifested better binding affinity with the three proteins comparing with that of CQ. The common residues of CQ and HCQ constitute hydrophobic pocket, in which the target proteins showed interactions with CQ or HCQ. However, HCQ had H-bond interactions with ACE2 (Chain A: Glu208), 3CLpro (Chain A: Thr 190 and Gln 192) and TMPRSS2 (Chain A: Arg 240) (Green dashed lines in fig. 10), while CQ had no H-bond in the hydrophobic pocket. Other studies also showed a better binding affinity of HCQ with TMPRSS2 than that of CQ[9,10].

Previous studies on SARS-COV-2 in Vero E6 cell model showed that CQ had potential activity in the prevention and treatment of novel coronavirus[11]. HCQ could block the entry of SARS-CoV virus into human cells by changing the glycosylation of ACE2 receptor protein and played an inhibitory effect on the invasion of the virus[12]. In addition, COVID-19 patients exhibit an overactive immune inflammatory response, which is also known as “cytokine storm”, which causes detrimental effects to the patients[13]. Autopsy reports of patients who died from COVID-19 revealed substantial lung lesions, liver enlargement, hepatocellular degeneration and cardiovascular system damage[3]. Due to the fact that ACE2 is expressed in the myocardium, the heart has become an important target organ for SARS-COV-2. Studies have summarized the mechanisms of heart damage associated with SARS- COV-2 infection, providing a basis for CQ and HCQ to play roles in combating cardiac detriments[14].The role of CQ and HCQ in regulating immune inflammation may be the main mechanism for their potential anti- COVID-19 effects. Meanwhile, both drugs may be involved in the regulation of drug metabolic enzymes. Besides, CQ affects the metabolism of small lipid molecules and amino acids, and may also be involved in the protection of impaired cardiac and liver functions in COVID-19 patients. HCQ is involved in the regulation of synapses, and may play a role in arachidonic acid metabolism and neuroprotection.

What’s more, tissue enrichment analysis of the CQ and HCQ targets showed the potential regulation of susceptible tissue targets as shown in fig. 4. Both the 51 or 47 COVID-19-associated target genes for CQ or HCQ were enriched in the lung, liver, brain, placenta, kidney, blood, platelet, testis, prostate, eye, epithelium and bone marrow. Besides, CQ targets were exclusively enriched in the lymph, pancreas and skin, while HCQ targets were uniquely enrichment in the heart and fetal brain. ACE2 and its co-factor TMPRSS2 are primarily expressed in bronchial transient secretory cells[15], while the lung is the main organ that suffers the SARS-CoV-2 infection[16]. ACE2 receptors were also identified in gastrointestinal tract and the cholangiocytes of the liver. Hepatic dysfunction occurs in 14 %-53 % of COVID-19 patients[17]. On the other hand, study reported the first case of SARS-CoV- 2-associated meningitis/encephalitis[18]. So far, there are several studies discussing the potential invasion mechanisms of SARS-CoV-2 into the brain[19-21]. There are conflicting data on the effect of SARS- CoV-2 in female reproductive system. Some studies have reported the mechanisms of placenta invasion by the virus[22-24]. In addition, maternal COVID-19 may affect fetal development and phosphatidylcholine or choline supplements have been shown to help mitigate the detrimental fetal brain effects[25]. For hospitalized COVID-19 patients, 36.6 % developed acute kidney injury[26]. It has been shown that SARS-CoV-2 can infect podocytes and tubular epithelial cells which attributed to the renal abnormalities[27]. Hypoxia and hypercoagulability may also cause renal injury[28]. In the circulation system, morphological anomalies in the blood cells and coagulation dysfunction have been observed[29-31]. SARS-CoV-2 infection was associated with platelet hyperactivity and could contribute to blood coagulation[32]. In fact, COVID-19 often causes thrombosis during SARS-CoV-2 infection[33]. There is conflicting data on the effect of the SARS-CoV-2 on male reproductive. It has been reported that the human testis is a potential target for SARS-CoV-2[34] and ACE2 may be mediating the effect[35]. COVID-19 may cause significant seminiferous tubular injury, Leydig cells decrease and mild lymphocytic inflammation of the testis[36]. TMPRSS2 together with ACE2 are essential for the cell invasion of the SARS-CoV-2. TMPRSS2 is expressed in normal prostatic epithelium and is increased in malignant prostatic tissue. Report showed that expressed prostatic secretion of COVID-19 patients was absent of coronavirus[37]. Research indicated that nasolacrimal system is a conduit between the eye and the respiratory tract, so more attention should be paid to SARS-CoV-2 transmission through the eye[38]. SARS- CoV-2 appears to preferentially target respiratory epithelium. While ACE2 is mainly expressed in the respiratory epithelium, the different tissue epithelium, such as gastrointestinal epithelium, nasal cavity olfactory epithelium and conjunctival, limbal or corneal epithelium, are all susceptible to the SARS- CoV-2 infection[39-41]. There are few reports about SARS-CoV-2 invasion in the bone marrow. The first case reported the persistent pancytopenia attributed to the SARS-CoV-2 infection of bone marrow[42,43].

Our data showed that the CQ targets were exclusively enriched in the lymph, pancreas and skin, while HCQ targets were uniquely enrichment in heart and fetal brain. It has been demonstrated that severe and critical COVID-19 patients showed higher incidences of lymph node enlargement, compared with the ordinary patients[44]. Another study showed the first report of SARS-CoV-2 infection-associated hemophagocytic lymphohistiocytosis in autopsy[45]. Based on evidences concerning the multi-organ abnormalities attributed to the SARS-CoV-2, COVID-19 should be recognized as a multi-systemic and polyhedral disease. ACE2 receptors are expressed in various tissues, including respiratory tract, heart, kidney, gastrointestinal tract, liver, pancreas, nervous systems and skin[46]. ACE2 imbalance in the pancreas may cause acute beta-cell dysfunction[47]. Besides, the SARS-CoV-2 infection in the endocrine pancreas may activate Na+/H+ Exchanger 2 (NHE2) and increase lactate levels[48]. In addition, plenty studies have also reported various skin lesions and cutaneous manifestations of COVID-19, but the associated mechanisms need furth51er interrogation[49,50]. Single nucleus RNA sequencing study showed that pericytes with high expression of ACE2 might act as cardiac cell targets for SARS-CoV-2[51]. SARS-CoV-2 infection may cause direct and indirect damages to the heart, with abnormal cardiac injury biomarkers, structural and functional detriments. Myocarditis and heart failure are the common cardiac manifestations in COVID-19 patients[14,52].

Taken together, our data robustly demonstrate CQ and HCQ may have potential regulatory effects on COVID-19 disease network, which may affect multiple organs, protein targets and pathways. Despite the common and diverging aspects in the modes of multi- actions, CQ and HCQ both affect common pathways which are associated with SARS-CoV-2 entry and infection. The results indicate that both CQ and HCQ could have potential to inhibit over-activated immunity and inflammation. Routine measurements of the CQ and HCQ blood concentrations and tailored therapy regimen may be essential for individual COVID-19 patient. However, further rigorous and high quality randomized controlled clinical trials are warranted to validate the antiviral effects of CQ and HCQ against SARS-CoV-2. Our proposed strategy could facilitate the drug repurposing efforts for COVID-19 treatment.

Acknowledgements:

This research was funded by the Natural Science Foundation of Zhejiang Province (grant number LYY19H280005 and number LQ17H280001); the Scientific Foundation in Traditional Chinese Medicine of Zhejiang Province (grant number 2017ZQ008); the Natural Science Foundation of Hangzhou Medical College (grant number 2015B01); the Zhejiang Pharmaceutical Association (grant number 2018ZYY11); the “13th Five-Year” Chinese Medicine Key Discipline in Zhejiang Province-Chinese Medicine Quality and functional evaluation (grant number 2017- XK-A43 to J. Shi). We thank HOME for Researchers for its linguistic assistance during the preparation of this manuscript.

Conflict of interests:

The authors declared no conflicts of interest.

References



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Evaluation of Antidiabetic Activity of Vitis pedata in Alloxan Induced Diabetic Rats

June 24, 2022 Published by


Joyeeta Bhattacharya*, S. Ata1, S. Chakrabarty, S. K. Jha and P. Roy

Department of Pharmaceutical Technology, Adamas University, Kolkata, West Bengal 700126, 1Department of Pharmacology, East Point College of Pharmacy, Bengaluru, Karnataka 560049, India

*Corresponding Author:

Joyeeta Bhattacharya

Department of Pharmaceutical Technology,

Adamas University,

Kolkata,

West Bengal 700126,

India

E-mail: [email protected]







Date of Received 24 November 2020
Date of Revision 24 September 2021
Date of Acceptance 18 May 2022
Indian J Pharm Sci 2022;84(3):631-641  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.958

Abstract

The primary focus of the research was to evaluate the antidiabetic activity of the ethanolic and aqueous extract of Vitis pedata in alloxan induced rats. The entire study was divided into two phases: Phase 1 and phase 2. The phase 1 is initiated with the collection and authentication of the plant, followed by extraction with ethanol and water and finally screening of the phytochemical constituents of the extracts. The phase 2 is more inclined on the therapeutic effect of the plant extracts on experimentally induced diabetic rats. Several parameters such as estimating the body weight and liver weight, blood glucose level, total proteins, hemoglobin, serum albumin, serum urea, serum cholesterol levels were examined in the diabetic rats. The histopathological changes in the pancreas of diabetic rats were also studied. The aqueous extract of Vitis pedata showed a significant reduction in the blood glucose levels, lipid profile and serum biomarkers in diabetic rats, quite similar to the standard treatment of glibenclamide, whereas the ether extract showed a less effect compared to the other two extracts. The plant extracts also highlighted an improvement in the beta cell mass in islets of pancreas. Thus, the study of aqueous and ethanolic extract of Vitis pedata indicated a promising antihyperglycemic activity in alloxan induced diabetic rats. Furthermore, the study also opens the door for extended research to explicate the mechanism of action of the plant extracts.

Keywords

Diabetes mellitus, phytochemical, Vitis pedata, alloxan, antihyperglycemic activity

Diabetes mellitus has become a real issue of public health in most of the developing countries, where its prevalence is moving to a higher surge particularly in low and middle-income countries. According to World Health Organization (WHO), in 2016, diabetes was solely responsible for 1.6 million of deaths globally[1]. The ever-increasing growth of diabetic mellitus is attributed to several factors such as age, obesity, sedentary lifestyles and unhealthy eating habits[2]. In such economically under-developed countries, adequate treatment is either expensive or unavailable. The genre of medical science has witnessed a huge number of advancements in the development of synthetic pharmaceutical medicaments to combat the disease. Examples of these drugs can be classified into major chemical groups such as biguanides, sulphonylureas, thiazolidinediones, alpha (α)-glucosidase inhibitors and also insulin analogues. Each of these drugs has different mechanism as an antidiabetic agent. The management of diabetes mellitus by the existing therapeutic agents is failing to arrest the pathogenesis of the disorder. Although they possess hypoglycemic effect, the long term usage of these drugs is associated with many adverse effects such as gastrointestinal disturbances, nephrological complications, brain atrophy and hepatic disorders. The treatment of diabetes mellitus with insulin analogues also shows detrimental effects in the long term use[3,4]. The shooting price of the medicaments and the lack of availability of up-to-date treatment procedures are still not accessible to a major proportion of rural population especially in the developing countries. Thus, there is an urge amongst the researchers to develop alternating approaches to treat this disease with respect to the present therapeutic approach of diabetes mellitus[5]. The evolution of human civilization has witnessed the significance of plants and its parts as a remedy for several ailments. The paradigm shift from the synthetic drugs to the herbal medicines for the treatment of the diabetes mellitus, without any unwanted effects, appealed the scientists around the globe. The ethno botanical information reports state that huge number of plants may possess antidiabetic potential[6]. A report of the clinical trials conducted on human patients during the past few years affirmed the glycemic control properties of several medicinal plants such as Scoparia dulcis[7], Cinnamomum cassia[8], Ficus racemosa bark and Portulaca oleracea[9]. Most of the plants have been found to contain substances like glycosides, alkaloids, terpenoids, flavonoids etc., that are frequently implicated as having a specific mode of action of these plant drug or herbal formulation used for treating diabetes[10].

Vitis pedata (V. pedata) (also known as Cayratia pedata (C. pedata))[11] is a woody climber, sometimes vines, rarely small succulent trees, hermaphroditic or polygamomonoecious to polygamodioecious. It belongs to the family Vitaceae, which comprises about 14 genera and 900 species and it is distributed worldwide, but mostly in tropical and subtropical regions. It is locally known as Goalilata[12,13]. The ethno pharmacological claims for V. pedata included the utilization of its leaves as an anti-inflammatory agent[14]. The leaves of the plant is having astringent and haemostatic properties, which are being used in the treatment of diarrhea, hemorrhage, varicose veins, hemorrhoids, inflammatory disorder, pain, hepatitis and free radical related diseases and also as a wound healer on external use[15]. A number of research work on the plant extract have also highlighted its antimicrobial[16], anti-nociceptive[17], anti-arthritic and anti-oxidant properties[18].

To understand the rationalization of choosing this plant species for the research, we need to throw some light on the association of inflammation and its biomarkers on the pathogenesis of diabetes mellitus. Insulin resistance which is a key component of type 2 diabetes is majorly attributed with steatosis (accumulation of fats in liver) which is a resultant of obesity. This in turn causes decreased hepatic insulin sensitivity followed by increased fasting hyperglycemia. Extensive researches on the obesity has also reported the activation of two inflammatory pathways, namely stress-activated c-Jun N-Terminal Kinase (JNK) and the transcription factor Nuclear Factor kappa B (NF-κB). These inflammatory pathways are responsible for augmented adipokinesis through production of several cytokines in obese people thus making them more prone to develop metabolic diseases like diabetes mellitus[19].

Intensive researches have also suggested the role of Interleukin-1 (IL-1) in auto inflammatory process leading to destruction of beta cells in the islets of pancreas[20].

Pradhan et al. carried out a nested case control study among 27 628 women where he evaluated the levels of inflammatory biomarkers such as IL-6 and C-reactive proteins. The results highlighted the elevated levels of inflammatory biomarkers, thus confirming the possible role of inflammation in the onset of diabetes[21].

Several studies on V. pedata confirmed its anti-inflammatory studies. Rajmohanan et al. investigated the anti-inflammatory properties of C. pedata leaf extract in both in vivo and in vitro models[22]. Alcoholic extract of C. pedata (250 mg/kg and 500 mg/kg body weight (b.w.), orally (p.o.)) were efficient in reducing the formation of granuloma thus inhibiting the inflammation. The study also explored the mechanism of action of the extract which demonstrated the inhibitory action of cyclooxygenase in lipopolysaccharide macrophages and also extensively prevent the action of proteinase.

Furthermore, Rajendran et al. in his study confirmed the anti-inflammatory properties of aqueous and alcoholic extracts of V. pedata [17].

The significant correlation between inflammation and diabetes mellitus discussed above became the hallmark of our research. Since V. pedata, had been a traditional anti-inflammatory agent, we thought to investigate its anti-diabetic properties, which have not been conducted till date. The main objective of the proposed work was to screen the ethanolic and aqueous extract of the plant V. pedata and to evaluate of its anti-diabetic activity in alloxan induced rats.

Materials and Methods

Materials:

Alloxan (Spectrachem Pvt. Ltd. Company), sodium Tripolyphosphate (TPP) was procured from Sigma- Aldrich (Missouri (MO), United States of America (USA)) while water of High Performance Liquid Chromatography (HPLC) grade and acetic acid was obtained from Spectrochem (Mumbai, India), albumin estimation kit, cholesterol estimation kit, urea estimation kit, High-Density Lipoprotein Cholesterol (HDL-C) estimation kit were purchased from Span Diagnostics, glibenclamide (Darwin Formulations). Windows Excel (version 2003; Redmond, Washington), Statistical Package for Social Sciences (SPSS)/10.0 (SPSS, USA), SigmaPlot (version 6.0; Zendal Scientific, USA) softwares were used for data analysis.

Plant material:

The whole plant of V. pedata was collected from Howrah district (West Bengal) and authenticated by Botanist Dr. Jagadeesh Singh, Principal, East Point College of Pharmacy, Bangalore, Karnataka. The voucher specimen (No: Vitis pedata/02/EPCP/2013-2014) was preserved in the Department of Pharmacology laboratory of East Point College of Pharmacy for future reference. The plant was processed, powdered coarsely and coarse plant materials were used for extraction.

Preparation of plant material:

The collected plant material was cut into small pieces and shade dried. The dried material was then powdered by a mechanical grinder. The resulting powder was then processed for extraction with ethanol and water. The Ethanolic Extract of V. pedata (EEVP) and the Aqueous Extract of V. pedata (AEVP) were concentrated under reduced pressure and stored in desiccator.

Preliminary phytochemical analysis of EEVP and AEVP[19]:

Phytochemical analysis was carried out by using the standard procedures. Alkaloids, carbohydrates, flavonoids, glycosides, phytosterols/terpenes, proteins, tannins, saponins and lipids were qualitatively analyzed.

Alkaloids: EEVP and AEVP were separately dissolved in dilute Sulphuric acid (H2SO4) and filtered. The filtrate was treated with Dragendorff’s, Hager’s, Mayer’s and Wagner’s reagent separately. Appearance of reddish brown, yellow, orange brown and cream coloured precipitates in response to the above reagents respectively indicate the presence of alkaloids.

Carbohydrates: EEVP and AEVP were separately treated with Benedict’s, Fehling’s, Molisch’s and Barfoed’s reagents under suitable conditions. Appearance of reddish brown, purple ring at junction and brick red colour in response to the above reagents respectively indicates the presence of carbohydrates.

Flavonoids: EEVP and AEVP were separately treated with few ml of alcohol and were heated with magnesium ribbon and concentrated Hydrochloric acid (HCl) under cooling. Appearance of magenta red colour indicates the presence of flavonoids. A few ml of both the extracts were treated with Ferric chloride (FeCl3), an appearance of intense green colour was observed. The extracts were again treated with few ml of aqueous Sodium hydroxide (NaOH), appearance of yellow colour and changes to colourless with HCl indicate the presence of flavonoids. EEVP and AEVP were treated with lead acetate (10 %), formation of yellow precipitates indicates presence of flavonoids.

Glycosides: Under suitable conditions, small quantity of the EEVP and AEVP were subjected to Baljet’s, Borntrager’s, Keller-Killiani, Legal and Modified Borntrager’s test respectively. Appearance of yellow-orange, pink-violet brown, lower layer reddish brown and upper layer bluish green, pinkred and rose pink-cherry red colour in response to the above tests respectively indicates the presence of glycosides.

Phytosterol/Terpenes: The EEVP and AEVP were treated with Lieberman-Burchard, Salkowski’s and Zak’s tests respectively under suitable condition. Appearance of brown ring at junctions and upper layer turns green, lower layer turns red-yellow and purple-olive green colour in response to the above tests respectively indicate the presence of phytosterols/terpenes.

Proteins: The EEVP and AEVP were respectively subjected to Biuret, Million’s, Xanthoproteic and Ninhydrin tests. Appearance of blue colour, yellow stain, yellow precipitate and blue colour, in response to the above reactions respectively indicates the presence of proteins.

Tannins: Small quantity of EEVP and AEVP were dissolved in water and to that FeCl3 (5 %) or gelatine solution (1 %) or lead acetate solution (10 %) was added. Appearance of dark blue colour with FeCl3 or precipitation with other reagent indicates the presence of tannins and phenols.

Saponins: Small quantity of EEVP and AEVP were mixed with water in a test tube and shaken well for 15 min. Foam was observed, it indicates the presence of saponins.

Lipids: Few drops of 0.5 N alcoholic NaOH was added to small quantity of EEVP and AEVP respectively with a few drops of phenolphthalein. The mixture is heated on water bath for 1-2 h, formation of soap or partial neutralisation of alkali was observed, it indicates the presence of lipids.

Experimental animals:

Albino Wistar rats weighing 150-200 g were procured from Ganesh animal supplier, Vijayanagar, Bangalore. They were maintained in the animal house of East Point College of Pharmacy for experimental purpose. Animals were kept under controlled conditions of temperature at 27°±2° and 12 h light-dark cycles for 1 w. They were housed in polypropylene cages with paddy husk as bedding. The animals were fed with commercially available rat pellet diet. Water was allowed ad libitum under strict hygienic conditions. All the studies conducted were approved by the Institutional Animal Ethical Committee (IAEC) of East Point College of Pharmacy, Bangalore (REF-EPCP/IAEC/02/2013- 14) according to prescribed guidelines of Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Government of India.

Oral Glucose Tolerance Test (OGTT) in normal rats[12]:

The OGTT was performed in rats weighing 150-200 g. The selected animals were fasted for 16 h before the commencement of experiment. However, they were allowed for free access to water. These rats were categorized into four groups having six in each group. Rats of all groups were administered with glucose 2 g/kg, p.o. 30 min after drug administration. Blood samples were collected from the tail vein prior to drug administration and at 30, 90 and 150 min of glucose administration.

Group-I: Saline was supplied and serves as control.

Group-II: Animals received a dose of 5 mg/kg b.w., p.o. of standard drug (Glibenclamide).

Group-III: Animals received a dose of 400 mg/kg b.w., p.o. of AEVP.

Group-IV: Animals received a dose of 400 mg/kg b.w., p.o. of EEVP.

Experimentally induced diabetes mellitus[13]:

The Wistar albino rats 150-200 g of either sex were allowed to fast for 24 h prior to the experiment. The diabetes was induced by injection of single dose of alloxan 120 mg/kg of b.w. in 0.3 % sodium Carboxymethyl Cellulose (CMC) by intraperitoneal (i.p) route. After 1 h of alloxanisation the animals were given feed ad libitum and 5 % dextrose solution for a day to avoid early hypoglycemic phase. The blood glucose was monitored after every 24 h of alloxanisation. The diabetic condition was observed at 48 h and 72 h of alloxan injection. The diabetic rats (glucose level>300 mg/dl) were separated and used for the study. The animals were divided into five groups.

Group-I: Animals received saline and served as normal control.

Group-II: Animal received saline+Alloxan (120 mg/kg b.w.) and served as diabetic control.

Group-III: Animals received a dose of 5 mg/kg b.w. of glibenclamide (p.o)+Alloxan (120 mg/kg b.w.).

Group-IV: Animals received a dose of 400 mg/kg b.w. p.o. of EEVP+Alloxan (120 mg/kg b.w.).

Group-V: Animals received a dose of 400 mg/kg b.w., p.o. of AEVP+Alloxan (120 mg/kg b.w.).

The study was conducted for 21 d. On d 0, before the administration of the extracts, the fasting blood glucose levels were recorded. Following this, the extract along with the standard drug (Glibenclamide) were administered daily for a continuous of 21 d.

The blood glucose levels were monitored on 0, 7, 14 and 21 d of treatment period. Blood was collected from the rat tail. Blood glucose levels were measured by using the glucometer. The b.w. of the all animals in each group was noted on the 0, 7, 14 and 21 d of the experiment period. The differences in weight were calculated. At the end of the experiment, blood was collected by cardiac puncture from each rat under mild ether anaesthesia. The blood samples were used for the estimation of haemoglobin levels and remaining was allowed to clot for 30 min at room temperature and they were centrifuged at 3000 rpm for 10 min. The serum was used for the estimation of serum albumin levels[23,24], serum urea[25,26], serum total proteins[27,28], serum HDL-C[29,30], haemoglobin levels[31]. The liver was isolated and washed with saline. The weights were determined by using an electronic balance. The liver weights were expressed with respect to its b.w. i.e. g/100 g.

Ultrastructural studies[32]:

The pancreas of each animal was isolated and was cut into small pieces and preserved and fixed in 10 % formalin for 2 d. Following this, the pancreas pieces were washed in running water for 12 h followed by dehydration with isopropyl alcohol of increasing strength (70 %, 80 % and 90 %) for 12 h each. Then the final dehydration is done using absolute alcohol with about three changes for 12 h each. The clearing was done by using chloroform with two changes for 15 to 20 min each. After clearing the pancreas pieces were subjected to paraffin infiltration in automatic tissue processing unit. The pancreas pieces were washed with running water to remove formalin completely. To remove the water, alcohol of increasing strength were used since it is a dehydrating agent. Further alcohol was removed by using chloroform and chloroform was removed by paraffin infiltration.

Statistical analysis:

The values were expressed as mean±Standard Error of the Mean (SEM). The data was analysed by using one way Analysis of Variance (ANOVA) followed by Dunnett’s test using Graph pad prism software. Statistical significance was set at p≤0.05.

Results and Discussion

The phytochemical analysis revealed the presence of alkaloids, carbohydrates, steroids, flavones and flavonoids in ether extract, whereas aqueous extract exhibited presence of carbohydrates, alkaloids, phenols, flavones and flavonoids, tannins (Table 1).

































S. No Test Ethanol extract Aqueous extract
1 Carbohydrates    
  Molisch’s test + +
  Fehling’s test + +
2 Proteins and amino acids    
  Ninhydrin test _ _
  Biuret test _ _
3 Alkaloids    
  Mayer’s test + +
  Wagner’s test + +
4 Glycosides    
  Borntrager’s test _ _
  Legal’s test _ _
5 Steroids    
  Lieberman-Burchard test + _
  Salkowski’s test + _
6 Triterpenoids    
  Tin+thionyl chloride + _
7 Phenolics and tannins    
  Ferric chloride test _ +
  Gelatine test _ +
  Lead acetate test + +
  Alkaline reagent test + +
  Dilute Nitric acid (HNO3) test + +
8 Saponins    
  Foam test + +
  Haemolysis test + +
9 Flavones and flavonoids    
  Caddy’s test + +
  Shinoda test + +

Table 1: Preliminary Phytochemical Screening of Extracts.

The effect of AEVP and EEVP on OGTT was tabulated in the Table 2. AEVP of 400 mg/kg, p.o. did not show significant reduction in blood glucose levels at 0, 30, 90 min and it shows the significant effect at 150 min (p<0.01), whereas, EEVP of 400 mg/kg show a significant decrease in blood glucose levels, when administered 30 min before glucose loading. It showed a significant activity at the time intervals of 90 min and 150 min (p<0.01). Significant reduction was more at 150 min when compared with the 90 min. Glibenclamide showed its potent antidiabetic activity in normal rats, it bring backs the elevated blood glucose levels to normal levels compared to normal control group at 90 min (p<0.001).









Groups Treatment Blood glucose levels (mg/dl) and time in min
0th min 30th min 90th min 150th min
Group-I Saline 87.33±2.81 132.70±1.11 111.50±1.40 96.67±3.13
Group-II Glibenclamide (5 mg/kg) 81.83±1.79 ns 102.00±2.88*** 84.83±2.38*** 60.00±1.71***
Group-III AEVP (400 mg/kg) 93.33±2.459 ns 109.50±2.952 ns 87.33±2.996 ns 74.33±4.161**
Group-IV EEVP (400 mg/kg) 93.00±4.844 ns 115.8±4.347* 102.7±4.072*** 77.67±3.981***

Table 2: Effect of V. Pedata Plant Extract on Blood Glucose Levels on Ogtt in Normal Rats.

The study of 21 d was done in alloxan induced diabetic rats with plant extracts of V. pedata and the results of blood glucose levels are tabulated in the Table 3. On d 0, there was no much variation in the blood glucose levels within the group. Immediately after the administration of alloxan, the diabetes control rates (Group II) highlighted a substantial rise in the blood glucose level from (320.6±17.39 mg/dl) at d 0 to (371.5±23.43 mg/dl) and (395.23±23.85 mg/dl) on d 7 and d 21 respectively. In case of the diabetic rats treated with AEVP (400 mg/kg) (Group V), a significant decrease of the blood glucose levels were observed from 331.32±20.04 to 106.89±9.28 (p<0.001) from d 0 to d 21. On the other hand, the blood glucose levels in the Group IV animals receiving EEVP of 400 mg/ kg, gradually decreased from 316.84±24.47 on d 0 to 178.78±13.58 (p<0.001) on d 21. Being the standard drug glibenclamide showed its potency and reduced the blood glucose levels of diabetic rats to the level significantly (332.5 3±20.38 to 108.23±6.83) at d 21.










Groups Treatment Blood glucose levels (mg/dl)
0 d 7 d 14 d 21 d
Group-I Saline 80.10±5.08 83.15±5.27*** 79.36±3.88** 83.37±3.67***
Group-II Saline+alloxan (120 mg/kg) 320.6±17.39 371.5±23.43 372.6±22.49 395.23±23.85
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 332.5 3±20.38 257.73±11.62*** 181.34±20.20*** 108.23±6.83***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 316.84±24.47 292.56±11.40** 249.77±13.04*** 178.78±13.58***
Group-V AEVP (400 mg/kg)+alloxan (120 mg/kg) 331.32±20.04 282..83±10.89*** 176.86±15.25*** 106.89±9.28***

Table 3: Effect of V. Pedata Plant Extracts on Blood Glucose Levels in Alloxan Induced Diabetic Rats.

There was a dynamic modification seen in the b.w. of animals after the treatment with the plant extract. The decreased b.w. of the animals was seen to regain when compared with the diabetic control animals after treatment for 21 d. The b.w of normal control group was significantly increased as compared to initial b.w. The changes in the b.w. of animals during 0, 7, 14 and 21 d were tabulated in the Table 4. Rats treated with alloxan showed a decrease in the liver weight of untreated diabetic rats, whereas in treated rats there was a significant restoration of wet liver weight which was near to the normal levels. The values of the wet liver weight were tabulated in the Table 4.










Groups Treatment Body weight (g) Liver Weight
0 d 7 d 14 d 21 d Wet liver weight Weight/100 g body weight
Group-I Saline 180.3±5.11 185.4±4.49*** 188.3±4.89*** 191.3±4.58*** 5.60±0.27*** 2.98±0.14**
Group-II Saline+alloxan (120 mg/kg) 171.5±4.74 158.24±1.93 153.72±3.25 151.02±2.490 3.89±0.14 2.07±0.21
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 185.3±4.78 182.5±2.59*** 187.5±2.67*** 191.8±2.42*** 5.38±0.16*** 2.98±0.09**
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 189.5±5.06 177.7±3.67* 180.7±3.67*** 184.7±3.99*** 4.99±0.14* 2.69±0.13 ns
Group-V AEVP (400 mg/kg)+alloxan (120 mg/kg) 183.4±4.99 181.5±3.66** 185.6±3.53*** 188.9±3.99*** 5.29±0.27** 2.79±0.16*

Table 4: Effect of V. Pedata Plant Extracts on Body Weight and Wet Liver Weight.

The alloxan diabetic animals showed a significant increase in the serum urea, Triglycerides (TGs), Total Cholesterol (TC), Low-Density Lipoprotein Cholesterol (LDL-C) and Very Low-Density Lipoprotein Cholesterol (VLDL-C) levels whereas a suppression in serum albumin, serum protein, hemoglobin and HDL-C levels. However after the treatment with the extracts the serum albumin, serum protein, hemoglobin and HDL-C levels were increased and serum urea, TGs, TC, LDL-C and VLDL-C levels were decreased and almost similar to the normal group, which has been depicted from Table 5-Table 9. The histopathological studies of the tissues from each group have been illustrated in fig. 1.









Groups Treatment Serum albumin levels (g/dl)
Group-I Saline 3.56±0.09***
Group-II Saline+alloxan (120 mg/kg) 1.64±0.11
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 3.16 ± 0.11***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 2.46 ± 0.10**
Group- V AEVP (400 mg/kg)+alloxan (120 mg/kg) 3.07 ± 0.22***

Table 5: Effect of V. Pedata Plant Extracts on Serum Albumin Levels in Alloxan Induced Diabetic Rats.Table 5: Effect of V. Pedata Plant Extracts on Serum Albumin Levels in Alloxan Induced Diabetic Rats.









Groups Treatment Serum urea levels (mg/dl)
Group-I Saline 25.16±0.76***
Group-II Saline+alloxan (120 mg/kg) 42.17±1.22
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 28.47±0.90***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 37.25±0.66*
Group- V AEVP (400 mg/kg)+alloxan (120 mg/kg) 33.98±1.96***

Table 6: Effect of V. Pedata Plant Extracts on Serum Urea Levels in Alloxan Induced Diabetic Rats.









Groups Treatment Serum total protein levels (mg/dl)
Group-I Saline 8.65±0.31***
Group-II Saline+alloxan (120 mg/kg) 5.37±0.32
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 8.28±0.25***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 6.99±0.21**
Group- V AEVP (400 mg/kg)+alloxan (120 mg/kg) 7.05±0.25**

Table 7: Effect of V. Pedata Plant Extract on Serum Total Protein Levels in Alloxan Induced Diabetic Rats.









Groups Treatment Haemoglobin (mg/dl)
Group-I Saline 12.07±0.55***
Group-II Saline+alloxan (120 mg/kg) 8.98±0.49
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 12.47±0.64***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 11.09±0.44*
Group- V AEVP (400 mg/kg)+alloxan (120 mg/kg) 12.34±0.42***

Table 8: Effect of V. Pedata Plant Extracts on Haemoglobin Levels in Alloxan Induced Diabetic Rats.










Groups Treatment Serum lipid profile (mg/dl)
TC TG HDL-C LDL-C VLDL-C
Group-I Saline 62.94±1.08*** 74.25±4.69*** 23.46±0.42*** 24.66±0.90*** 14.86±0.92***
Group-II Saline+alloxan (120 mg/kg) 120.5±3.15 139.86±10.03 15.78±0.72 76.65±3.28 27.96±2.00
Group-III Glibenclamide (5 mg/kg)+alloxan (120 mg/kg) 74.86±0.81*** 75.28±4.91*** 22.23±0.82*** 37.53±1.31*** 15.05±0.98***
Group-IV EEVP (400 mg/kg)+alloxan (120 mg/kg) 96.76±2.48*** 87.47±6.51*** 18.16±0.27* 61.54±2.23*** 17.19±1.42***
Group- V AEVP (400 mg/kg)+alloxan (120 mg/kg) 81.24±2.71*** 85.25±2.04*** 19.55±0.47*** 44.56±3.00*** 17.03±0.41***

Table 9: Effect of V. Pedata Plant Extracts on Serum Lipid Profile of Alloxan Induced Diabetic Rats.

histopathological

Fig. 1: Liver histopathological study after 21 d of treatment, (A) Saline; (B) Saline+alloxan; (C) Glibenclamide+alloxan; (D) EEVP+alloxan and (E) AEVP+alloxan.

The rising trends of diabetes mellitus associated with its complications have manifested it to an epidemic level especially in the developing countries. Bringing in modifications in the lifestyle pattern such as weight loss, cutting out sedentary regime and adopting physical exercise with staple diet are considered as the first line treatment in controlling the diabetes mellitus. However, combatting the complications associated with it, does require support of medications[2]. Apart from the standard treatment with synthetic drugs, the medical team around the world are focusing on the therapeutic impact of phytochemicals owing to its improved patient acceptability and reduced systemic toxicity[33]. Right from the birth of humankind, medicinal plants have always been a key resource to alleviate several diseases and that tradition is religiously followed till present era. In fact, the modern pharmacopoeia also incorporates 25 % of plant-derived drugs[34]. The demand of plant extracts are increasing in the modern society for the management of diabetes mellitus and its complications[4].

In the present study, the phytochemical studies revealed the presence of alkaloids, carbohydrates, steroids, flavones and flavonoids in ether extract and the presence of carbohydrates, alkaloids, flavones and flavonoids, phenols and tannins in the aqueous extract. Several studies have highlighted the antihyperglycemic properties of some of these phytochemicals such as flavones, alkaloids, tannins etc. For example, the antihyperglycemic properties of flavones and flavonoids are accredited to its capability to modulate the cell signaling process and also showcasing the anti-oxidant potency. In another study, Sharma et al.[35] have demonstrated the possible antidiabetic mechanism of alkaloids through the significant improvement in the glucokinase, Glucose Transporter type 4 (GLUT4) and Peroxisome Proliferator-Activated Receptor gamma (PPAR-γ) activities. The study also reported the decrease in TC and TGs.

One of the well-established facts in controlling the post-prandial hyperglycemia is to arrest the absorption of glucose which is mainly achieved by inhibiting α-glucosidase enzyme. Kunyanga et al. in his work reported that tannins, which are polyphenolic molecules, exhibit the antidiabetic properties possibly by its inhibitory effect of α-amylase and α-glucosidase enzymes[36,37]. Although we can find the presence of alkaloids and flavonoids in EEVP, we hypothesized AEVP to be a better candidate having anti-diabetic property since it contains alkaloids, flavonoids, as well as phenols and tannins. This fact was evidenced from the results of OGTT in normal rats and experimentally induced in alloxan induced diabetic rats.

The in vivo study was initiated with an OGTT, mainly to evaluate the glucose homeostasis in normal and identify the pre-diabetic conditions if any. The animals having normal glucose homeostasis were selected for further studies. Alloxan, a pyrimidine derivative was used for diabetes induction of insulin by partially damaging the beta (β)-cells in pancreas, thereby compromising the production of sufficient insulin which further increases the blood glucose levels[13]. A continuous treatment of the diabetic rats with our plant extracts EEVP (400 mg/kg), AEVP (400 mg/kg) were able to markedly reduce the blood glucose level as compared with that of the untreated rats. The probable reason for the hypoglycemic property of V. pedata, may be linked to the stimulation and restoration of the surviving β-cells to release insulin. Additionally, there was a decrease in the b.w. of the animals due to the induction of alloxan was mainly due to the wasting of the tissue protein[38].

Diabetic rats which received the EEVP and AEVP showcased an improved result in comparison to the normal diabetic control. The probable reasons behind this could be the protective effect of plants extracts in controlling the muscle wasting, thus reversing the process of gluconeogenesis. The study highlighted a marked decrease in the liver weight of diabetic control animals, probably as a result of insufficient insulin release that prompted a diminution in the storage of glucose as glycogen in liver. Nevertheless, when AEVP 400 mg/kg was administered for 21 d, it improved the liver weight more efficiently than EEVP 400 mg/kg. The presence of albumin in the urine is a key indicator of diabetic induced nephropathy[39]. In the present study, a marked reduction in albumin and total protein was observed in the diabetic rats. Consequently, the treatment of the diabetic rats with AEVP (400 mg/ kg) and glibenclamide showed a significant increase in the levels of albumin and protein levels compared to the normal untreated diabetic rats. Urea, being a metabolic product of protein is mostly excreted through the kidneys. The increased levels of serum urea levels are also an indicative to diabetic induced kidney damage[40]. An increased serum urea levels was found in the diabetic rats when compared with the respective control group rats. While after the treatment with extracts of V. pedata, the levels were significantly diminished. Hyperlipidaemia is triggered as a result of diabetes, mainly due to the increased action of lipase in breaking down of TGs into fatty acids which are then easily available in the circulation. Following to this the excess fatty acids are converted into phospholipids and cholesterol in the liver, which is then released as lipoproteins in the blood circulation[41]. Diabetic rats treated with the AEVP (400 mg/kg) and glibenclamide has shown a significant decrease in the levels of TG, TC, LDL-C and VLDL-C, where as it increases the levels of HDL-C when compared to the normal diabetic control rats. In EEVP treated rats HDL-C levels is less significant. The histological evidence showing the authenticated injury caused by alloxan and the protection offered by AEVP (400 mg/kg) and EEVP (400 mg/kg) and glibenclamide in pancreatic cells are shown. The haemoglobin levels of the diabetic group of rats were found to be reduced significantly as against the normal haemoglobin levels of the normal group of rats; which is possibly due to the fact that hyperglycemia promoted hemoglobin aggregation and thus may subsequently decrease the level of hemoglobin[42].

Histopathological examination demonstrated a cell necrosis with inflammatory collections in the central zone in alloxan induced diabetic rats. Results proved that V. pedata has the capacity to increase islet cell mass and restore the hepatic tissue architecture. Maximum homeostasis was observed in AEVP treated group which is similar to other reports.

The present study indicated that administration of AEVP at doses of 400 mg/kg and EEVP at a dose of 400 mg/kg, produced significant antihyperglycemic activity in alloxan induced diabetic rats. The acute toxicity study indicated that the extracts are devoid of major toxic effects. Besides this the drug treated to alloxan induced diabetic rats showed a significant reduction in blood glucose levels and the other serum biomarker levels and also increases the hemoglobin levels. The reports of histopathology study concluded that there is an increased mass of β-cells in the pancreatic islets. The result showed in AEVP of 400 mg/ kg, which is more similar to glibenclamide treated group was used as reference standard. In overall we observed significant activity may be due to presence of active constituents present in leaves extract of V. pedata. These observations concluded that the plant extracts of the plant V. pedata possess antidiabetic as well as hypolipidemic property. Further, the work could be extended to evaluate the effectiveness of the marker compounds for the treatment of diabetes at its cellular level to elucidate its exact mechanism for the traditional claim.

Acknowledgements:

The authors are dedicated to Chancellor, Adamas University and Principal of East Point College of Pharmacy for providing the support and necessary facilities for execution of this research work.

Conflict of interests:

The authors declare that they have no conflict of interest in the publication.

References



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Delineation of a Novel Non-Steroidal Anti-Inflammatory Drugs Derivative Using Molecular Docking and Pharmacological Assessment

June 24, 2022 Published by


K. Shah* and S. Mujwar

Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh 281406, India

*Corresponding Author:

K. Shah

Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh 281406, India

E-mail: [email protected]







Date of Received 13 November 2020
Date of Revision 18 September 2021
Date of Acceptance 20 May 2022
Indian J Pharm Sci 2022;84(3):642-653  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.959

Abstract

Cyclooxygenase inhibitors are widely used in prescription. They are the choice of drugs worldwide. The drug naproxen, which is a propionic acid nonselective cyclooxygenase inhibitor, is still preferred in arthritis, osteoarthritis and gout. The drawback associated with this drug is that it causes nausea, vomiting or gastrointestinal upsets. The cause of gastrointestinal upset may be due to perforation of gastric mucosa or ulceration. To overcome this problem, this is associated with the free carboxylic group present on naproxen. Here the parent drug is modified using glycolic acid precursors with phytophenols. The phytophenols are known for their antioxidant properties and they also reduce ulceration. The glycolic acid spacer provides a single bond rotation that promotes excellent binding with the receptor. The different derivatives of naproxen were designed and screened by computational technique based on virtual screening against human cyclooxygenase-2 enzyme through AutoDock. The identified derivatives were curtained through Lipinski’s rule of pharmacokinetics. The effective and innocuous molecule was identified. The identified derivative was synthesized, purified, characterized and pharmacological studies were done. The result obtained clearly indicating that there is a depiction in molecular docking and pharmacological studies.

Keywords

Naproxen, cyclooxygenase, gastrointestinal, glycolic acid spacer, ligands, molecular docking, inflammation, ulcer

Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are one of the common drugs in prescription so widely used due to its analgesic and anti-inflammatory property and due to the extensive utility of NSAIDs, preferred for treatment purposes. The parent drug selected for the experiment is Naproxen (NXN). It is a non-selective Cyclooxygenase (COX) inhibitor bearing propionic acid in structure[1,2]. It is primarily given in the treatment of pain of arthritis or gout[3,4]. The drawback associated with the parent drug is the free carboxylic group which causes peripheral erosion of mucosa of the stomach which results in gastric ulcer. The non-selective nature of NXN indicates that it not only inhibits COX-2 but also COX-1. COX-1 is responsible for damaging the desired mucus lining of the stomach that results in erosion or Gastrointestinal (GI) bleeding[5,6]. Here the parent drug is modified using Glycolic acid (-OCH2COO-) precursors with phytophenols. The phytophenols are known for their antioxidant properties[7]. They act by masking through reactive oxygen species which are responsible for ulceration[8,9]. The glycolic acid spacer provides single bond rotation that promotes excellent binding with receptor. The antioxidants agents selected for the study were syringaldehyde, Menthol (MNT), sesamol, umbelliferone, thymol, carvacrol, eugenol and vanillin. The docking studies were performed with designed derivatives of NXN coupled with antioxidants through spacer against COX-2 receptor. This computational study emancipates the researchers to save resources, time and money. This study gives safe and potent derivative. The screened derivative was synthesized in the chemistry laboratory and its pharmacological studies were established.

Materials and Methods

Materials:

The three-dimensional structural model of the COX-2 enzyme complexed with the ligand mefenamic acid was downloaded from the protein data bank database and its molecular docking simulation based on in silico screening was performed by using AutoDock 4.2 software[10]. The melting point was determined using the open capillary method with the help of melting point apparatus. The Ultraviolet (UV)/ Visible spectrophotometer available in the department (Shimadzu UV-1800, quartz cells) was utilized for the experiment. The method was established for the synthesized derivative using methyl alcohol at 260 nm absorption maxima, which had a linearity range in between 5-30 μg/ml. Fourier Transform Infrared (FTIR) spectrophotometer (using DRS-8000A) of Shimadzu IRAffinity-1 made was used to record infrared spectrum. The Proton Nuclear Magnetic Resonance (1H NMR) spectrum acquired by using Bruker Avance II 400 MHz using Deuterated Chloroform (CDCl3) as the solvent. The mass spectrum of Jeol SX 102/DA-600 mass spectrometer made, using Electron Ionization (EI) technique was used to record the mass spectrum. The purity of the synthesized drug was checked by Thin Layer Chromatography (TLC) using silica gel G. Iodine vapors were used for visualization. The mobile phase used was methanol:toluene (3:7). The parent drug and MNT were obtained from Yarrow Chem Product, Mumbai 400 086, India. Analytical/spectroscopic grade chemicals were used in the whole experiment.

In silico molecular docking simulation:

The mefenamic acid was separated from the threedimensional structural complex of the human COX-2 enzyme by using Chimera software[11]. The preparation of receptor and separated ligand mefenamic acid for molecular docking was performed by using AutoDock software[10]. The grid points were enumerated by considering the ligand and all the residues interacting with it to make certain that the ligand’s extended conformations fit well in the grid box. The grid parameter file consisting of these grid points was utilized by AutoGrid utility of the AutoDock suite for generating various map files for receptor as well as ligand, which were further utilized by AutoDock for performing docking simulations[12,13].

Docking parameter file consisting of various parameters utilized for performing molecular docking of the COX-2 enzyme was prepared by AutoDock. The AutoDock software utilized the Lamarckian Genetic Algorithm (LGA) as it is the primary conformational search algorithm for performing the docking studies. The ligand’s probable binding pattern was obtained based on their position and orientations identified after the molecular docking simulations. The parameters included in the current in silico study were validated by performing docking of the human COX-2 receptor against the crystallized ligand mefenamic acid[14,15]. The docking of the COX-2 receptor was validated by considering the binding energy, chemical interaction and its overlay of docked conformation of the ligand[16,17]. The parameters used in the docking studies were validated if the binding energy of the bound ligand should be in the predefined empirical range of -5 to -15 kcal/mol[18,19], having similar binding interactions as that were present in the downloaded enzyme complex and perfect overlay of the docked conformation of the bound ligand mefenamic acid regarding its bioactive conformation[14,18].

The comparative docking study of the existing approved COX-2 inhibitors was performed to pick the parent molecule based on the best binding score as well as potential binding interactions with the residues present in the active cavity of the macromolecular target. Ibuprofen, fluprofen, fenoprofen, ketoprofen, NXN and suprofen were used in the current comparative analysis for identification of the molecule having most potent binding interaction against the COX-2 enzyme.

A ligand library consisting of 8 esteric derivatives of NXN nuclei were prepared by complexing it with some of the phytophenols like carvacrol, guaiacol, eugenol, MNT, sesamol, thymol, umbelliferone and vanillin with the intent to increase the bioavailability of the complex derivatives at the site of action[13-15]. The validated docking parameters were further utilized to perform the molecular docking simulation based on virtual screening of the designed ligands against the target COX-2 enzyme to identify its potential inhibitors. The shortlisted leads were supposed to have a high affinity for the human COX-2 and possessing potential anti-inflammatory activity. The potential leads were shortlisted by considering the minimum binding energy in the predefined empirical range and were further evaluated for their pharmacokinetics and toxic effects by using Data Warrior[20] software[12,21,22]. This tool predicted the drug-likeness and score for the leads by considering their physicochemical properties[21,22].

Synthesis:

The first step involved the reaction of a Chloroacetyl Chloride (CAC) and MNT at lower temperature. This leads to synthesize ester. The obtained ester was further reacted with NXN to get the desired derivative. The MNT (0.1 mol) with triethylamine (0.1 mol) taken in dichloromethane (50 ml) at 0°. The CAC (0.1 mol) solution made in chloroform (25 ml) was added dropwise with constant stirring for 1 h, further the reaction mixture was stirred for about 4 h. The product obtained was washed with hydrochloric acid solution (0.1 N, 3×20 ml) followed by sodium hydroxide (0.1 N, 3×20 ml) washing. The product obtained was dried and solvent was removed under pressure to get the Antioxidant Derivative (AOD). The derivative was purified by recrystallization with hot ethanol. The AOD (0.01 mol) and parent drug NXN in equimolar amount were taken in round bottom flask using solvent dichloromethane (50 ml). The reaction mixture was stirred for 24 h at Room Temperature (RT). The organic layer obtained was washed with hydrochloric acid and sodium hydroxide solutions of 0.1 N. Finally, the solvent was removed under pressure and the final product (NMXP) was recrystallized with hot ethanol (fig. 1).

Synthetic

Fig. 1: Synthetic scheme of NMXP

Characterization of the synthesized drug:

Solubility and partition coefficient: The solubility of the synthesized derivative was checked by using various solvents. The partition coefficient of synthesized drug was determined by using n-octanol/phosphate buffer (pH 7.4). The NMXP (1 g) was weighed and transferred to 50 ml separating funnel having equal volumes of phosphate buffer and n-octanol. The separating funnel was shaken for 2 h at RT and kept undisturbed for 1 h. The aqueous layer (10 ml) was collected and underwent extraction with dichloromethane thrice. The dichloromethane layer had undergone quantitative analysis by the UV analysis.

Kinetics study: The United States Pharmacopeia (USP) apparatus II i.e. paddle type was used for the study of kinetics. The buffer solutions i.e. phosphate buffer having pH 7.4 and hydrochloric acid buffer having pH 2 were utilized during the study at 37°. This study helped us to govern the rate of chemical hydrolysis. The synthesized derivative NMXP (1 g) was taken in 100 ml volumetric flask and made to dissolve in methanol. The NMXP solution in methanol was kept at RT for 10 min. Further NMXP solution was transferred to vessel of the dissolution apparatus comprising of 900 ml of 0.1 M hydrochloric acid. The dissolution apparatus containing drug solution was stirred at 100 rpm and a solution of 10 ml was taken out at a gap of 30 min for upto 3 h. The solution withdrawn should be replaced by fresh buffer solution instantly. The solution withdrawn should be extracted with chloroform (3×5 ml). The extracted layer was dried on desiccated sodium sulphate further chloroform was removed under pressure. The obtained residue was taken in methanol and was dried further for estimation. Similarly, kinetics studies were done in phosphate buffer (pH 7.4).

Pharmacology:

The pharmacological experiments were performed as per the recommendation and approval of the Institutional Animal Ethical Committee (IAEC) of the Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, India (1260/PO/ Re/S/09/CPCSEA). The experiments were approved by IAEC on February 23, 2019 as per the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA) guidelines. Animals (Rats and Mice) were issued from the departmental animal house. The animals were maintained at standard conditions in animal house, where the temperature was 25°±2°, fed with standard animal feed and water ad libitum, kept in 12 h dark and light cycle along with relative humidity of 45 %-55 %. The pharmacological studies including analgesic, anti-inflammatory and ulcerogenic activity of NXN derivative (NMXP) and standard drug (NXN) were done. They were evaluated for pharmacological activities based on various methods. The details are as follows:

Anti-inflammatory activity: The carrageenan induced paw edema method was utilized to judge the antiinflammatory response[23]. The experimental animals used were albino rats of wistar strains, weighing 150-200 g. The animals used for experiment were fasted overnight prior to test. The animals were divided into three groups each group comprised of six animals. The various groups were group I vehicle control, group II was of standard drug (NXN) at dose of 53 mg/kg body weight and group III was for synthesized derivative (NMXP) at the dose of 98 mg/kg body weight, 230 mM, orally (p.o.). The vehicle control animals were fed with 0.5 % Carboxymethylcellulose (CMC) solution.

Analgesic activity: The analgesic activity was determined by abdominal writhing assay method[24]. The animals used during this study were Swiss albino mice, weighing 20-25 g of either sex. Three groups were made and each comprised of six animals. The vehicle used to prepare the sample was CMC (0.5 %). The suspensions of standard drug and synthesized derivative were made. These suspensions were fed orally prior to acetic acid solution (freshly prepared, 0.6 %, 10 ml/kg). The acetic acid solution was given intraperitoneally. The writhing was counted in terms of number of constrictions of the abdomen or turning of trunk or extension of hind limbs. The readings were noted for 20 min as writhes (for each animal). The readings obtained for synthesized derivative was compared with group I (vehicle control) and group II (standard drug) animals. The degree of analgesia was expressed as percent (%) inhibition as follows:

% inhibition=(1–Wt/WC)×100

Where, Wt=Writhing in derivative/standard treated animal and WC=Writhing in group I (Vehicle) control.

Ulcerogenic study: The ulcerogenic activity was performed to confirm gastro sparing activity of synthesized derivative. The animals utilized in this study were albino wistar rats. The animals used for activity were fasted for 24 h before activity[25]. The animals were divided into three groups each group comprised of six animals. The derivative and standard drug were administered at doses of 10 times of antiinflammatory activity. These animals were sacrificed 12 h after the treatment. The ulcer protective activity was observed by taking out the stomach from the rat. Then the stomach was unbolted along the larger curvature, rinsed with sodium chloride (0.9 % w/v) solution and the reading was taken. The observations made were scored as: 0=No observable damage; 1=Superficial ulcers; 2=Deep ulcers and 3=Perforation.

Statistical analysis:

Statistical analysis was carried out for in vivo studies data. The ulcer index data was subjected to student t-test (unpaired), Analysis of Variance (ANOVA) test, followed by Dunnett’s test for determining the levels of significance in antioxidant studies, p values<0.05 were considered statistically significant.

Results and Discussion

In silico molecular docking simulation was explained here. The one out of two identical chains of 551 amino acids present in the macromolecular complex was procured by deleting another one by using Chimera software. The macromolecule was prepared for docking by removing unnecessary water molecules, the addition of polar hydrogen, followed by addition and equal distribution of Gasteiger charge. Maximum possible flexibility is provided to the ligand in the current study by keeping all the available three bonds rotatable. The crystallized structure of human COX-2 enzyme complexed with mefenamic was shown in fig. 2[18].

mefenamic

Fig. 2: Structure model of the crystallized human COX-2 enzyme (Green) complexed with mefenamic acid (Red)

The macromolecular residues Tyr385 and Ser530 were involved in the active interactions with mefenamic acid. The grid coordinates utilized for making threedimensional imaginary grid-box are shown in Table 1 and its docking results were tabulated in Table 2.





Proteins x-D y-D z-D Spacing (A) x center y center z center
5IKR 46 44 46 0.375 38.042 2.131 61.280

Table 1: The Grid Coordinates of the Grid-Box











S. No. Ligand Structure Binding energy (kcal/mol) Interacting residues
1 Mefenamic acid -7.67 Val349, Ala527, Val116, Leu537, Ser530, Tyr385, Leu352
2 Suprofen -6.91 Met522, Trp387, Leu352, Val349, Leu359, Tyr355, Arg120, Val523, Ala527, Ser530
3 Ibuprofen -6.87 Leu352, Val349, Ala527, Val116, Ala527, Leu359, Tyr355, Arg120
4 Fenoprofen -6.01 Phe205, Val344, Leu534, Leu352, Val349, Ala527, Leu531, Tyr385
5 Fluprofen -7.16 Leu352, Val349, Ala527, Tyr325, Arg120, Leu359, Val116, Leu531
6 Ketoprofen -5.84 Val349, Leu531, Ala527, Tyr355, Met522, Leu352, Val523, Trp387, Ser530
7 NXN -7.55 Leu359, Val523, Tyr355, Arg120, Ala527, Met522, Gly526, Leu352, Val349

Table 2: Comparative Study of Approved COX-2 Inhibitor Molecules

The molecular docking process of mefenamic acid against human COX-2 enzyme was successfully validated as the binding energy was -7.67 kcal/mol lies well within the predefined empirical range. The docked conformation of the ligand was having similar interactions and it was perfectly overlaid with respect to its bioactive conformation. The docked conformation having a perfect overlay with respect to its bioactive conformation was represented in fig. 3. The binding interactions of the docked ligand with reference to its bioactive crystal structure were shown in fig. 4.

superimposed

Fig. 3: The superimposed docked conformation of mefenamic acid with respect to its bioactive conformation

ligand

Fig. 4: Binding mode and chemical interactions of the bound ligand mefenamic acid within the active ligand binding site of COX-2 receptor of human

The comparative analysis of the approved COX-2 inhibitors on the basis of the observed binding energy as well as their binding interactions with the macromolecular target clearly signifies NXN as the most potent COX-2 inhibitor. Thus, NXN was considered as a standard molecule in the current study and was further utilized for developing a ligand library by generating its various ester derivatives as a COX-2 inhibitor. The result of the comparative study of existing COX-2 inhibitors is shown in Table 2.

The prepared ligand library of the ester derivatives of NXN was virtually screened to identify potential COX-2 inhibitors. The structure activity relationship obtained after evaluating the binding pattern of all the lead molecules with the binding residues of the human COX-2 enzyme reveals that all the lead molecules were interacting with the Leu352, Phe518, Val349, Val523 and Ala527. Thus Leu352, Phe518, Val349, Val523 and Ala527 residues of human COX-2 enzyme were found to be key residues playing important role in the binding of the inhibitor molecule[26]. The other residues like Trp387, Tyr385, Met522, Arg120, etc. were also having a crucial role in the binding interaction of the large proportion of lead molecules. The docking results of the virtually screened ligand molecules against COX-2 enzyme were tabulated in Table 3.





















S. No. Name Structure Binding energy Interactions
1 NMXP -8.28 Tyr385, Leu352, Phe518, Trp387, Met522, Val523, Gly526, Ala527, Leu384, Tyr355, Leu359, Leu531, Met113, Ile345, Val116, Val349, Tyr348, Ser530, Phe381
2 NAPE -7.74 Val523, Gln192, Leu352, Met522, Phe518, Leu531, Val116, Val349, Leu359, Tyr355, Arg120, Ser353, Tyr348, Tyr385
3 NAPC -7.67 Val116, Leu359, Met113, Ile345, Leu531, Tyr355, Ser353, Ala527, Val349, Val523, Leu352, Phe518, Gly526, Met522, Trp387, Tyr385, Phe381, Phe205, Tyr348
4 NXN -7.41 Leu359, Val523, Tyr355, Arg120, Ala527, Met522, Gly526, Leu352, Val349
5 NAPU -7.24 Leu531, Val349, Ala527, Leu352, Arg120, Tyr355, Val523, Ala516, Phe518, Ile517, Arg513, Ser353, Gln192, Leu359
6 NAG -7.21 Phe518, Val523, Trp387, Leu352, Met522, Val349, Arg120, Ala527, Leu359, Leu531
7 NAPS -6.56 Tyr348, Phe209, Val344, Phe205, Leu534, Trp387, Tyr385, Val523, Val349, Leu352, Phe518, Leu531, Arg120, Ala527, Ser530, Phe381, Ser353, Tyr355
8 MNT -5.80 Trp387, Leu352, Val349, Ala527, Val523, Met522, Phe518, Gly526, Leu384
9 NAPT -5.75 Val344, Phe205, Tyr348, Leu531Val349, Arg120, Ala527, Ser353, Leu352, Val523, Trp387, Phe381, Leu384, Tyr385, Gly526, Phe518
10 NAPV -5.68 Arg120, Tyr355, Leu531, Leu359, Val116, Val349, Ala527, Leu352, Phe205, Tyr385, Ser353
11 Thymol -5.21 Trp387, Leu352, Leu384, Met522, Phe518, Gly526, Val523, Val349, Ala527
12 Carvacrol -5.10 Ala527, Val349, Leu531, Leu352, Leu384, Tyr385, Val523, Trp387, Met522, Phe518, Gly526, Phe381
13 Umbelliferone -5.10 Trp382, Met522, Leu352, Tyr348, Gly526, Val523, Tyr385, Val349, Phe518
14 Eugenol -4.42 Leu384, Met522, Tyr385, Trp387, Ala527, Leu352, Val349, Phe518, Val523
15 Vanillin -4.37 Leu352, Tyr385, Trp387, Phe381, Gly526, Ala523, Met522, Phe518
16 Sesamol -4.18 Phe357, Tyr585, Tyr547, Gln553, Trp629, His740, Ser552
17 Guaiacol -3.97 Gly526, Leu352, Phe518, Met522, Val523, Ala527

Table 3: Binding Energy of the Virtually Screened Leads and Their Parent Compounds against COX-2 Enzyme

All the designed NXN based ester leads were further evaluated for their pharmacokinetics by considering important physicochemical properties. The properties like calculated Partition coefficient (cLogP), Two-Dimensional Polar Surface Area (2D PSA), molecular weight, Hydrogen Bond Donor (HBD) and Hydrogen Bond Acceptor (HBA) sites etc. by using an online program OSIRIS molecular property explorer[27]. The physico-chemical properties of the shortlisted lead molecules for human COX-2 enzyme were shown in Table 4.





















Compound ID Molecular weight cLogP 2D PSA (Å2) HBA HBD Drug likeness
NXN 230.26 2.99 46.53 4 1 0.361
NMXP 426 5.45 61.83 5 0 -19.47
NAPE 434 5.16 71.06 6 0 1.83
NAPC 420 5.73 61.83 5 0 -0.01
NAPU 432 4.04 88.13 7 0 -0.78
NAG 394 4.13 71.06 6 0 3.5
NAPS 408 4.31 80.29 7 0 1.81
MNT 156 2.41 20.23 1 1 -10.47
NAPT 420 5.73 61.83 5 0 -0.22
NAPV 422 4.06 88.13 7 0 0.38
Thymol 150 2.84 20.23 1 1 -3.02
Carvacrol 150 2.84 20.23 1 1 -2.59
Umbelliferone 162 1.15 46.53 3 1 -4.4
Eugenol 164 2.27 29.46 2 1 -2.78
Vanillin 152 1.18 46.53 3 1 -4.35
Sesamol 138 1.43 38.69 3 1 -2.12
Guaiacol 124 1.24 29.46 2 1 -1.68

Table 4: “Lipinski’s Rule of Five” For the Lead Molecules Targeting COX-2 Enzyme

Later these leads were evaluated for the presence of any major toxic effect, drug-likeness and drug score values. It was observed that three out of five selected lead molecules, i.e. NMXP, N-Acetyl-D-Glucosamine (NAG) and N-Acetyl Phenylalanine derivative (NAPS) were having a good pharmacokinetic profile with the presence of no or very low toxic effects. The Absorption, Distribution, Metabolism and Excretion (ADME), and toxicity results of best lead molecules obtained after virtual screening were shown in Table 5.





















Lead compound Mutagenic Tumorigenic Irritant Reproductive effect
NXN High No High No
NMXP No No No No
NAPE No High High No
NAPC No No High No
NAPU No No No High
NAG No No No No
NAPS No No No No
MNT High High High No
NAPT No No High No
NAPV No No High No
Thymol High No No High
Carvacrol No No High No
Umbelliferone High No No No
Eugenol High High High No
Vanillin High No High High
Sesamol No High No No
Guaiacol No No High High

Table 5: Toxicity Prediction of Proposed Lead Molecules for COX-2 enzyme

The derivative obtained was a resultant of nucleophilic addition/elimination reaction between acyl chlorides (acid chlorides) and alcohols followed by reaction with alkyl halide with carboxylic acid, this result in formation of the double ester separated by methylene bridge. This glycolic acid spacer resulted to orient various conformation of synthesized derivative that might allow it to have the better binding affinity at active binding site. The melting point and percentage yield of NMXP were reported as 122°-124° and 61.4 % respectively. UV (absorption maxima (λmax)) nm: Methyl alcohol-242, 260. Infrared (IR) (Potassium bromide (KBr)), cm-1: 3092.24 Aromatic C-H stretching, 2924.26 Aliphatic C-H stretching, 1716.28 C=O stretching of esters, 1114.48 C-O stretching, 1453.46 CH2– bending. 1HNMR (CDCl3, 400 MHz) Delta (δ): 1.526-1.498 (d, 3H, 3x -CH3), 1.940-1.923 (m, 1H, 4x -CH), 2.160-2.124 (d, 3H, CH3), 2.456-2.398 (m, 2H, 3x -CH2 (cyclic)), 3.458 (s, 3H, -OCH3), 3.982-3.978 (q, H, -CH-CO), 5.248 (s, 2H, -CH2 ), 7.367-7.289 (d, 6H, Ar-H). Mass Spectrometry (MS): Mass to Charge ratio (m/z) 327.320 (M+) (100 % abundance).

The oral absorption and clinical utility was manifested by calculating the value of partition coefficient and solubility data. The solubility studies were done via using various solvents (methanol, ethanol, dichloromethane, chloroform and benzene). It was found that the synthesized derivative was found to show moderate to good solubility in polar to non-polar solvents. The newly derived derivative was insoluble in water and 0.1 N hydrochloric acid, while slightly soluble in 0.1 N sodium hydroxide. This solubility parameter confirmed the hydrophobic nature of drug. The value of partition coefficient was found to be 5.46 that signify it would be a good candidate for oral absorption. This potentiates to use it orally and also designated that it had better absorption via GI tract.

The derivative synthesized was evaluated for its stability at different pH. It was known that the drug should undergo unhydrolyzed at lower pH so that it could be passed to the stomach without any hydrolysis. The synthesized drug should also show sufficient stability in intestine. The result obtained gives a clear indication that the rate of hydrolysis in acidic media was lower (4.246×10-4 s-1 and 54.48 h) than that at intestinal pH (6.436×10-3 s-1 and 39.48 h). This data completely elucidated that the synthesized derivative was stable at acidic pH.

The synthesized derivative was screened for the antiinflammatory, analgesic and ulcerogenic activities. The anti-inflammatory activity was performed by using carrageenan (1 % w/v in 1 % w/v saline solution). The carrageenan was injected in all groups of animals at the sub-plantar region of the right hind paw. The swelling of the paws was measured by using Vernier caliper. Then the readings were taken for 24 h to observe the reduction in swelling. The reduction in swelling is marked by reduction in drug treated animals in comparison with control treated animals. The data represented the synthesized derivative was found to be effective in comparison with the standard drug. The inhibition in inflammation was calculated by ratio i.e. difference of paw diameters in control and drug treated to paw diameters in control. Finally, the result obtained was compared with the standard (Table 6).








S. No Group Difference in paw volume (mean±SD) % Inhibition
3 h 4 h 6 h 24 h 3 h 4 h 6 h 24 h
1 Vehicle (control) 1.46±0.037 1.39±0.051 1.19±0.042 0.98±0.048 – – – –
2 NXN 0.88±0.034* 0.69±0.052* 0.53±0.048* 0.43±0.037* 39.72 50.36 55.46 56.12
3 NMXP 0.98±0.064a 0.84±0.210b 0.61±0.062a 0.48±0.043 34.93 39.57 48.74 51.02

Table 6: Anti-Inflammatory Activity

The abdominal writhing method was utilized to measure the analgesic activity. The data obtained gives the idea that the synthesized derivative had retained the analgesic activity at same dose of standard drug (Table 7). The standard drug selected had ulcerogenic activity, the synthesized derivative was found to be gastric ulcer sparing. The data obtained depicted that the synthesized derivative did not cause ulceration and when it was compared with the standard drug at an equivalent dose (Table 7).







Groups Number of writhing % inhibition in writhing Ulcer index (Ui±SEM)
Control 54.58±2.458 – –
NXN 25.00±2.146 54.19±2.936 11.621±3.156*
NMXP 28.46±3.168 47.85±2.241a 7.468±4.642b

Table 7: Analgesic and Ulcerogenic Activity

Molecular docking simulation-based in silico virtual screening using Autodock was utilized in the current experimental study to design potential COX-2 targeting anti-inflammatory compounds. Three compounds NMXP, NAG and NAPS showed promising in silico results with potent inhibition of the COX-2 enzyme, good pharmacokinetic properties and the absence of any major toxic effects. These molecules could serve as promising lead compounds for further experimental validation as a novel pain-relieving drug.

The index obtained clearly signifying that the free carboxyl group present in parent drug was masked. It may be the cause of ulceration which was overcome here by this chemical modification.

In the present study NMXP drug was designed, synthesized and evaluated as a safer NSAID. The synthesized derivative was found to be chemically stable and bio labile. The synthesized drug showed desirable anti-inflammatory, analgesic activity with prominent reduced ulcerogenicity at equivalent doses. This might be due to upgraded physicochemical properties requisite for improved bioavailability. This may lead to design a safe and effective drug molecule. Based on these observations, it could be concluded that there is an advantage of giving NXN and MNT in the form of a single molecule i.e. NMXP drug.

Acknowledgements:

Authors thank to Central Instrument Facility, Bose Institute, Annex Building (First floor) Centenary campus, Kolkata for NMR and Mass data. Author also thanks the Management, GLA University, Mathura, Uttar Pradesh, India for providing the research facilities and financial assistance to carry out this work at the Institute of Pharmaceutical Research.

Conflict of interests:

The authors declare that they have no conflict of interest.

References



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A Novel Approach for the Management of Coronavirus Disease 2019

June 23, 2022 Published by


R. S. Shivatare*, R. Musale, N. K. Bhutale, S. M. Kewatkar1, H. L. Tare2, Vibhavari Chatur3, A. N. Khatawaka4 and D. S. Suryavanshi5

Department of Pharmacy, Jagdishprasad Jhabarwal Tibervala University, Jhunjhunu, Rajasthan 333001,1Department of Pharmacognosy, Rajarshi Shahu College of Pharmacy, Buldana, Maharashtra 443001,2Department of Pharmacy, Sharadchandra Pawar College of Pharmacy, Otur, Maharashtra 412409, 3Department of Pharmaceutics, Rasiklal M. Dhariwal Institute of Pharmaceutical Education and Research, Pune, Maharashtra 411019, 4Department of Pharmacy, Anuradha College of Pharmacy, Chikhli, Maharashtra 443201,5Department of Pharmaceutics, Dr. D. Y. Patil College of Pharmacy, Pune, Maharashtra 411044, India

*Corresponding Author:

R. S. Shivatare

Department of Pharmacy,

Jagdishprasad Jhabarwal Tibervala University,

Jhunjhunu,

Rajasthan 333001

E-mail: [email protected]







Date of Received 01 June 2020
Date of Revision 20 August 2021
Date of Acceptance 02 May 2022
Indian J Pharm Sci 2022;84(3):519-531  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.946

Abstract

The severe acute respiratory syndrome coronavirus 2, formerly known as 2019 novel coronavirus, the causative pathogen of coronavirus disease 2019 is a major source of disaster in the 21st century. In the second meeting of the Emergency Committee, the World Health Organization declared that coronavirus disease 2019 is a “public-health emergency of international concern” on 30 January, 2020. Coronavirus is transmitted via airborne droplets from human to human or human to animal. Through membrane angiotensin-converting enzyme 2 exopeptidase receptor coronavirus enters in human cell. For the treatment of this sudden and lethal disease during coronavirus disease 2019, there are no specific anti-virus drugs or vaccines. Still, the development of these medicines will take months, even years. Currently there is need of supportive care and non-specific treatment to improve the symptoms of coronavirus disease 2019 infected patient. For this specific indication, rapid performance of herbal medicine or phytochemicals can contribute as an alternative measure. Phytochemicals are a powerful group of chemicals that are derived from plants origin hence causing fewer side effects because of less use of additives, preservatives or excipients. Hence, this review will focus on some phytochemicals which may control and prevent severe acute respiratory syndrome coronavirus 2. Further, the existing healing options, drugs accessible, ongoing trials and current diagnostics to treat severe acute respiratory syndrome coronavirus 2 have been discussed. We suggested phytochemicals extracted from herbal plants are potential novel therapeutic approaches, completely targeting severe acute respiratory syndrome coronavirus 2 and its pathways.

Keywords

Severe acute respiratory syndrome coronavirus 2, phytochemicals, herbal medicine, coronavirus disease 2019

Coronaviruses (CoVs) classified to the subfamily Orthocoronavirinae in the family Coronaviridae and order Nidovirales. The subfamilies Orthocoronavirinae again contain four genera, namely Alphacoronavirus (α-CoV), Betacoronavirus (β-CoV), Gammacoronavirus (γ-CoV) and Deltacoronavirus (δ-CoV). From that, α and β-CoV genera are known to infect mammals, whilst δ and γ-CoVs are identified to infect birds. Coronavirus Disease 2019 (COVID-19) is not the first severe respiratory infection epidemic originated by the corona virus. In the past few decades, CoVs have caused three outbreak infections, namely, COVID-19, Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)[1,2]. This article gives a bird’s eye view about this new virus i.e. COVID-19 and phytochemicals which may be effective in the treatment of COVID-19 as given in fig. 1. In view of the fact that awareness about this new virus is speedily developing, readers are urged to modernize themselves repeatedly.

IJPS-covid19

Fig. 1: Information about COVID-19 and phytochemicals used for the treatment of COVID-19

History

Novel Coronavirus (nCoV)-precipitated pneumonia, which was named by the World Health Organization (WHO) on the February 11, 2020 as COVID-19, has swiftly accelerated in epidemic scale since it first appeared during December 2019, inside Wuhan city, China. The international virus classification commission, on the same day, declared that the nCoV was named as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Right now, the COVID-19 cases have been found in many countries around the world including United States of America (USA), India, Germany, Brazil, France etc.[3].

Genome structure:

COVID-19 is a sphere-shaped or pleomorphic enclosed particles surrounding single-stranded (positive-sense) Ribonucleic Acid (RNA) linked with a nucleo-protein within a capsid comprised of matrix protein. The envelope bears club-shaped glycoprotein protrusions. A few CoVs also enclose a Hemagglutinin-Esterase (HE) protein. A typical CoV have minimum six Open Reading Frames (ORFs) in its genome. Apart from γ-CoV that takes non-structural protein 1 (nsp1), the primary ORFs (ORF1a/b) constitute about two-thirds of the entire genome length, encoding sixteen nsps (nsp1-16). ORF1a and ORF1b fit in a frame shift among which it creates two polypeptides: Protein phosphatase (pp)1a and pp1ab. These polypeptides are progressed via virally encoded 3-Chymotrypsin Like protease (3CLpro) or Main protease (Mpro) and one or more papain-like protease into 16nsps. All the structural and accent proteins are translated from the single guide RNAs (sgRNAs) of CoVs[4]. Four main structural proteins contain Spike (S), Membrane (M), Envelope (E) and Nucleocapsid (N) proteins are encoded by ORFs on the one-third of the genome near the 30-terminus. In addition to these four main structural proteins, different CoVs instruct particular structural and accessory proteins, such as 3a/b protein, HE protein and 4a/b protein. These established proteins are responsible for numerous vital functions in genome safeguarding and virus reproduction. There are 3 or 4 viral proteins in the coronavirus membrane. The most enough structural protein is the membrane (M) glycoprotein; it extents the membrane belayed 3 times, parting a short Amine (NH2)-terminal area outdoor the virus and a long Carboxyl (COOH) terminus (cytoplasm domain) inner the virion. The spike protein (S) as a kind of membrane glycoprotein constitutes the peplomers. In fact, the primary inducer of neutralizing antibodies is S protein. Between the envelopes proteins there exists a molecular interplay that probably determines the formation and composition of the corona viral membrane. M performs a predominant function in the intracellular formation of virus particles except requiring S. In the existence of tunicamycin, CoV develop and generate spikeless, noninfectious virions that incorporate M but devoid of S[5].

Symptoms:

The signs and indications of COVID-19 contamination come out later than an incubation length of about 5.2 d. The time from the onset of COVID-19 symptoms to finish ranged from 6 to 41 d with a center of 14 d. This duration is dependent on the age of the affected person and repute of the patient’s immune system. It was once shorter among sufferers >70 y old in contrast with those under the age of 70. The most frequent signs and symptoms at onset of COVID-19 illness are fever, cough and fatigue, while other signs consist of sputum production, headache, haemoptysis, diarrhoea, dyspnoea and lymphopenia. Clinical facets published with the aid of a chest Computed Tomography (CT) scan introduced as pneumonia; however, there were bizarre facets such as RNAaemia, acute respiratory distress syndrome, acute cardiac injury and occurrence of grand-glass opacities that led to death. In some cases, the couple of peripheral ground-glass opacities were observed in subpleural regions of each lung that in all likelihood precipitated both systemic and localized immune response that led to extended inflammation. Regrettably, treatment of some instances with Interferon (IFN) inhalation showed no scientific effect and rather appeared to irritate the circumstance by using progressing pulmonary opacities[6,7].

Transmission route:

Transmission of the virus is primarily via inhalation of suspended respiratory secretions, i.e., droplets generated when an infected individual coughs, sneezes or speaks, or through direct contact with an infected patient. There is a possibility that viral RNA may also be transmitted through microparticles of saliva, e.g. in exhaled air or when speaking, although this remains to be confirmed. Viral load in saliva peaks at presentation and remains high for at least the 1st w of symptomatic illness, gradually declining the reafter but remaining detectable for 20 d or more.

The virus can also be transmitted via fomites. It remains viable for up to 24 h on cardboard and for up to 72 h on plastic and stainless steel. Infectious droplets and body fluids can also contaminate the human conjunctival epithelium, producing ocular complications that may then progress to respiratory infection. At later stages of infection, viral persistence has been detected in anal swabs, blood and serum, suggesting additional shedding mechanisms and the potential for transmission via the oral-fecal or body fluid routes[4,8].

Incubation period:

The mean incubation period of SARS-CoV-2 is estimated to be 3-7 d (range, 2-14 d), indicating a long transmission period of SARS-CoV-2. It is estimated that SARS-CoV-2 latency is consistent with those of other known human CoVs, including non-SARS human CoVs (mean 3 d, range 2-5 d), SARS-CoV (mean 5 d, range 2-14 d) and MERS-CoV (mean 5.7 d, range 2-14 d). Moreover, it has been reported that asymptomatic COVID-19 patients during their incubation periods can effectively transmit SARS-CoV-2, which is different from SARS-CoV because most SARS-CoV cases are infected by ‘superspreaders’ and SARS-CoV cases cannot infect susceptible persons during the incubation period. Taken together, these data fully support the current period of active monitoring recommended by the WHO of 14 d[9,10].

Diagnosis:

Quick and precise detection of SARS-CoV-2 is important to manage the outbreak of COVID-19. Nucleic acid detection may be a major technique of laboratory designation. Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) may be a molecular biological analysis technology based on nucleic acid sequences. The entire SARS-CoV-2 genome sequences are existing in GenBank. Thus, the nucleic acid of SARS-CoV-2 is often identified by RT-qPCR or through viral gene sequencing of nasopharyngeal and oropharyngeal swabs, sputum, stool or blood samples. However, assortment of these specimen arrange by healthcare workers involve close contact with patients, that poses a possibility of scattering the virus to healthcare workers. Furthermore, gathering of nasopharyngeal or oropharyngeal specimens could cause bleeding, particularly in patients with blood disease. Significantly, To et al. establish that SARS-CoV-2 may be effectively detected within the saliva samples of infected patients, signifying that saliva may be a promising non-invasive specimen form for analysis, monitoring and infection control of COVID-19 patients[11,12].

Further RT-qPCR, Zhang et al. explained a protocol by means of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based Specific High-sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK) method for the finding of SARS-CoV-2. With artificial SARS-CoV-2 virus RNA fragments, the authors established that this technique is able to constantly detect target sequences of SARS-CoV-2 in a range between 20 and 200 attometer (am) (10- 100 copies per microlitre of input). This test will be read out by a dipstick in <1 h, without requiring complicated instrumentation. As compared to RTqPCR, the SHERLOCK technique is more precise and also the detection time is reduced by one-half. Therefore, utilization of the SHERLOCK technique for the finding of SARS-CoV-2 in clinical patient samples is estimated[13-15].

Current treatment:

The person-to-person communication of COVID-19 contamination led to the segregation of patients that were administered a range of treatments. Currently, there are no definite antiviral drugs or vaccine to treat COVID-19 infection for potential treatment of humans. The sole alternative accessible is using broad-spectrum antiviral drugs similar to nucleoside analogues and moreover Human Immunodeficiency Virus (HIV)-protease inhibitors that might attenuate the infection until the precise antiviral becomes existing. The treatment that has been so far used proved that 75 patients were administrated existing antiviral drugs. The track of treatment enclosed double on a daily basis oral administration of 75 mg oseltamivir, 500 mg lopinavir, 500 mg ritonavir and therefore the intravenous administration of 0-25 g ganciclovir for 3-14 d. One more information showed that the broadspectrum antivirals remdesivir and chloroquine are extremely effective within the management of 2019- nCoV infection in vitro. These antiviral compounds are utilized in human patients with a security record. Hence, these therapeutic agents are thought to treat COVID-19 infection. Additionally, there are varieties of alternative compounds that are in development. These contain the clinical applicant Molnupiravir (EIDD-2801) that has exposed high therapeutic potential against seasonal and epidemic contagion virus infections and this shows another potential drug to be considered for the management of COVID-19 infection. Beside those lines, until additional precise therapeutics become existing, it is sensible to judge more broad-spectrum antivirals that offer drug treatment alternative for COVID-19 infection which contain neuraminidase inhibitors, peptide (EK1), lopinavir/ritonavir and RNA synthesis inhibitors. It is still obvious, that additional analysis is desperately required to spot novel chemotherapeutic medicines for treating COVID-19 infections. So as to develop pre and post exposure prevention against COVID-19, there is an urgent requirement to set up an animal model to reproduce the severe disease presently ascertained in humans. Several teams of scientists are presently operating and exhausting to build up a nonhuman primate model to learn COVID-19 infection to determine fast track novel therapeutics and for the testing of possible vaccines in addition to providing a superior understanding of virus-host interactions[16,17].

Importance of phytochemicals:

Medicines originated from herbals include Ayurveda, Siddha and Unani that are effectively accomplished for treating various diseases. These came into continuation 5000 y ago and these systems are seen and scripted in olden literature. The phytochemicals are created by plants to shield themselves against environmental hazards like water changes and microorganisms and to relinquish them their specific color, aroma, flavor and texture. Additionally, recent analysis demonstrated that they also need significant effects on human health nonetheless they never thought of essential nutrients. Research facility studies have exposed that these phytochemicals have the capability to stop certain compounds in drink, food and breathe from changing into carcinogens. It will moreover, decrease the swelling that triggers cancer growth. Besides, the phytochemicals reduce the oxidative damage to cells which will cause a variety of diseases and help in hormonal regulation. Researchers predicted that there are about 4000 phytochemicals that have been recognized so far, but only a little fraction of them have been studied strongly. These phytochemicals are frequently found in an extensive range of plants and are mainly present in consumed foods such as vegetables, fruits, green tea, coffee, grains, beans and so on. Phytochemicals are usually unnoticed in research and development of current drugs because their translational potentials are frequently belittled[18,19]. In spite of the fact that these medicines are ambiguous, there is wide background for their utilization in non- Western medical technology. A single herb may contain numerous phytochemical constituents that work alone or in combination with other compounds to deliver the specified pharmacological impact. The seek for new compounds with antiviral action has regularly been unsuitable due to viral resistance together with viral inactivity and repetitive contamination in immunecompromised patients. Among antiviral therapeutic technique, the bulk of them are non-specific for viruses. The progressions in creating antiviral agents are the major spotlight in medical research. The antiviral impacts of phytochemicals have played a huge part at diverse stages of viral development[20]. Phytochemicals derived pharmacological formulations stamped a major commitment for viral contaminations. Based on the accessibility of reasonable, proficient and fast bioassay systems, the antiviral compounds have been utilized for quick screening from plant extracts and fractions. Rather than synthetic antiviral drugs, phytochemicals convey fundamental raw materials for significant antiviral drugs. Synthetic drugs have been supplemented by phytochemicals, as life-saving drugs in a variety of viral diseases. Shockingly, the usage of this medicine has been passed down to eras by word of mouth and most of them have been misplaced over time, due to the need of appropriate documentation.

Research on these phytochemicals may offer assistance to advance their utilization in clinical settings to avoid or treat different ailments. Since numerous Indian medicinal plants display anti-inflammatory, antiviral and antioxidant properties, it could be favorable to believe them for the treatment of COVID-19. It is obvious that standard clinical trials ought to be carried out to logically demonstrate its adequacy[21,22].

Phytochemicals in the Management of COVID-19

Curcumin:

In recent times a molecular docking study indicated that curcumin have higher binding capability to the receptors and should restrain the entry of COVID-19 virus. Angiotensin Converting Enzyme 2 (ACE2) is the receptor that connects with SARS-CoV-2 spike glycoprotein which supports the membrane fusion and virus infection happens through endocytosis. Hence, spike glycoprotein could be a potential candidate for drug targeting to restrain the entry of virus, that in silico docking studies exposed that curcumin may possibly restrain ACE2 to suppress COVID19 passage to the cell[23].

Wen et al. have examined the impact of curcumin on viral replication by measurement of the amount of spike proteins that show in cultures of Vero E6 cells infected with SARS-CoV. Their result is incontestible that the inhibitory result of curcumin in EC50 values was higher than 10 μM on SARS-CoV replication[24].

Khaerunnisa et al. inspected the part of many phytochemical compounds like curcumin that will have the potential to repress the COVID-19 disease by molecular docking. Curcumin appeared generally with low binding energies and inhibition constants. They recommended that curcumin may have a latent inhibitory consequence on COVID-19 Mpro and might potentially act as a therapeutic agent[25].

There is growing proof on the repressive actions of curcumin on inflammatory cytokines. Curcumin obstructs the vital signals, directing the expression of different pro-inflammatory cytokines together with Nuclear Factor-Kappa B (NF-κB) and Mitogen- Activated Protein Kinase (MAPK) pathways. Curcumin have anti-inflammatory and anti-fibrotic impacts by diminishing the expression of vital chemokines and cytokines included in lung infection such as Interferon gamma (IFN-γ), Monocyte Chemoattractant Protein-1 (MCP-1), Interleukin (IL)-6 and IL-10. Curcumin has an inhibitory impact against the human Respiratory Syncytial Virus (RSV) contamination by avoiding RSV replication, the discharge of Tumour Necrosis Factor alpha (TNF-α) and down regulation of phospho-NF- κB[26].

Resveratrol:

Lin et al. illustrated conceivable antiviral mechanisms for resveratrol. Resveratrol has been reported to stimulate Extracellular Signal-Regulated Kinase 1/2 (ERK1/2) signaling pathway and support cell proliferation and improve Sirtuin 1 (SIR1) signaling, both of which are connected to cellular survival and Deoxyribonucleic Acid (DNA) restore in response to DNA harm. On the other hand, resveratrol may neutralize the MERS-CoV-induced apoptosis by down-regulating Fibroblast Growth Factor-2 (FGF-2) signaling. In addition, MERS-CoV infection might lead to the generation of inflammatory cytokines while resveratrol could decrease the inflammation via interfering with the NF-κB pathway. In their experiment, the levels of cleaved caspase 3 were decreased by resveratrol later than MERS-CoV infection. These modifications may be consequences of direct inhibition of caspase 3 cleavage by reversion of cell survival and the decrease of virus-induced apoptosis by resveratrol or restraint of an upstream incident that is required for caspase 3 cleavage[27].

Resveratrol-treatment curbed the TNF-α generation, showing that the anti-retroviral action of resveratrol may be accomplished by lessening the inflammatory response. The IFN-γ level was prominent in the dose of 10 mg/kg/d resveratrol treated cluster as well as 30 mg/kg/d resveratrol-treated set after RV infection. The proportion of Cluster of Differentiation (CD) 4+/CD8+ in resveratrol-treated sets were the similar as that in mock infected cluster, signifying that resveratrol may maintain the immune function in Rotavirus-infected piglets. It was found that resveratrol might reduce diarrhea stimulated by Rotavirus infection[28].

Zhao et al. examined the antiviral action of resveratrol against Pseudorabies Virus (PRV) and its mechanism of action. The consequences proved that resveratrol potently repressed PRV replication in a dose-dependent manner. The inhibition of virus reproduction in the existence of resveratrol was not credited to straight inactivation or inhibition of viral entrance into the host cells but due to the inhibition of viral reproduction in host cells. Additional studies illustrated that resveratrol may be a strong inhibitor of both NF-κB activation and NF κB-dependent gene expression through its capacity to restrain Inhibitor of NF-κB (IκB) kinase activity, which is the key controller in NF-κB actuation. Therefore, the inhibitory impact of resveratrol on PRV-induced cell passing and gene expression may be due to its capacity to restrain the degradation of IκB kinase[29]. In spite of the fact that there are no information for utilizing resveratrol in peoples infected with SARS-CoV-2, the above results illustrate that this compound might be an adjunctive antiviral agent to believe, particularly based on the information distributed by Linn et al. showing activity against MERS-CoV in vitro.

Gallic acid:

Gallic acid interfered with different intra-cellular inflammatory pathways that actuate ulcerative colitis. The compound hinders the expression of nuclear transcription variables, such as NF-κB and Signal Transducer and Activator of Transcription 3 (STAT3), and down-regulates their inflammatory downstream objectives. It too decreases the expression and/or action of pro-inflammatory cytokines and inflammatory proteins, including INF-γ, TNF-α, IL-1β, IL-17, IL-6, IL-23, IL-21, inducible Nitric Oxide Synthase (iNOS) and Cyclooxygenase (COX)-2, and diminishes the expression and invasion of neutrophils and CD68+ macrophages into the colon[30,31]. Gallic acid is able to quench the flames of inflammation by means of distinctive mechanisms. It diminishes the expression and discharge of pro-inflammatory and inflammatory mediators, such as substance P, bradykinin, COX- 2, NF-κB, IL-4, IL-2, IL-5, TNF-α and IFN-γ. The compound moreover represses the phagocyte or Polymorphonuclear (PMN) mediated inflammatory reactions by scavenging Reactive Oxygen Species (ROS) and diminishing the Myeloperoxidase (MPO) action[32].

Gallic acid is able to restrain HIV-1 integrase, HIV-1 protease dimerization, HIV-1 transcriptase, Hepatitis C Virus (HCV) replication, HCV attachment and penetration, the Herpes Simplex Virus (HSV)-1, HCV serine protease and HSV-2 attachment and diffusion. It as well causes disturbance in Haemophilus influenzae A and B particles[32,33].

Phenolic compounds through their phenol rings interaction with viral proteins and/or RNA, or via its modifiable MAPK signaling in host cell defense, act as antiviral activity against many viruses such as HCV and HIV. Gallic acid polyphenols executed hydrogen bonds with 1 or 2 of the Nucleoside Triphosphate (NTP) entry channels amino acids in COVID-19 polymerase. Polyphenols binds with NTP of COVID-19 polymerase could influence in the access of the substrate and divalent cations into the central active site cavity, repressing the enzyme activity. It shows promising result that gallic acid displayed high binding resemblance than ribavirin to COVID-19 polymerase and show good drug resemblance and pharmacokinetic properties. Thus, gallic acid may be considered as a potential treatment option for COVID-19[34].

Glycyrrhizin:

As the host cell receptor is significant for virus access, focusing on ACE2 could be a promising potential approach for avoiding SARS-CoV-2 contamination and more valuably, repressing the virus from diffusing out of the contaminated cell and attaching to and entering new permissive target cells. Glycyrrhizin has newly been shown to have the potential to attach with ACE2. Even though this investigation was performed in silico by means of molecular docking and the in vitro exhibition of an interaction remains to be confirmed, glycyrrhizin may still be considered as a latent treatment for COVID-19 because it has an antiviral outcome on SARS-CoV[35].

In addition, glycyrrhizin has been reported to produce endogenous IFN. IFN is suggested in all 7 descriptions of the diagnosis and treatment of pneumonia contaminated by nCoV issued by the National Health Commission of China, maybe because of the current experience of clinical practice on COVID-19 and earlier settlement in management of severe MERS-CoV infection. While IFN is a broad-spectrum antiviral, it would restrain spreading of infection by restraining replication of both DNA and RNA infections at diverse stages of their replicative cycles and by actuating immune cell populations to clear virus infections. For that reason, glycyrrhizin may too play an indirect part in treatment of COVID-19. In the absence of a pathogen-specific antiviral or a targeted vaccine, numerous drugs with antiviral potential have been explored as of late for the treatment of COVID-19. Drug-producing liver injury has become a severe health problem. Glycyrrhizin, with its recognized liver-protection actions, could play a supporting role in COVID-19 treatment[36].

Since ROS play an essential role in inflammatory reaction, antioxidants can also be efficient for the management of cytokine storm stimulated by infection. Glycyrrhizin may be able to inhibit the accumulation of intracellular ROS caused by virus contamination. Inhibition of ROS development through glycyrrhizin can also decrease the activation of c-Jun N-terminal Kinase (JNK), NF-κB, p38 and redox-sensitive signaling procedures that are known to be appropriate for virus reproduction, by this means of suppressing virus reproduction. In expansion, a steady inflammatory or cytokine storm reaction caused by SARS-CoV-2 can result in enactment of coagulation and complement cascades, which may lead to several organs failure. Records appeared that glycyrrhizin could be a specific inhibitor of thrombin. These results indicate that glycyrrhizin has significant therapeutic benefits for COVID-19 through multisite mechanisms[37,38].

Withanone:

Kumar et al. inspected the binding potential of withanone (active withanolides extracted from Ashwagandha) to an extremely conserved protein, Mpro of SARS-CoV-2. They established that withanone attach to the substrate-binding pocket of SARS-CoV-2 Mpro with adequacy and binding energies corresponding to a previously claimed N3 protease inhibitor. Comparative to N3 inhibitor, withanone were binding with the extremely preserved residues of the proteases of CoVs. The interacting stability of these molecules was further evaluated by means of molecular dynamics replication. The interacting free energies deliberated via Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) for N3 inhibitor. Information at this point predicted that these natural compounds may have the potential to repress the efficient activity of SARS-CoV- 2 protease (a crucial protein for virus endurance) and therefore may save time and cost required for designing/development and early screening for anti- COVID drugs; may propose few therapeutic value for the management of original deadly coronavirus disease; warrants prioritized advance approval within the research facility and clinical tests[39].

Balkrishna et al. reported that withanone, docked exceptionally well in the binding border of ACE2- Receptor Binding Domain (RBD) complex and were found to shift somewhat towards the interface middle on simulation. Withanone altogether diminished electrostatic component of interacting free energies of ACE2-RBD complex. Two salt bridges were moreover distinguished at the interface; inclusion of withanone destabilized these salt bridges and diminished their occupancies. They hypothesize, such an intrusion of electrostatic interactions between the RBD and ACE2 would obstruct or deteriorate COVID-19 access and its subsequent contamination[40].

Colchicine:

Colchicine interacts to unpolymerized tubulin heterodimers, producing a constant compound that successfully restrains microtubule dynamics upon interacting to microtubule ends. Besides, colchicine may be a non-selective inhibitor of Nod-Like Receptor Protein 3 (NLRP3) inflammasome. Whereas firstly it has been thought of just as an inhibitor of microtubule polymerization and leucocyte invasion, it is presently assumed that a considerable part of colchicine is recognized to restraint of the NLRP3 inflammasome. Colchicine represses inflammasome on two levels: it restrains P2X Purinoceptor 7 (P2X7) receptor enactment and Apoptosis-associated Speck-like protein containing a Caspase-activation and recruitment domain (ASC) polymerization, in that way hindering interaction among pyrin-like domains[41]. Moreover, colchicine smoothen the transport of mitochondria and consequent approximation of ASC to NLRP3, showing that microtubules mediated the transfer of mitochondria to produce best sites for enactment of the NLRP3 inflammasome. Colchicine has been appeared to constrain IL-1β production as a response to diverse NLRP3 inflammasome inducers in a dose-dependent manner. For instance, in the situation of acute coronary syndrome, colchicine was useful in stifling IL-1β, IL-6 and IL-18, which was credited to inflammasome inhibition[42,43].

The Greek Study in the Effects of Colchicine in COVID-19 complications prevention (GRECCO-19) will be a planned, open-labeled, randomized, controlled study to evaluate the effects of colchicine in COVID-19 complications anticipation. Patients with research lab established SARS-CoV-2 infection (beneath RT-PCR) and clinical picture that includes temperature more than 37.5o and at least 2 out of the below will be included: Sustained throat; sustained coughing pain; fatigue/ tiredness; anosmia and/or ageusia; Partial pressure of oxygen (PaO2)<95 mmHg. Patients will be randomized (1:1) in colchicine or control set[44].

Andrographolide:

Andrographolide is a laboratory diterpenoid derived from Andrographis paniculata stem and leaves. Andrographolide hindered IFN-γ, IL-2 and IL-6 expression, diminishing the cellular and humoral adaptive immune reaction in T cells. Andrographolide diminished the antigen presenting potential of dendritic cells to T cells. The andrographolide administration decreased the serum immunoglobulin, IL-4, IL- 13, IL-5 and T helper type 2 (Th2) cytokine, in an ovalbumin-induced asthma rat model. Andrographolide signifying its role in angiogenesis by decreasing migration and invasion, adhesion molecule Intercellular Adhesion Molecule 1 (ICAM-1) and endothelial cell proliferation[45]. The andrographolide hindered NF- κB interacting to DNA and hence diminishing proinflammatory proteins expression like iNOS and COX- 2[46]. Zhang et al. carried out an experiment to conclude the effect of andrographolide on insulinoma tumor growth. Andrographolide restrain the development of insulinoma tumor by focusing the Toll-Like Receptor 4 (TLR4)/NF-κB signaling pathway[47]. In neutrophils, the generation of ROS was restrained by andrographolide. Andrographolide directed the generation of components such as IFN-γ, Natural Killer (NK) cells, IL-2 and TNF-α. The andrographolide enhanced the expression of CD markers and generation of TNF-α, as a result increasing the cytotoxic potential of lymphocytes[48]. Enmozhi et al. assessed andrographolide as a potential inhibitor of the Mpro of SARS-COV-2 through in silico studies such as target analysis, molecular docking, Absorption, Distribution, Metabolism and Excretion (ADME) prediction and toxicity prediction. Andrographolide was docked effectively in the binding position of SARS-CoV-2 Mpro. This molecule moreover complies with the Lipinski’s rule, which makes it a promising compound to seek after assisting biochemical and cell based assays to investigate its potential for utilization against COVID-19[49].

Astaxanthin:

Astaxanthin is a xanthophyll carotenoid, which is present in Haematococcus pluvialis, Chlorella zofingiensis, Chlorococcum and Phaffia rhodozyma. Astaxanthin essentially constrict pathological elevation, inflammatory cell signaling NF-κB pathway together with in vitro and in vivo study and diminish TNF-α in humans, ensuring reduction in numerous pro-inflammatory cytokine level, which might show potential in maintaining lung health and reducing the impact of SARS-CoV-2 infection. Astaxanthin moreover identified to notably decrease other significant mediators of inflammation, including IL-1β, IL-6, C-Reactive Protein (CRP), COX-2, iNOS, Prostaglandin E2 (PGE2) and Nitric Oxide (NO)[50]. Miyachi et al. studied that treatment with astaxanthin show localization of NF-κB/ p65 and the level of inflammatory cytokines (TNF-α, IL-6) tend to decrease and considerable enhancement of cell proliferation in vitro. Astaxanthin moreover reported to hinder apoptosis in alveolar epithelial cells. Furthermore, to the restraint of NF-κB pathway activation, reduction in the M1/M2 macrophage phenotype proportion is significant in diminishing levels of inflammatory cytokines[51]. These molecules also modulate the generation of Th1 cytokines, such as IFN-γ and IL-2, without causing considerable cytotoxic effects in primary cultured lymphocytes. Astaxanthin applies regulatory activities on the immune system and specifically upgrades the immune response by improving multiplication and development of NK cells, granulocytes, T and B lymphocytes and monocytes[52].

Immunomodulation by natural bioactive molecules is able to give additional therapeutic support to conventional chemotherapy for a range of diseases together with COVID-19, particularly when discriminating immunosuppression is required for autoimmune disorders. Dietary astaxanthin regulate immune response; protect oxidative damage and inflammation simultaneously in human model. Astaxanthin enhanced both cell-mediated and humoral immune responses. Considerable increase of immune markers including B-cell and T-cell mitogen-induced lymphocyte proliferation, Leukocyte Functionassociated Antigen-1 (LFA-1) expression and IFN-γ and IL-6 production were reported[53]. With the viral disease of respiratory epithelial cells, dendritic cells phagocytose the virus and given antigens to T cells. Effector T cells worked by killing the infected epithelial cells and cytotoxic CD8+ T cells create and free pro-inflammatory cytokines which bring cell apoptosis. Both the cell apoptosis and pathogen activate and intensify the host innate immune response. Characteristics of COVID-19 recommend a reduced level of lymphocytes, neutrophils, CD8+ T and CD4+ T cells in peripheral blood specify disease severity[50].

Emodin:

Emodin may show its antiviral activity by restraining casein kinase 2, which is broken by many viruses for the phosphorylation of proteins that are crucial for their life cycle[54]. Emodin also interrupted the lipid bilayer, resulting in the inactivation of enclosed virus[55]. Ho et al. demonstrated that emodin was able to block the SARSCoV S protein and ACE2 interaction. Preincubation of S protein or S protein-pseudo type retrovirus with emodin also eliminated the SARS-CoV and Vero E6 cell interaction. These conclusion recommended that in addition to disrupting the viral envelope, emodin might stop SARS-CoV infection by opposing the interacting site of S protein with ACE2[56]. Promazine is a phenolic compound consisting of three cyclic rings which has been considered to show anti-SARS-CoV effect. Emodin along with promazine inhibited the S protein and ACE2 in a dose-dependent manner. This outcome suggested that the side chains except the anthraquinone skeleton have an immense impact on the S protein and ACE2 binding. These conclusions also point out that promazine shown anti-SARS effect by restraining both the virus access and protein processing[56,57]. Schwarz et al. observed that emodin capable of restraining the 3a ion channel of coronavirus SARS-CoV and Human Coronavirus OC43 (HCoV-OC43) plus virus discharge from HCoV-OC43 with a K1/2 value of about 20 μM. They propose that viral ion channels could be an excellent target for the development of antiviral agents[58].

Some other phytochemicals used in the treatment of COVID-19:

A product of the plant Artemisia annua, artemisinin is a class of antimalarial medicines that have been marketed is used in the treatment. Increasing the severity of SARSCoV- 2 infection is associated with the development of pulmonary fibrosis, which is mediated by IL-1. There have been many studies that indicate that oxidative stress is linked with pulmonary illnesses and it is probable that the intake of natural antioxidants is beneficial in the treatment of lung fibrosis. The antioxidant activity of Artemisia annua extract is considerable, which is most likely owing to the high phenolic content of the extract. A derivative of Artemisia annua is artesunate, has shown promising effect in the treatment of pulmonary fibrosis. It works by blocking pro-fibrotic molecules linked with the disease[59]. Artemisinin and its derived molecules demonstrated an additional mode of interaction by binding to the Lysine (Lys) 353 and Lys31-binding hotspots of the SARS-CoV-2 spike protein and producing a better Autodock Vina score than hydroxychloroquine. Artemisinin and its derived molecules were found to have a higher Autodock Vina score than hydroxychloroquine. The findings of the research also showed that the complexes produced interfered with the SARS-CoV-2 Spike protein receptor site and stayed stable on the receptor site. Additionally, Artemisia has a high concentration of zinc, which has been shown to be beneficial for the immunomodulation impact of the host response as well as an increase in CD4 cell count[60].

Betulinic acid (Bet) is a naturally occurring product with a pentacyclic triterpene nucleus that exhibits a broad spectrum of biological and pharmacological activities including antiviral, anti-HIV, antibacterial, antiinflammatory, anthelmintic, anticancer and antimalarial properties. Bet is found in plants and has a pentacyclic triterpene nucleus. Bet (A8) interacts with Glycine 274 (GLY 274), Leucine 287 (LEU 287), Methionine 276 (MET 276) and LEU 286 amino acids and forms two hydrogen bonds with each of these amino acids, which represents the majority of sulfhydryl groups in the amino acid. Despite this, Bet (A3) and Bet (A4) were the most often suggested possible inhibitors of COVID- 19’s primary protease[61].

Luteolin substantially increases the amount of CD4+ CD25+ regulatory T-cells in murine splenic CD4+-T cells that have been activated with anti-CD3/anti- CD28 antibodies, as shown in this study. Luteolin also shown immunomodulatory action, reducing the amount of immune cells such as CD19+ B, CD4+ T, CD3-C-C chemokine receptor type 3+ (CCR3+) and CD11b+ Granulocyte-1 (Gr-1+) in the lungs of an inflamed airway mouse model with inflamed airways. Luteolin also has anti-inflammatory properties, since it inhibits the NF-κB pathway, lowers TNF-α, IL-6 and IL-1α levels and substantially decreases MPO activity in the blood. Additionally, luteolin had a protective effect against the Lipopolysaccharide (LPS)-induced Acute Lung Injury (ALI), mice model by inhibiting MAPK pathways, which resulted in the suppression of the NF-κB pathway and the degradation of IκB protein. By inhibiting microsomal PGE synthase 1 and 5-lipoxygenase, caflanone has anti-inflammatory action on the body.

It is widely known that flavonoids are phenolic natural compounds that are used in the treatment of a variety of illnesses, including viral infection, in both traditional and contemporary medicine. CoVs, particularly the current pandemic epidemic caused by the SARS-CoV-2 and identified as COVID-19, have been shown to be susceptible to flavonoids potential inhibitory action against them. All flavonoids were found in in silico as possible SARS-CoV-2 inhibitors. Specifically, it has been determined that Mpro is needed for the replication of the SARS-CoV[62]. Further investigation revealed that the Mpro of SARS-CoV-2 and SARS-CoV are very similar. A dose-dependent antiviral activity against the HSV-1 and HSV-2 was also shown in cell cultures using quercetin. In cultured cells, it has been shown to suppress a number of different respiratory viruses.

Several rhinovirus, echovirus (type 7, 11, 12 and 19), coxsackievirus (A21 and B1) and poliovirus serotypes are inhibited in their cytopathic effects by this substance (type 1 Sabin). Antiviral action against the Canine distemper virus is shown by the fact that it reduces viral expression while increasing cellular survival. This compound has been investigated in different kinds and models of viral infection because of its potential antiviral actions on polymerases, proteases, reverse transcriptase, decreasing DNA gyrase and binding viral capsid proteins. 3CLpro was shown to be inhibited by this compound, which was discovered as one of the components of Pichia pastoris. Phytochemical studies have shown that quercetin-3-O-galactoside binds to SARS-CoV 3CLpro and inhibits its proteolytic activity[63]. Chrysin has the ability to inhibit the NF- κB, which regulates the production of genes encoding pro-inflammatory cytokines such as COX-2 and iNOS. Furthermore, it is an agonist of the Peroxisome Proliferator-Activated Receptors (PPAR) receptor, which inhibits the expression of COX-2, MPO and iNOS. The pre-treatment of mice with chrysin before they were exposed to cigarette smoking to induce inflammation of airway epithelial cells alleviated the inflammation by suppressing the release of TNF-α, IL-1α, IL-8 and MPO expression in the lung tissue, as well as the expression of MPO in the lung tissue. Chrysin also has the additional effect of inhibiting ERK and p38 phosphorylation. The immunomodulatory effect of chrysin on rat peritoneal macrophages was investigated in another study. Chrysin was found to stimulate macrophage lysosomal activity, which was involved in killing and digesting the microbial pathogens, as well as inhibiting the production of NO in this study. A docking research revealed that chrysin may bind poorly to COX-1 enzymes but strongly to COX-2 enzymes, suggesting that it has relative selectivity for COX-2 enzymes and as a result, lowers the likelihood of unwanted Gastrointestinal (GIT) side effects. Similar results were obtained when apigenin was administered prior to the induction of inflammation in human macrophages[62,63].

Apigenin was shown to significantly decrease IL-6 production as well as the stability of IL-6 messenger RNA (mRNA). HCoV-Netherland 63 (NL63) replication was shown to be inhibited by tryptanthrin, which was discovered to be the most active component in Strobilanthes cusia leaf methanol extract in a celltype- independent manner. Intriguingly, tryptanthrin has a higher antiviral activity against HCoV-NL63 than indigodole B (5aR-ethyltryptanthrin), which has an additional ethyl moiety at C5a instead of the double bond in tryptanthrin. This demonstrates that the double bond in the quinazoline ring of the tryptanthrin skeleton is the active contributor to the antiviral tryptanthrin which also changes the antigenic structure of viral spike proteins and reduces the cleavage activity of Proteolipid Protein 2 (PLP2), which is linked with virucidal activity and inhibits the post-entry stage of HCoV-NL63 replication, in addition to other effects. Our particular interest is that the spike protein from HCoV-NL63 has significant sequence and structural similarities with the viruses that cause SARS and COVID-19, indicating that all of these viruses use the ACE2 receptor[64]. This is consistent with the fact that all of these viruses use the ACE2 receptor. Lycorine is a phenanthridine alkaloid from the Amaryllidaceae family that was discovered in the bulbs of the plant Lycoris radiata. Several CoV infections, including the SARS-CoV infection and four additional CoV infections, such as the HCoV-OC43 infection, the MERS-CoV infection, the Mouse Hepatitis Virus strain A59 (MHV-A59) infection, and the HCV-OC43 infection, have been shown to be inhibited by lycorine. Lycorine has been shown to inhibit viral RNA replication as well as viral protein synthesis in the presence of poliovirus, Enterovirus 71 (EV71) and H5N1 avian influenza virus. Recently, it was proposed that lycorine may block Zika virus viral RNA production and bind to the Zika RNA-dependent RNA polymerase (RdRp) protein, thus preventing the virus from spreading. According to the findings, lycorine forms hydrogen bonds with RdRp at the amino acid residues Aspartic acid 623 (Asp623), Asp691 and Serine 759 (Ser759), which is comparable to remdesivir[65].

Conclusion

nCoV (COVID-19) is causing an increasing number of cases of pneumonia and was declared a Public Health Emergency of International concern by the World Health Organization.

According to WHO, major concern among public health throughout the world and many countries have taken precautionary measures against the virus and Government officials in all countries continue to make hard work to diminish person to person contact by facilitating area wise shutdowns of public places plus a variety of steps have been instigated to ensure the security of the public, similar to social distancing and self-quarantine which limits our social interactions.

Although a large number of review articles have been published since the COVID-19 epidemic, the significance of natural products in the prevention and treatment of SARS-CoV-2 has received little attention. However, there is a body of research describing a variety of naturally occurring chemicals that have strong anti-SARS-CoV and anti-MERS-CoV action. Unquestionably, there is a high degree of sequence similarity between SARS CoV-2 and either SARS-CoV or MERS-CoV. The use of computational methods to repurpose these anti-SARS-CoV or anti-MERS-CoV natural compounds may lead to the identification of candidates for the development of COVID-19 medication that is both safe and cost-effective.

Herbal medicines have gathered thousand-of-year’s experiences in the treatment of pandemic and endemic diseases. Providing complementary and alternative treatments are still urgently needed for the management of patients with SARS-CoV-2 infection, experiences in herbal medicine is certainly worth learning. Fighting against existing pandemic also give an opportunity to test the true significance of phytomedicine in treating promising infectious diseases. Numerous phytochemicals have revealed inhibitory activity against HIV proteases which also have immunomodulatory activity and these molecules can be promising drugs for COVID-19. These phytochemicals can be used to ameliorate the symptoms of COVID-19. Though many phytochemicals have been identified, a lot of research has to be carried out for the development of drug specific to SARS-CoV-2. Therefore, it is important to explore the effect of these prescribed phytochemicals on SARS-CoV-2.

Acknowledgements

The authors recognize the contribution of MPREX healthcare for the financial and infrastructural assistance provided during the present review work.

Conflict of Intetests

The authors declared no conflict of interest.

References



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Tanshinone IIA Induces Apoptosis of Leukemia Cancer Cells and Inhibits Tumor Growth In Vivo Through Mitochondrial Pathway

June 23, 2022 Published by


Lei Deng, P. Tian and S. Chen*

Department of Hematology and Rheumatology, Ankang Central Hospital, Ankang, Shaanxi Province 725000, China

*Corresponding Author:

S. Chen

Department of Hematology and Rheumatology, Ankang Central Hospital, Ankang, Shaanxi Province 725000, China

E-mail: [email protected]







Date of Received 20 February 2021
Date of Revision 04 December 2021
Date of Acceptance 26 May 2022
Indian J Pharm Sci 2022;84(3):654-659  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.960

Abstract

To investigate the effect of Tanshinone IIA on apoptosis of leukemic cancer cells and tumor growth inhibition in vivo. Human leukemia cell line HL-60 cells were divided into model group, low-dose group, medium dose group and high-dose group and transfected according to the groups. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide method was used to calculate the inhibition rate of cell growth, flow cytometry was used to detect the apoptosis rate and mitochondrial membrane potential and Western blot was used to detect mitochondrial membrane potential related protein. The treatment group was treated with Tanshinone IIA and the control group was given phosphate-buffered saline+dimethyl sulfoxide intraperitoneal injection. 2 w later, the tumor size and weight were measured and weighed. The 24 h and 48 h proliferation inhibition rates of the experimental group were significantly higher than those of the model group and increased with the increase of the dose (p<0.05). The early apoptosis rate and late apoptosis rate of the experimental group were significantly higher than those of the model group and increased with the increase of the dose (p<0.05). The level of integral optical density in the experimental group was significantly lower than that in the model group and decreased with the increase of the dose (p<0.05). The levels of caspase-9, caspase-3 and B-cell lymphoma 2 related X protein in the experimental group were significantly higher than those in the model group, while the level of B-cell lymphoma 2 in the experimental group was significantly lower than that in the model group and there was drug dependence (p<0.05). The tumor size and weight of the treatment group were significantly lower than those of the control group (p<0.05). Tanshinone IIA can induce apoptosis and tumor growth of leukemia cancer cells in vivo and slow down the proliferation of cancer cells, which may be related to the regulation of mitochondrial pathway and the inhibition of mitochondrial membrane potential related protein.

Keywords

Tanshinone IIA, mitochondrial pathway, leukemia, apoptosis, tumor growth, mechanism

Acute Myeloid Leukemia (AML) is a common hematologic malignancy, whose pathology mainly characterized in that marrow primitive granulocytes are more than 20 %[1]. However, the pathogenesis of AML is not fully understood at present. And many studies agree that AML is attributed to a combination of environmental, genetic and biological behaviors[2]. Chemotherapy is currently the first-line treatment for patients with AML, but there are still some patients who are not sensitive to chemotherapy, while longterm chemotherapy also foster drug resistance and unsatisfactory chemotherapy treatment[3].

Tanshinone IIA (TAT) is the main active ingredient of Salvia miltiorrhiza, one of the Lamiaceae plants, which has antioxidant, anti-vasodilatory, anti-liver fibrosis and anti-liver injury functions and is widely applied in clinical cardiovascular and inflammatory diseases[4]. Recent studies have revealed the apoptotic, proliferative and migratory activities of TAT in various cancer cells. Zhang et al.[5] showed that TAT may inhibit the Wingless-related integration site (Wnt)/ beta (β)-catenin signaling pathway and reduce the expression of Glycogen Synthase Kinase-3β (GSK-3β), Axin and Adenomatous Polyposis Coli (APC) proteins, thus inhibiting the invasive metastasis of hepatocellular carcinoma stem cells. Currently, there are few studies related to TAT for the treatment of patients with AML. The experiment in the study selects human leukemia cell line HL-60 cells as the observation subjects and aims to analyze the effect of TAT on apoptosis of leukemia cancer cells and tumor growth inhibition in vivo and related mechanism thereof.

Materials and Methods

General material:

Experimental cell: Human leukemia cell line HL-60, which is provided by Wuhan Boster Biological Technology Co., Ltd.

Experimental animal: Clean healthy female Nonobese Diabetic/Severe Combined Immunodeficiency (NOD/ SCID) mice, 6±1 w, 19±1 g, which are provided by Shanghai SLAC Laboratory Animal Co., Ltd. Mice were placed in an animal room at 20°-23°, 40 %-70 % humidity, with 12 h day and night alternation and given a laboratory diet with free access to water. And the experiments were performed after rearing 1 w. The experimental operations of the animals in this experiment were in accordance with the relevant standards of the Regulations for the Administration of Laboratory Animals. All experimental operations on experimental animals were in accordance with the relevant standards of the Regulations for the Administration of Affairs Concerning Ex-Experimental Animals.

Experimental drug: TAT (purity ≥98 %), which is provided by Chengdu Must Bio-Technology Co., Ltd.

Methods:

Cell culture and grouping: HL-60 cells were cultured in Roswell Park Memorial Institute (RPMI)-1640 medium, where the medium was placed at 37° and 5 % carbon dioxide saturated humidity to culture and subculture. Cells grown in log phase were taken and randomly divided into model group, low-dose group, medium-dose group and high-dose group.

Mice subcutaneous transplantation and treatment with drugs: There were 40 NOD/SCID mice selected to establish subcutaneous implantation experiment. HL-60 cells in logarithmic growth phase were taken and their concentration was adjusted to 1×107 cells/ml to mix with MatrigelTM basement membrane matrix, then the mixture was injected into the left side of axilla of each mouse by 1 ml syringe. Upon observation of tumor development to 100 mm3, the mice were randomly divided into control and treatment groups, in which the treatment group was given 50 mg/kg TAT intraperitoneal injection and the control group was given the same amount of Phosphate-Buffered Saline+Dimethyl Sulfoxide (PBS+DMSO) intraperitoneal injection and both groups were treated for 2 w.

Observation index:

MTT assay to detect the cell proliferation ability for each group: HL-60 cells were cultured in RPMI-1640 medium, then the cell concentration was adjusted to 1×105 cells/ml and TAT at indicated concentrations (20, 40, 60) μmol/l and DMSO were given according to the group and centrifuged after 24 h and 48 h of transfection respectively. Subsequently, 200 μl of DMSO was added to each well and the absorbance value at 490 nm was measured by enzyme standardization, followed by calculation of the growth inhibition rate of each group of cells.

Flow cytometry to detect the apoptosis rate of each group: HL-60 cells were transfected in groups and washed with PBS and then the cells were resuspended, followed by the addition of 5 μl of AnnexinV-FIFC and mixed well. After that, 5 μl of Propidium iodide (PI) was added and mixed well, with photophobic reaction for 10 min. The early apoptosis rate and late apoptosis rate of each group of cells were detected by flow cytometry.

Flow cytometry to analyze mitochondrial membrane potential changes: HL-60 cells were transfected in groups, centrifuged and washed with PBS, and then 5,5,6,6′-tetrachloro-1,1′,3,3′ tetraethylbenzimidazoylcarbocyanine iodide (JC-1) working solution was prepared to resuspend cells. Cells were incubated for 15 min within dark, centrifuged and washed for cell smear and visualized by microscopy. Image Pro Plus image analysis software was used to detect the ratio of red and green Integral Optical Density (IOD) value.

Western blot assay to detect mitochondrial membrane potential-related proteins: HL-60 cells were transfected in groups, washed with PBS and followed by lysis in ice bath for 20 min with the addition of cell lysis solution. Then the supernatant was taken to quantify the proteins of each group by Bicinchoninic Acid (BCA) assay, the proteins were electrotransferred to Polyvinylidene Difluoride (PVDF) membrane by Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), blocked with skim milk for 1 h, incubated with primary antibody overnight and blocked with secondary antibody for 1 h. BIO-RAD chemical imaging system was used to develop and image the relative content of each group of caspase-9, caspase-3, B-lymphoma-2 (Bcl-2) and Bcl-2 related X protein (BAX), with Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) as the internal reference.

Tumor size detection of mice in groups: Mice were executed by cervical vertebrae luxation after intraperitoneal injection of the drug for 2 w and tumor tissues were taken from each group of mice to measure and weigh the tumor size and weight.

Statistical methods:

In this study, the data were analyzed statistically by Statistical Package for the Social Sciences (SPSS) 20.0 software package. The measured data such as 24 h proliferation inhibition rate, 48 h proliferation inhibition rate, early apoptosis rate, late apoptosis rate, IOD, caspase-9, caspase-3, BAX, Bcl-2, tumor size and weight were all conformed to normal distribution and expressed by (x±s) and the comparison among multiple groups was performed by one-way Analysis of Variance (ANOVA) and Student Newman Keuls-q test were used for pairwise comparison; and the statistical results were statistically significant at p<0.05.

Results and Discussion

The 24 h proliferation inhibition rate and 48 h proliferation inhibition rate of cells in the experimental group are significantly higher than those in the model group, which are elevated with the increase of dose and the difference is statistically significant (p<0.05) as shown in Table 1.











Group 24 h inhibition rate (%) 48 h inhibition rate (%)
Model group 0.62±0.03 0.11±0.01
Experimental group 2 d 2 d
Low-dose group 1.03±0.24a 1.06±0.14a
Medium-dose group 2.06±0.46ab 1.25±0.15ab
High-dose group 3.41±0.25abc 3.06±0.23abc
F 372.29 1277.03
p <0.001 <0.001

Table 1: Comparison of Cell Proliferation Inhibition Rate in Groups (X±S).

The early apoptosis rate and late apoptosis rate of cells in the experimental group are significantly higher than those in the model group, which are elevated with the increase of dose and the difference is statistically significant (p<0.05) as shown in Table 2.











Group Early apoptosis rate (%) Late apoptosis rate (%)
Model group 0 0
Experimental group    
Low-dose group 79.26±0.26a 1.06±0.14a
Medium-dose group 88.65±0.45ab 1.25±0.15ab
High-dose group 93.64±0.58abc 3.06±0.23abc
F 7088.77 5273.81
p <0.001 <0.001

Table 2: Comparison of Apoptotic Ability in Groups (X±S).

The mitochondrial IOD of cells in the experimental group is significantly lower than that in the model group, which decreases with the increase of dose and the difference is statistically significant (p<0.05) as shown in Table 3.











Group IOD
Model group 1.32±0.16
Experimental group <0.001
Low-dose group 0.98±0.16a
Medium-dose group 0.71±0.12ab
High-dose group 0.49±0.25abc
F 80.04
p <0.001

Table 3: Changes on the Mitochondrial Membrane Potential of the Cells in Groups (X±S).

The levels of caspase-9, caspase-3 and BAX in the cells of the experimental group are significantly higher than those of the model group, while the level of Bcl-2 is significantly lower than that of the model group, drug dependence exists and the differences are statistically significant (p<0.05) as shown in Table 4.











Group Caspase-9 Caspase-3 BAX Bcl-2
Model group 0.45±0.03 0.52±0.12 0.85±0.12 2.64±0.16
Experimental group <0.001 <0.001 <0.001 <0.001
Low-dose group 1.36±0.06a 0.87±0.26a 1.06±0.18a 2.31±0.15a
Medium-dose group 1.68±0.15ab 1.23±0.21ab 1.49±0.25ab 2.01±0.12ab
High-dose group 2.03±0.21abc 1.46±0.25abc 2.16±0.45abc 1.46±0.85abc
F 516.76 72.14 72.14 25.59
p <0.001 <0.001 <0.001 <0.001

Table 4: Comparison of Mitochondrial Membrane Potential-Related Protein Expression Levels in the Groups of Cells (X±S).

The tumor size and weight of mice in the treatment group are significantly lower than those in the control group and the difference is statistically significant (p<0.05) as shown in Table 5.








Group  Tumor size (mm3) Tumor weight (g)
Control group 1652.38±236.52 4.36±1.02
Treatment group 563.85±125.36 2.46±0.86
t 18.185 6.368
p <0.001 <0.001

Table 5: Analysis of Changes in Tumor Size and Weight of Mice in Groups (X±S).

AML is one of the most common types of leukemia in adults and currently chemotherapy is still the mayor treatment for AML. However, the treatment effect is poor and there are still about 70% of patients who obtain remission eventually relapse and evolve into refractory leukemia, leading to treatment failure and death[6-8].

Therefore, scholars at home and abroad have focused on developing an effective and safe treatment for AML. TAT is the most abundant active ingredient in the Chinese herbal medicine Salvia miltiorrhiza, which is widely used in clinical practice[9]. Chen et al.[10] studied that TAT could contribute to anti-atherosclerotic effects via promoting the activation of M2-type cells to secrete anti-inflammatory factors and inhibiting the activation of M1-type cells to secrete pro-inflammatory factors. Recent studies have revealed that TAT has greater potential for biomedical applications and serves as a regulator of biological activities in malignant tumors such as breast, rectal, gastric and bladder cancers.

Wenna et al.[11] revealed that TAT could enhance the sensitivity of breast cancer cells to Adriamycin chemotherapy by regulating the APC/β-catenin signaling pathway, with certain adjuvant therapeutic effects. Zhou et al.[12] concluded that TAT could lower the level of Hypoxia-Inducible Factor 1-alpha (HIF-1α) in human colon cancer cells to inhibit cellular pro-angiogenic factor expression and suppress the angiogenesis of Human Umbilical Vein Endothelial Cells (HUVECs). Liu et al.[13] showed that TAT could inhibit gastric cancer cell proliferation and induce apoptosis by reducing COX-2 and NF-κB pathwayrelated protein levels.

In this experiment, the 24 h proliferation inhibition rate and 48 h proliferation inhibition rate of cells in the experimental group are significantly higher than those in the model group, which are higher with the increase of the dose. And the early apoptosis rate and late apoptosis rate in the experimental group are significantly higher than those in the model group, which are higher with the increase of the dose.

It is indicated that TAT could induce apoptosis and inhibit cell proliferation in leukemic cancer cells. In this experiment, we further establish a tumor growth mouse model in vivo and find that the tumor size and weight of mice in the treatment group are significantly lower than those in the control group, further indicating that TAT has an inhibitory effect on the development of leukemia, which is similar to the findings of Bh et al.[14].

Apoptosis is a form of programmed cell death comprising the death receptor pathway and the mitochondrial pathway[15]. Mitochondria are the power house within eukaryotic cells as a significant regulator in cell survival, proliferation, apoptosis, metastasis and other activities in a variety of malignant tumor cells including lung, breast and prostate cancers.

In the mitochondrial pathway, the Bcl-2/BAX ratio act as a regulator and a reduced ratio level can lead to mitochondrial dysfunction. Then mitochondria release large amounts of cytochrome c, which activates caspase-9, which further activates various caspases such as caspase-3, causing a caspase cascade reaction and ultimately inducing apoptosis[16,17]. Shen et al.[18] studied that both arsenic trioxide and quercetin could induce apoptosis in leukemic cells by the intervention of altered mitochondrial structure and thus activating the induced mitochondrial apoptosis pathway.

In this experiment, the IOD level of cells in the experimental group is significantly lower than that in the model group, which decreases with the increase of dose. The levels of caspase-9, caspase-3 and BAX in the experimental group are significantly higher than those in the model group, while the levels of Bcl-2 are significantly lower than those in the model group, with drug dependence. It is indicated that TAT can induce apoptosis, inhibit cell proliferation and delay the development of leukemia. Moreover, its mechanism may be associated to the regulation of mitochondrial pathway by TAT.

In summary, TAT can induce apoptosis and tumor growth of leukemia cancer cells in vivo and slow down the proliferation of cancer cells, which may be related to the regulation of mitochondrial pathway and the inhibition of mitochondrial membrane potential related protein.

Conflict of interests:

The authors declared no conflicts of interest.

References



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Chemometric Assisted Spectrophotometric Method for the Simultaneous Determination of Olmesartan Medoxomil and Hydrochlorothiazide in Bulk and Tablet Dosage Form

June 23, 2022 Published by


Binny Mehta1*, H. Joshi1, U. Shah1 and P. Patel

Department of Pharmaceutical Chemistry and Analysis, Smt. S. M. Shah College of Pharmacy, Mahemdavad, Aamsaran, Gujarat 387110, 1Department of Pharmaceutical Chemistry and Analysis, Nootan College of Pharmacy, Sankalchand Patel
University, Visnagar, Gujarat 384315, India

*Corresponding Author:

Binny Mehta

Department of Pharmaceutical Chemistry and Analysis, Nootan College of Pharmacy, Sankalchand Patel
University, Visnagar, Gujarat 384315, India

E-mail: [email protected]







Date of Submission 23 February 2021
Date of Revision 24 August 2021
Date of Acceptance 29 May 2022
Indian J Pharm Sci 2022;84(3):669-675  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.962

Abstract

In this work, a numerical method, based on the use of spectrophotometric data coupled to partial least squares, multivariate calibration is evaluated for the simultaneous determination of olmesartan medoxomil and hydrochlorothiazide in bulk and tablet dosage form. Tablet Olmetor-H (HTZ 12.5 mg and OLM 20.0 mg) was used in this study. The equipment used was a Ultraviolet-Visible double beam spectrophotometer with a matching pair of 1 cm quartz cell and electronic balance. Spectra of olmesartan medoxomil and hydrochlorothiazide were recorded at concentrations within their linear ranges 2.5-20 μg/ml and 4-32 μg/ ml, respectively and were used to compute a total of 25 synthetic mixtures involving 16 calibration and 9 validation sets between the wavelength range of 200 nm and 350 nm with the wavelengths intervals, lambda=3 nm in methanol. The analytical performances of these chemometric methods were characterized by relative prediction errors and recovery studies (%), and were compared with each other. The proposed method is simple, rapid and can be easily used as an alternative analysis tool in the quality control of drugs and formulation. PLS was applied successfully for the simultaneous determination of OLM and HTZ in laboratory mixtures and pharmaceutical formulation.

Keywords

Hydrochlorothiazide, olmesartan medoxomil, partial least squares, chemometrics, ultravioletvisible spectrophotometry

Olmesartan Medoxomil (OLM), chemically 2,3-dihydroxy-2-butenyl 4-(1-hydroxy-1-methylethyl)- 2-propyl-1-[p-(O-1H-tetrazol-5-yl phenyl) benzyl] imidazole-5-carboxylate, cyclic 2,3-carbonate is a prodrug and it is hydrolysed to olmesartan during absorption from the gastrointestinal tract (fig. 1A). It is a selective Angiotensin 1 (AT1) subtype angiotensin II receptor antagonist. Hydrochlorothiazide (HTZ), chemically 6-chloro-3,4-dihydro-2,4-1,2,4- benzothiadiazine-7-sulfonamide-1,1-dioxide (fig. 1B), is a widely used thiazide diuretic[1-3]. Olmesartan and HTZ are available in the market as a combined dosage form for the treatment of hypertension. An extensive literature survey revealed the determination of OLM in dosage form by Ultraviolet (UV)-visible spectrophotometry[4,5], High Performance Liquid Chromatography (HPLC)- UV[6,7] and capillary electrophoresis[8] and in biological fluids by HPLC[9] and Liquid Chromatography- Mass Spectrometry (LC-MS)[10,11]. Determination methods of HTZ in pharmaceutical dosage form and biological fluids include chemiluminescence[12], HPLC[13] and electrochemical study[14]. Determination methods of OLM and HTZ combination include UVspectrophotometry[ 15-18], Reverse Phase (RP)-HPLC and High-Performance Thin Layer Chromatography (HPTLC)[19,20]. In this work, a simple, accurate, precise and inexpensive quantitative method has been developed for the simultaneous determination of the coexisting two drugs in the tablet dosage form. The method is based on a Partial Least Square (PLS) multivariate calibration chemometric procedure. Moreover, being simple and inexpensive, it is more appealing to use for routine assay of OLM and HTZ combination in a tablet than HPLC which is demanding in terms of running cost and sophistication.

IJPS-chemical

Fig. 1: Chemical structure of (A) OLM and (B) HTZ

Materials and Methods

The reference standard of HTZ and OLM was obtained as a gratis sample from Sidmak Labs Pvt. Ltd., Valsad, Gujarat. Methanol was procured from Chemdyes Corporation, Vadodara, Gujarat. Tablet Olmetor-H (HTZ 12.5 mg and OLM 20.0 mg) was provided by Torrent Pharmaceuticals. The equipment used was a UV-Visible double beam spectrophotometer with a matching pair of 1 cm quartz cell (Shimadzu UV-1800, Shimadzu Corporation, Kyoto, Japan) and electronic balance (Mettler Toledo). Data was acquired and processed using XLSTAT software and it was used for PLS model development and data analysis.

Preparation of standard stock solution:

Accurately weighed and transferred OLM (10 mg) and HTZ (10 mg) into two different 100 ml volumetric flask respectively and volume was made up to 100 ml with methanol up to the mark. The final concentration of OLM and HTZ were 100 μg/ml.

Preparation of working stock solution:

The standard stock solution of OLM and HTZ 100 μg/ ml was used as a working solution.

Construction of calibration and validation set:

Two sets of standard solutions, a calibration set and a validation set were prepared. The multivariate calibration requires a suitable experimental design of the standard composition of the calibration set to provide the best prediction. The factorial design method was used to construct the calibration set. The application of two factorial designs led to the construction and optimization of the PLS model. Two binary sets of the drug present in the random ratio were prepared, one set with 5 samples so that a total of 25 samples were employed for optimization by PLS method by mixing appropriate volumes of the working standard solutions of OLM and HTZ, and diluting to volume with methanol. Eight validation standard mixtures were prepared. The combination of OLM and HTZ is illustrated in Table 1. The absorption spectra of the prepared solutions were measured from 200- 400 nm with 3 nm intervals. The absorbance data of the calibration set were then subjected to the XLSTAT program for the PLS model. For validation of the PLS model, the concentrations of OLM and HTZ in the validation set were predicted by using the proposed PLS model. The validation of all the methods was performed by International Council for Harmonisation (ICH) Q2 (R1) and International Union of Pure and Applied Chemistry (IUPAC) guidelines for calibration in analytical chemistry.






























Sample

Sr. no.		 Concentration (µg/ml)
OLM HTZ
1 2.5 4
2 2.5 8
3 2.5 16
4 2.5 24
5 2.5 32
6 5 4
7 5 8
8 5 16
9 5 24
10 5 32
11 10 4
12 10 8
13 10 16
14 10 24
15 10 32
16 15 4
17 15 8
18 15 16
19 15 24
20 15 32
21 20 4
22 20 8
23 20 16
24 20 24
25 20 32

Table 1: Composition of Calibration Sample of Olm and Htz

Assay of marketed formulation:

Twenty tablets were weighed and the average weight was calculated. The tablets were triturated thoroughly and mixed. Tablet powder equivalent to 12.5 mg of HTZ and 20.0 mg of OLM, based on label claim was transferred to a 50.0 ml volumetric flask, dissolved by sonication for 15 min with enough methanol, and volume was made up to mark with methanol. The content was filtered through Whatman filter paper (No. 41). A 10.0 ml portion of the above filtrate was further diluted to 50.0 ml with distilled water. A 10.0 ml portion of this solution was further diluted to 50.0 ml with distilled water. The analysis procedure was repeated six times for tablet formulation and the results were shown below.

Results and Discussion

Calibration matrix and selection of spectral zones for analysis by PLS was shown here. Fig. 2 shows the UV spectra for OLM and HTZ individual and the mixture of them in methanol. As shown, there is clear overlapping between them. The spectral overlapping of these drugs prevents the resolution of the mixtures by direct spectrophotometric measurements. OLM exhibits absorption maxima at 257 nm and HTZ exhibits absorption maxima at 225 nm. The OLM and HTZ spectra are overlapped in the absorption maxima.

IJPS-spectra

Fig. 2: Overlay spectra of OLM and HTZ. The UV spectra for OLM and HTZ individually and the mixture of them in methanol

The first step in multivariate methods involved constructing the calibration matrix. The wavelength range used was 200-350 nm. 52 spectral points with 3 nm intervals were selected within this range. The compositions of the calibration mixtures were randomly designed to collect maximum information from the spectra of these mixtures.

The quality of multicomponent analysis is dependent on the wavelength range and spectral mode used. The UV absorption spectra of HTZ, OLM at their nominal concentrations are shown in fig. 2. The calibration set and validation set were randomly prepared with a mixture of OLM and HTZ in methanol (Table 1). The UV spectra were observed and the absorbances were measured at 52 wavelength points in the region between 200-350 nm with 3 nm intervals. The calibration data obtained from the experimental were gathered in a matrix data by Microsoft Office Excel (Version-1811) (all the data were transposed in Microsoft Office Excel). All these data are subjected to PLS treatment by XLSTAT (Ver. 2019). The predicted concentrations of the components in each sample were compared with the actual concentrations of the components in each of the validation samples and Standard Deviation (SD), mean and Relative Standard Deviation (RSD) was calculated (Table 2 and Table 3).




























Observation Weight Actual value Predicted value Residual matrix Standard residual matrix SD Mean RSD
Obs1 1 2.5 1.089 1.411 0.239      
Obs2 1 2.5 1.519 0.981 0.166 1.0208 2.313 50.963
Obs3 1 2.5 3.278 -0.778 -0.132      
Obs4 1 2.5 3.366 -0.866 -0.147      
Obs5 1 5 3.854 1.146 0.194 1.25645 5.6434 22.264
Obs6 1 5 4.838 0.162 0.027
Obs7 1 5 6.487 -1.487 -0.252
Obs8 1 5 6.85 -1.85 -0.313
Obs9 1 5 6.188 -1.188 -0.201
Obs10 1 10 6.188 3.812 0.645 2.2574 9.9088 22.781
Obs11 1 10 9.395 0.605 0.102
Obs12 1 10 11.383 -1.383 -0.234
Obs13 1 10 11.672 -1.672 -0.283
Obs14 1 10 10.906 -0.906 -0.153
Obs15 1 15 16.623 -1.623 -0.275 0.98631 15.697 6.283
Obs16 1 15 16.687 -1.687 -0.285
Obs17 1 15 15.415 -0.415 -0.07
Obs18 1 15 15.449 -0.449 -0.076
Obs19 1 15 14.314 0.686 0.116
Obs20 1 20 18.72 1.28 0.217 0.7998 18.8998 4.231
Obs21 1 20 19.662 0.338 0.057
Obs22 1 20 19.517 0.483 0.082
Obs23 1 20 18.95 1.05 0.178
Obs24 1 20 17.65 2.35 0.398

Table 2: Statistical Analysis of Olm By Pls Method




























Observation Weight Actual value Pred (4) Residual matrix Standard residual matrix SD          Mean RSD
Obs1 1 8.000 2.781 5.219 0.552 12.350 22.64 54.54
Obs2 1 16.000 16.536 -0.536 -0.057
Obs3 1 24.000 22.765 1.235 0.131
Obs4 1 32.000 28.626 3.374 0.357
Obs5 1 4.000 3.520 0.480 0.051 10.342 15.610 66.25
Obs6 1 8.000 7.638 0.362 0.038
Obs7 1 16.000 15.568 0.432 0.046
Obs8 1 24.000 22.838 1.162 0.123
Obs9 1 32.000 28.489 3.511 0.371
Obs10 1 4.000 28.489 -24.489 -2.589 7.954 20.22 39.33
Obs11 1 8.000 9.489 -1.489 -0.157
Obs12 1 16.000 15.410 0.590 0.062
Obs13 1 24.000 20.720 3.280 0.347
Obs14 1 32.000 27.014 4.986 0.527
Obs15 1 4.000 6.681 -2.681 -0.283 8.389 16.40 51.15
Obs16 1 8.000 9.779 -1.779 -0.188
Obs17 1 16.000 16.491 -0.491 -0.052
Obs18 1 24.000 22.235 1.765 0.187
Obs19 1 32.000 26.850 5.150 0.545
Obs20 1 4.000 6.541 -2.541 -0.269 8.4955 16.81 50.53
Obs21 1 8.000 10.636 -2.636 -0.279
Obs22 1 16.000 16.915 -0.915 -0.097
Obs23 1 24.000 22.634 1.366 0.144
Obs24 1 32.000 27.355 4.645 0.491

Table 3: Statistical Analysis of Htz By Pls Method

PLS regression, uses the two-block predictive PLS model to model the relationship between two matrices, X and Y. Also, Partial Least Squares Regression (PLSR) models the structure of X and of Y, which gives richer results than the traditional multiple regression approach. PLSR and similar approaches provide quantitative multivariate modeling methods, with inferential possibilities similar to multiple regression, t-tests and Analysis of Variance (ANOVA). The evaluation of the predictive abilities of the models was performed by plotting the actual known concentrations against the predicted concentrations and the plot of the actual known concentrations against the predicted concentrations are mentioned in fig. 3A-fig. 3C for OLM and fig. 4A-fig. 4C for HTZ. Standardized validation matrix and predicted validation matrix of OLM and HTZ are mentioned in fig. 5A-fig. 5.

IJPS-residual

Fig. 3: (A) Standard residual matrix of OLM; (B) Predicted residual matrix of OLM and (C) Correlation matrix between the actual and predicted concentration of OLM

IJPS-concentration

Fig. 4: (A) Standard residual matrix of HTZ; (B) Predicted residual matrix of HTZ and (C) Correlation matrix between the actual and predicted concentration of HTZ

IJPS-standardized

Fig. 5: (A) Standardized validation matrix of OLM; (B) Predicted validation matrix of OLM; (C) Standardized residual matrix of HTZ and (D) Predicted validation matrix of HTZ

The accuracy of the method was carried out at three levels 80 %, 100 % and 120 % of the working concentration of the sample. A calculated amount of standard solution of OLM and HTZ were spiked with added sample solution to prepare level 80 %, 100 % and 120 % of the working concentration. The analysis procedure was repeated three times. The result was shown in Table 4. The statistical parameters of the validation set and calibration set were illustrated in Table 5 and Table 6.









Level of spiking Blank Amount of standard drug added Amount of drug recovery % Recovery
OLM HTZ OLM HTZ OLM HTZ
Unspiked              
80 0 4 7.6 3.97±0.02 7.58±0.56 98.93±0.87 99.47±1.54
100 0 5 8 4.98±0.02 7.96±0.39 99.85±0.20 99.65±0.25
120 0 6 9.6 5.59±0.02 9.57±0.20 99.63±0.37 99.80±1.68

Table 4: Accuracy Data of Olm And Htz












Observation Weight Actual value Predicted value Residual matrix Standard residual matrix Mean SD
1 1 5 5.122 -0.122 -5.7547 -0.015 0.0212
2 1 5 5.092 -0.092 -4.3396
3 1 10 9.841 0.159 0.76849 0.038 0.2069
4 1 10 9.844 0.156 0.75399
5 1 10 10.201 -0.201 -0.9715
6 1 15 15.122 0.122 1.88854 0.18 0.0646
7 1 15 14.83 0.17 2.63158
8 1 15 14.75 0.25 3.86997

Table 5: Validation Set Analysis By Pls Method for Olm












Observation Weight Actual value Predicted value Residual matrix Standard residual matrix Mean SD
1 1 8 7.993 0.007 0.12374 0.047 0.05657
2 1 8 7.913 0.087 1.53982
3 1 16 16.024 -0.024 -0.4932 -0.0116 0.04866
4 1 16 16.012 -0.012 -0.2466
5 1 16 15.905 0.095 1.95232
6 1 24 23.954 0.046 1.36905 0.01666 0.03361
7 1 24 23.976 0.024 0.71416
8 1 24 24.02 -0.02 -0.5951

Table 6: Validation Set Analysis By Pls Method for Htz

The validated chemometrics-assisted UV spectrophotometric methods were used in the analysis of the marketed formulation Olmetor-H with a label claim of 12.5 mg of HTZ and 20.0 mg of OLM per tablets. The results for drug assays show good agreement with the label claims (Table 7).













Label claim % Label claim
HTZ OLM HTZ OLM
12.5 20 101.2 99.8
    99.8 102
    100.9 100.9
    101.2 101.2
    101.6 101.6
Mean   100.98 100.66
SD±   0.61 0.82
RSD   0.6 0.81

Table 7: Assay Results of Olm and Htz

PLS chemometric method was applied successfully to the simultaneous determination of OLM and HTZ in the pharmaceutical dosage form. The summary parameters of the chemometric method were shown in Table 8.











Parameters OLM HTZ
PLS PLS
Range (μg/ml) 2.5-20 4-32
Wavelength (nm) 200-350  
Δλ (nm) 3 3
Factor 7 7
% recovery 98.93±99.85 99.47±99.80
Assay 100.66 100.98

Table 8: Summary of Uv Spectrophotometric Method Using Pls Method

Conventional multi-component UV spectroscopic methods are not suitable for combination drugs having a narrow difference in absorption maxima lambda (λmax). In such cases, chemometric method serves as an alternative to other sophisticated methods like HPLC. When once the calibration matrix is built and stored in the data computation device, the samples can simply be prepared, diluted and absorbance was measured and the concentration of the sample was read from the stored matrix. PLS was applied successfully for the simultaneous determination of OLM and HTZ in laboratory mixtures and pharmaceutical formulation.

Acknowledgements:

The author(s) would like to thank Sidmak Laboratories Pvt. Ltd. for providing gift samples of OLM and HTZ and Nootan College of Pharmacy, Visnagar for providing the necessary infrastructure to carry out the research.

Conflict of interests:

The authors declared no conflicts of interest.

References



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Effects of Cluster of Differentiation 147 Expression Down-Regulation on Proliferation, Migration and Invasion of Cervical Cancer Cells

June 23, 2022 Published by


Xuejuan Ma, Baojin Zeng*, Lingzhi Zheng, Jingjing Lin, J. Wang and Xing Chen

Department of Obstetrics and Gynecology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Taizhou, Zhejiang Province 317000, China

*Corresponding Author:

Baojin Zeng

Department of Obstetrics and Gynecology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Taizhou, Zhejiang Province 317000, China

E-mail: [email protected]







Date of Submission 05 January 2021
Date of Revision 14 July 2021
Date of Acceptance 30 May 2022
Indian J Pharm Sci 2022;84(3):676-682  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.963

Abstract

To assess the effects of cluster of differentiation 147 on the proliferation, migration and invasion of cervical cancer HeLa cells by inhibiting its expression using short hairpin ribonucleic acid. A total of 110 cases of cervical tissues, including 20 cases of normal cervical epithelial tissues and 90 cases of cervical cancer tissues (30 cases of cervical cancer in situ tissues and 60 cases of cervical squamous cell cancer tissues) stored in our hospital from February 2016 to February 2019 were selected. The expression of cluster of differentiation 147 was detected by immunohistochemistry. Then the correlations of cluster of differentiation 147 expression with clinicopathological parameters such as age, tumor size, invasion depth, clinical stage and lymphatic metastasis of cervical cancer patients were analyzed. Control and experimental groups were transfected with green fluorescent protein-short hairpin ribonucleic acid-cluster of differentiation 147-negative control and green fluorescent protein-short hairpin ribonucleic acid-cluster of differentiation 147, respectively and blank cells were set as normal group. Cell proliferative ability was measured through 5-ethynyl-2′- deoxyuridine labeling assay and the migratory and invasive abilities of the cells were determined using Transwell assay. Western blotting was performed to detect the protein expressions of monocarboxylate transporter 1 and cluster of differentiation 147. The positive expression rate of cluster of differentiation 147 in cervical cancer in situ tissues significantly increased compared with that in normal cervical tissues, which was significantly higher in cervical squamous cell cancer tissues (p<0.05). The expression of cluster of differentiation 147 was not associated with the age and clinical stage of cervical cancer patients (p>0.05), but had close correlations with the tumor size, invasion depth or lymphatic metastasis of cervical cancer patients (p<0.05). Compared with normal group, the proliferative, migratory and invasive abilities of cells were significantly weakened and the protein expressions of monocarboxylate transporter 1 and cluster of differentiation 147 were significantly down-regulated in experimental group (p<0.05). The expression of cluster of differentiation 147 is increased in cervical cancer tissues and inhibiting such expression can prominently decrease the proliferative, invasive and migratory abilities of tumor cells in vitro and in vivo.

Keywords

Cluster of differentiation 147, short hairpin ribonucleic acid, cervical cancer, proliferation, migration, invasion

Cervical cancer is one of the common reproductive system malignancies, of which the cancer in situ frequently occurs at the age of 20-45 y old, while invasive squamous cell cancer has a high incidence rate among people aged 45-55 y old. Cervical cancer is a leading cause of cancer-related death of females and its morbidity and mortality rates are particularly higher in developing regions. Since the disease has an insidious site and no typical clinical symptoms in the early stage and rapidly progresses due to ultra-strong malignant proliferation and target organ metastasis of tumor cells, most patients have been in the intermediate and advanced stage when definitely diagnosed, missing the best timing for surgical resection[1]. The recurrence and malignant invasion of tumor are important causes of death of patients with intermediate and advanced cervical cancer[2]. Hence, actively exploring the specific molecular markers for cancer cell proliferation and metastasis from the perspective of molecular mechanism of the disease is not only beneficial to the early diagnosis of cervical cancer, but also conducive to conducting targeted therapy and reducing mortality rate of patients. Extracellular Matrix Metalloproteinase (MMP) inducer, also known as Cluster of Differentiation 147 (CD147), is a transmembrane glycoprotein in the immunoglobulin superfamily. CD147 can enhance MMP expression in tumor cells and promote the infiltration, invasion, adhesion and malignant metastasis of cancer cells[3]. The expression of CD147 is raised in multiple malignant tumors including laryngeal squamous cell carcinoma, nonsmall cell lung cancer, breast invasive ductal carcinoma and ovarian cancer, and tumor patients with a high CD147 expression have a poor prognosis[4]. However, the role of CD147 in cervical cancer is rarely reported. Short hairpin Ribonucleic Acid (shRNA) refers to an RNA interference technique for silencing target gene expression by means of gene technology[5]. In this study, the expression of CD147 in cervical cancer was detected by researching the clinical tissue samples of cervical cancer and cancer cells, and its influences on the proliferation, invasion and metastasis of cervical cancer cells were investigated, aiming to provide reliable target sites for targeted therapy of cervical cancer in clinic.

Materials and Methods

Data of clinical tissue samples:

A total of 110 cases of cervical tissues, including 20 cases of normal cervical epithelial tissues and 90 cases of cervical cancer tissues (30 cases of cervical cancer in situ tissues and 60 cases of cervical squamous cell cancer tissues) stored in the pathological tissue room of our hospital from February 2016 to February 2019 were selected. Before sample collection, all patients were diagnosed with cervical cancer for the first time and none of them received radiotherapy, chemotherapy, immunotherapy or endocrine therapy for intervention. It was found through statistical analysis that there were no significant differences in age, childbearing or not, body height and body weight of patients (p>0.05). The patients or their families were informed of the study protocol and objective, and signed the informed consent. This study was reviewed and approved by the Ethics Committee of the Zhejiang Taizhou Hospital Affiliated to Wenzhou Medical University.

Cells and main reagents:

Cervical cancer HeLa cell line was provided by American Type Culture Collection. RNA interference technique and the plasmids for constructing adenovirusmediated Green Fluorescent Protein (GFP)-shRNACD147 and GFP-shRNA-CD147-Negative Control (NC) were offered by Shanghai GenePharma Co., Ltd.

Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) and rat anti-rabbit antibodies against CD147 and Monocarboxylate Transporter 1 (MCT1) were purchased from Abcam (USA). Kits for 5-Ethynyl-2′-deoxyuridine (EdU) labeling assay, immunohistochemistry, Transwell assay and Western blotting were bought from Shanghai Beyotime Biotechnology Co., Ltd. In addition, micropipette (Eppendorf, Germany), intelligent incubator (SANYO, Japan), IXplore Standard inverted microscope (Olympus, Japan), real-time fluorescence quantitative Polymerase Chain Reaction (PCR) instrument (ABI, USA), fluorescence microscope (Nikon, Japan) and gel imaging system (UVP, USA) were employed.

Detection of CD147 expression in clinical samples by immunohistochemistry:

The sample tissues were routinely fixed, embedded in paraffin and sliced to 4 μm-thick sections for retrieval with citric acid buffer solution and blocking with hydrogen peroxide. Then the tissues were incubated with primary antibodies in a cassette at 4° overnight and added with secondary antibodies, followed by color development, cleaning, counterstaining, dehydration, transparentization, mounting and microscopic examination. The yellowish-brown granules in the field of vision indicated positive results[6]. Positive or negative expression of CD147 was judged based on the staining intensity and percentage of positive cells in 5 fields of vision randomly selected from each sample by two senior pathological researchers. Moreover, the correlations of CD147 expression with clinicopathological parameters such as age, tumor size, invasion depth, clinical stage and lymphatic metastasis of patients were analyzed.

Cell transfection:

After thawing, HeLa cells were inoculated into a Dulbecco’s Modified Eagle Medium (DMEM) containing inactivated 10 % Fetal Bovine Serum (FBS) and cultured in a constant-temperature and constanthumidity biochemistry incubator with 5 % Carbon dioxide (CO2) at 37°. The experiment was initiated when the cell density reached 85 %. The cells in control group and experimental group were transfected with GFP-shRNA-CD147-NC and GFP-shRNA-CD147, respectively and blank cells were set as normal group.

All the three groups of cells were routinely cultured for 24 h and the transfection efficiency was determined using the PCR instrument.

Observation of morphological changes of cells:

The cells were seeded into 6-well culture plates at 5×104 cells/well after concentration adjustment and subjected to the aforementioned grouped treatments. Next, the cells were routinely cultured in the constanttemperature and constant-humidity biochemistry incubator for 24 h after gentle mixing, fixed with 10 % formaldehyde and observed under an inverted phasecontrast microscope.

Detection of proliferative ability of cells by EdU labeling assay:

After concentration adjustment, the cells were seeded into 6-well culture plates at 5×104 cells/well and received the same grouped treatments as mentioned above. Next, the cells were routinely cultured in the constant-temperature and constant-humidity biochemistry incubator for 24 h after gentle mixing, fixed with 10 % formaldehyde and subjected to the EdU labeling assay strictly according to the kit instructions.

Detection of migratory and invasive abilities of cells by Transwell assay:

A layer of Matrigel (about 50 μl) was paved onto the plate in the Transwell chambers, the single-cell suspension of the serum-free medium in each group was added into the upper chamber (Matrigel was paved in invasion assay instead of migration assay) and the lower chamber was paved with the DMEM containing inactivated 10 % FBS, followed by routine culture in a 5 % CO2 incubator at 37° for 24 h. After washing, the chambers were fixed using formaldehyde, stained and observed under the microscope. Finally, the transmembrane cells were counted and recorded.

Detection of expression levels of CD147 and MCT1 in cells by Western blotting:

The cells were digested with trypsin, centrifuged and diluted into single-cell suspension, followed by the aforementioned grouped treatments. Then the cells were seeded into 6-well plates at 1×105 cells/well and cultured in the incubator with 5 % CO2 at 37° for 24 h. Later, protein lysate was added to extract total proteins from cells in each group. Next, the concentration was detected and the proteins were denatured in boiling water, separated by electrophoresis and transferred onto a membrane. After washing, the proteins were incubated with blocking buffer for 30 min, primary antibody (1:500) overnight and secondary antibody (1:1000), followed by color development with diaminobenzidine. Finally, the grayscale value of each band was statistically analyzed using ImageJ image analysis software with GAPDH as the internal reference.

Statistical analysis:

Statistical Package for the Social Sciences (SPSS) 19.0 software was used for statistical analysis of experimental data. The measurement data were represented as mean±standard deviation and analyzed by t-test. All the data conformed to normal distribution. The numerical data were analyzed by Chi-square (χ2) test. p<0.05 suggested statistically significant differences.

Results and Discussion

The results of immunohistochemistry indicated that the positive expression rate of CD147 in cervical cancer in situ tissues was significantly increased compared with that in normal cervical tissues, while it was significantly decreased in contrast with that in cervical squamous cell cancer tissues (p<0.05) as shown in fig. 1.

IJPS-cervical

Fig. 1: CD147 expression (400×)

Note: *p<0.05 vs. normal cervical tissues and #p<0.05 vs.cervical cancer in situ tissues,Equation

According to the results of correlation analysis, the expression of CD147 was not associated with the age and clinical stage of cervical cancer patients (p>0.05), but had close correlations with the tumor size, invasion depth and lymphatic metastasis in cervical cancer patients (p<0.05) as shown in Table 1. The PCR results manifested that the messenger RNA (mRNA) expression of CD147 was reduced significantly in experimental group in comparison with that in normal group as shown in fig. 2, suggesting that the transfection was successful.

IJPS-transfection

Fig. 2: Cell transfection results (400×), (A): Normal group; (B): Control group and (C): Experimental group

Note: *p<0.05 vs. normal group




















Item Quantity n (%) CD147 χ2 p
Negative expression Positive expression
Age (year)       0.004 0.95
<50 42 (46.67) 12 30    
≥50 48 (53.33) 14 34    
Tumor size (cm)       9.097 0.003
<3 50 (55.56) 10 40    
≥3 40 (44.44) 16 24    
Clinical stage       0.163 0.686
Ia1-Ib1 47 (52.22) 13 29    
Ib2-IIa2 43 (47.78) 13 35    
Invasion depth       31.15 <0.001
Not into serosa 45 (50) 25 20    
Into serosa 45 (50) 1 44    
Lymph node metastasis       18.68 <0.001
No 44 (48.89) 22 22    
Yes 46 (51.11) 4 42    

Table 1: Correlations Cd147 Expression with Clinicopathological Parameters of Cervical Cancer Patients (N=120, %)

After EdU labeling, the percentage of EdU-positive cells was significantly lower in experimental group than that in normal group (p<0.05). However, there were no significant differences between normal group and control group (p>0.05) as shown in fig. 3.

IJPS-staining

Fig. 3: EdU staining results (400×), (A): Normal group; (B): Control group and (C): Experimental group

Note: *p<0.05 vs. normal group

As shown in the results of Transwell assay, experimental group exhibited significantly smaller numbers of migrating cells and cells passing through Matrigel than normal group (p<0.05). However, no significant differences were found between normal group and control group (p>0.05) as shown in fig. 4 and fig. 5.

IJPS-migration

Fig. 4: Cell migration results (400×), (A): Normal group; (B): Control group and (C): Experimental group

Note: *p<0.05 vs. normal group

IJPS-invasion

Fig. 5: Cell invasion results (400×), (A): Normal group; (B): Control group and (C): Experimental group

Note: *p<0.05 vs. normal group

The gene ID of CD147 is Basigin (BSG) and that of MCT1 is Solute Carrier Family 16 Member 1 (SLC16A1). It has been discovered through the GeneMANIA database (http://genemania.org/) that there are specific regulatory sites between CD147 and MCT1 as shown in fig. 6. It was revealed in the results of Western blotting that the protein expressions of CD147 and MCT1 declined significantly in experimental group compared with those in normal group, but they were not significantly different between normal group and control group (p>0.05) as shown in fig. 7. As a common gynecological malignant tumor, cervical cancer frequently occurs in females aged 20-55 y old, seriously threatening the health and life of women worldwide. Although the early screening technique for cervical cancer has been advanced with social development and lifestyle transformation, the age of onset of the disease is becoming younger and younger, and the number of patients who have not given birth to a child is increasing.

IJPS-regulatory

Fig. 6: Specific regulatory sites between CD147 and MCT1

IJPS-cd147

Fig. 7: Expressions of CD147 and MCT1 in cells, (A): Normal group; (B): Control group and (C): Experimental group

Note: *p<0.05 vs. normal group

In addition, the recurrence and metastasis rates are rising constantly, and the long-term survival rate of the majority of cervical cancer patients is unsatisfying[7]. The molecular mechanism of cervical cancer is very complicated and still under research, so seeking for potential molecular markers for progression of the disease and positively developing efficacious molecular targeted drugs are great challenges faced by clinicians.

CD147 is a type of transmembrane protein of about 58 kD located on the surface of cell membrane, which is expressed in various cells and tissues, such as thymocytes and epithelial cells in human body. Large quantities of studies have demonstrated that CD147 is lowly expressed in normal tissues or cells, but its expression is elevated in many malignant tumors, including urinary system tumor, ovarian cancer, liver cancer and neuroglioma. Therefore, CD147 is a hot spot of research on targeted therapy for malignant tumors. Fang et al.[8] studied and found that the expression of CD147 was increased in prostate cancer and the increased CD147 mediated the Wnt/β-catenin pathway to promote epithelial-mesenchymal transition of tumor cells and disease progression in patients. The study of Min et al.[9] revealed that in the case of renal cell carcinoma, inhibiting the expression elevation of CD147 could remarkably repress the energy metabolism of tumor cells and down-regulate the proliferative ability of cancer cells. Yin et al.[10] denoted that the expression of CD147 rose in esophageal squamous cell carcinoma and the in vitro and in vivo proliferative and invasive abilities of tumor cells were obviously suppressed after the interference of CD147 expression. In the present study, the immunohistochemistry results showed that the expression of CD147 was increased in cervical cancer tissues, accompanied by progression of the disease. Besides, the results of correlation analysis indicated that CD147 expression was highly associated with the tumor size, invasion depth and lymphatic metastasis in cervical cancer patients, suggesting that it is of definite clinical significance to research the expression of CD147 in the case of cervical cancer. Finally, after CD147 expression was silenced by shRNA technique, the cell growth was restrained and the proliferative, invasive and migratory abilities were down-regulated in experimental group, illustrating the potential significance of this target in the clinical treatment of cervical cancer.

The progression of cervical cancer is a slow multistage process involving multiple genes and controlled by various molecules. Specifically, cervical squamous epithelial cells in patients are proliferating abnormally and then activated for differentiation under the stimulation of carcinogenic factors, thereby forming atypical hyperplasia of cervical epithelium (cancer in situ). The synergistic effects of many genes facilitate the disease progression, which gradually develops into squamous intraepithelial lesion, invasive cancer and malignant metastasis to lymph, colon and other target organs, thus endangering patients’ lives[11]. MCT1 is a vital mediator for tumor cells to acquire energy through glycolysis. The expression of MCT1 is raised in such tumor tissues as neuroglioma, colon cancer and gastric cancer, and it has a close relationship with the prognosis of patients[12]. Logotheti et al.[13] researched and denoted that inhibiting the expression of MCT1 in bladder cancer could markedly restrain the metabolic reprogramming of tumor cells and the growth of cancer cells. Al-Mousawi et al.[14] reported in study that the down-regulated MCT1 expression in colon cancer HT-29 cells could distinctly reduce the energy needed for tumor cell invasion and malignant metastasis. In this study, it was found in the GeneMANIA database that CD147 and MCT1 had specific regulatory sites. Based on cell experiments, it was discovered that the expression of MCT1 declined evidently in experimental group after silencing CD147 expression.

In conclusion, the expression of CD147 is increased in cervical cancer tissues and repressing CD147 expression can prominently decrease the proliferative, invasive and migratory abilities of tumor cells in vitro and in vivo. However, whether CD147 can be made into a specific probe for the diagnosis of cervical cancer progression needs to be deeply investigated.

Conflict of interests:

The authors declared no conflict of interests.

References



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Dual Organic Modifier, Solid Core Technology and Quality by Design Based Tripartite Synergistic Model for Development and Validation of Stability Indicating Method by Reverse Phase-High Performance Liquid Chromatography for Assay and Impurities of Etoricoxib Tablets

June 23, 2022 Published by


S. B. Kolla1*, M. R. Vallabhaneni, S. B. Puttagunta2 and V. V. S. M. Chakka1

Department of Chemistry, Vignan’s Foundation for Science, Technology and Research University, Vadlamudi, Guntur, Andhra Pradesh 522213, 1Department of Analytical Research and Development, Formulations, GVK Biosciences Private Limited, Mallapur, Hyderabad, Telangana 500076, 2Department of Pharmacy, Vignan College of Pharmacy, Jawaharlal Nehru Technological University-Kakinada, Vadlamudi, Guntur, Andhra Pradesh 522213, India

*Corresponding Author:

S. B. Kolla

Department of Analytical Research and Development, Formulations, GVK Biosciences Private Limited, Mallapur, Hyderabad, Telangana 500076

E-mail: [email protected]







Date of Received 09 April 2021
Date of Revision 13 July 2021
Date of Acceptance 02 June 2022
Indian J Pharm Sci 2022;84(3):683-702  

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.964

Abstract

Tripartite synergistic model of solid core technology, dual organic modifiers and combined mixture design was implemented to achieve combined assay and related substances method by reverse phasehigh performance liquid chromatography with short run time, enhanced sensitivity and improved resolution between multiple impurity peaks. pH of mobile phase, ternary mobile phase composition and high performance liquid chromatography column temperature are experimented as variable parameters. Acetonitrile and isopropyl alcohol mixture was experimented as dual organic modifier. Special focus was given to detailed methodology of dealing with elution order changes by assigning negative sign for resolution. Separation of etoricoxib and related impurities was evaluated as a case study to prove this concept. The method was developed with Ascentis® Express C18, 150×4.6 mm, 2.7 μ column. Mobile phase comprised of buffer (0.1 % v/v ortho phosphoric acid, pH 3.6), acetonitrile and isopropyl alcohol (65.3:29:5.7 v/v) with a flow rate of 1.0 ml/min and ultraviolet detection at 285 nm. Forced degradation studies revealed that the method was stability indicating, suitable for both assay and impurities of drug product. The recoveries for impurities and assay were found to be in the range of 94.0 %-111.0 % and 97.9 %-101.8 %, respectively. Linearity was established for impurities and assay in the range of 0.25-2.0 μg/ml and 125-750 μg/ml, respectively. The method was validated as per international conference on harmonisation guidelines. The method can be successfully employed for determination of assay and impurities of etoricoxib in bulk drugs and formulations.

Keywords

Design of experiments, solid core technology, dual organic modifier, assay and impurities method, impurity profile swap, etoricoxib, stability indicating, high performance liquid chromatography

Pharmaceutical dosage forms are to be tested for critical quality attributes like assay and Related Substances (RS) to ascertain potency and purity[1,2]. Most of the assay and RS methods are developed by Reverse Phase-High Performance Liquid Chromatography (RP-HPLC) technique with either isocratic or gradient elution of mobile phase depending on the number of analytes to be analysed. RP-HPLC methods for assay have an average run time of 15 min and above and may not be suitable for RS, but not in all cases[3]. If multiple analytes are difficult to separate by isocratic method, a gradient change of organic modifiers such as buffer and organic solvent are applied over a constant time for impurities methods along with Ultra-High Performance Liquid-Chromatography (UHPLC) to achieve shorter run times[4-7]. Gradient elution mode of RP-HPLC employs aqueous buffer solvent in one channel and organic solvent in another channel at standard flow rate but with varying compositions over time to generate a gradient program for elution. Aqueous solvents used for the mobile phase are usually buffered solvent with addition of inorganic salt as an organic modifier to impart sufficient buffer strength and maintain constant pH. Acetonitrile (ACN) and Methanol (MeOH) are often the organic solvents of choice as they can solubilize many small molecules, offers low back pressure and miscible with most of the aqueous buffers used in RP-HPLC[8]. Solvent elution strength order for commonly used solvents is MeOH<ACN<Isopropyl Alcohol (IPA)[9]. Selectivity (alpha (α)) and retention factor (k prime as capacity factor) can be altered with introduction of a second organic modifier to compensate the part of volume of first organic modifier, so that same elution strength of mobile phase is maintained with ternary mobile phase and MeOH, ethanol and IPA are common solvents of choice in RP-HPLC[10,11]. IPA is the solvent of choice to consider as second organic modifier over MeOH and ethanol because MeOH exerts high back pressure and the solvent strength is less, hence required in large proportions in mobile phase. Ethanol on the other hand is having high elution strength but is highly volatile and is also a controlled category solvent and hence is not of primary choice as mobile phase component. IPA as organic modifier is well conversed in the past in separation of complex analytical resolutions in micellar liquid chromatography[12] in glycol-peptide enrichment[13] with Hydrophilic Interaction Liquid Chromatography (HILIC) columns, in aqueous normal phase chromatography[14] and as washing solvent in column regeneration with ACN at 25 % volume[15].

Solvochromatic properties like elution strength (εº), Polarity (P), dipole character (Π), acidity (α) and basicity (beta (β)) play an important role in selectivity of analytes. IPA, because of high viscosity (2.04 mPa) and higher elution strength (3.9 polarity) is not a primary choice in chromatography as sole organic modifier[8]. Many articles in past discussed ethanol as substitution solvent to ACN and MeOH[16]. IPA has two main drawbacks which can hinder its use in HPLC. The first drawback is Ultraviolet (UV) cut off (205 nm) and second, high viscosity in combination with water, which exerts high back pressure on to HPLC columns[12]. IPA is not widely used as solo organic modifier because of high elution strength which causes the analytes to merge and distort increasing the band broadening of peaks[17]. IPA can be experimented as second organic modifier as elution strength of IPA (8.3) is much higher than ACN (3.1), MeOH (1.0) and Tetrahydrofuran (THF) (3.7). Acidity and basicity of IPA are almost same (0.36 and 0.40), which makes this solvent suitable for separation of both acidic and basic molecules[8]. Because of all above reasons, IPA scores over MeOH, ethanol and THF as co-solvent of choice. THF also has higher elution strength compared to other common solvent used in RP-HPLC, however, THF is relatively unstable and is prone to oxidation. Additives to stabilize the THF solvent are also incompatible with several inorganic salts of mobile phase. THF also has significant absorptive properties in UV range and is not a solvent of choice when higher sensitivities are expected at lower sample concentration. Loss of sensitivity requires the analytes to be injected at higher concentrations, which further will cause band broadening. Moreover, miscibility of THF with ACN and buffer solvents is less compared to IPA. Hence THF is not considered as tertiary solvent. Several literature articles discuss use of IPA as second organic modifier and the elution strength is two to three times higher than MeOH, ethanol and THF. Higher elution strength entails less volume of solvent requirement to elute late eluting/ low polar analytes. MeOH has less elution strength and is required in large volumes to compensate part of primary organic modifier, which in turn will increase the viscosity of mobile phase excreting high back pressure. Ethanol on the other hand is highly volatile and is also a controlled substance and hence is not of primary choice. A blend of ACN and IPA are used in the past to resolve co-elution of non-polar analytes[18]. IPA is also studied as an alternate organic modifier during ACN shortage[19] and also as green chromatography solvent, because it is environmentally benign in nature[20].

Current trends in HPLC method development use solid core technology columns to arrive at benefits of UHPLC technologies[21,22]. These particles operate at elevated mobile phase linear velocities to affect dramatic increase in the efficiency. Solid core particles are 2.7 μm in diameter with a 1.7 μm solid core and 0.5 μm porous shell that results in superior mass transfer kinetics and high efficiency in chromatography[23]. Superficially porous particle columns offer reduced plate height (h), reduced diffusion length which in turn improves plate number (n); enhancing selectivity for multiple analytes with improved performance for separation goals and also offer reduced back pressure that will allow to accommodate high viscosity organic modifiers like IPA[24-26]. Design of Experiments (DoE) technique employs randomization of selected variables with statistical algorithms to plan controlled experiments. When multiple responses are to be optimized involving both numeric and mixture variables, combined mixture design is used to determine the optimum combinations of factors, that deliver a desired response by using a minimum number of experimental runs[27-29].

Etoricoxib (ETO) is a Cyclooxygenase-2 (COX-2) selective inhibitor and crystalline in nature with acid dissociation constant (pKa) value of 4.5 and having chromophores[30]. ETO is soluble in organic solvents like ACN, MeOH and insoluble in water and is available in tablet dosage form. Many studies are reported in literature for assay and impurities for ETO[31-36]. Yet there is no study, which separates all impurities in isocratic method with runtime of less than 15 min. One of the literature reported method discussed the separation of about 13 impurities for ETO Active Pharmaceutical Ingredient (API), with gradient elution and also specified elution order reversal is observed with change in pH from 2.5 to 3.7[36]. Majority of the literature reported methods has pH of mobile phase around 3.0 to 5.0 except for one article which operates at pH 7.0. However, the current study focused on separation of 5 major impurities which are relevant for the tablet dosage form. Moreover, no supporting literature exists on the application of combined benefits of ternary isocratic mobile phase system, solid core technology and DoE to achieve fast and efficient method for assay and RS. In this study, tripartite concepts of solid core technology columns combining the benefits of dual organic modifiers in mobile phase and DoE concepts of combined mixture design are implemented to achieve sharp and well resolved peaks in combined assay and impurities method for ETO. The approach presented here was successfully implemented to reduce 45 min runtime of a literature reported gradient method to a 15 min isocratic method.

Materials and Methods

Materials:

ETO of pharmaceutical grade was procured from Inogent Laboratories (A GVK Bio Company, Hyderabad, India). ETO Immediate Release (IR) tablets were supplied by GVK Biosciences (FDS, Hyderabad, India). Impurity-A (5-{5-chloro-3-[4-(methylsulfonyl) phenyl] pyridin-2-yl}-2-methylpyridine 1-oxide), Impurity-B (5-chloro-3-[4-(methylsulfonyl)phenyl]- 2,3′-bipyridine), Impurity-C (1-(6-Methylpyridin- 3-yl)-2-[4-(methyl sulfonyl)phenyl]ethanone), Impurity-D (6′-methyl-3-[4-(methylsulfonyl)phenyl]- 2,3’bipyridine (or) 3-(4-methyl sulfonyl)phenyl-2- (2-methyl-5-pyridinyl)-pyridine)]) and Impurity-E (6″-dimethyl-3′,5′-bis[4-(methylsulfonyl)phenyl]- 3,2′:6′,3″-terpyridine) (fig. 1) were procured from Glenmark pharmaceuticals (Gujarat, India) and Mylan Laboratories (Hyderabad, India). Ortho Phosphoric Acid (OPA) (analytical grade), IPA (HPLC grade), Sodium hydroxide (NaOH) pellets (Emplura® grade), Hydrochloric acid (HCl) (Laboratory Reagent (LR) grade), ACN (HPLC grade) were purchased from Merck Ltd (Mumbai, India). Buffer salts and all other chemicals were of Emplura® grade from Merck India. Ultra-pure water was obtained from a Milli-Q® purification system (Millipore, Mumbai, India).

IJPS-structures

Fig. 1: Structures of the compounds used in the study

Instrumentation and software:

HPLC studies were carried out with Agilent™ instrument (Model 1200 Series), which was equipped with a photo diode array detector. The software used to operate the instrument and data processing was Waters ™ Empower® software. Other equipment’s used were micro balance (ME 5, Sartorius, Switzerland), analytical balance (XB220A, Precisa Gravimetric AG, Dietikon, Switzerland) and magnetic stirrer (Model Remi equipment’s private limited). Pipettes and remaining glassware were made of borosil. 0.45 μm Polyvinylidene Fluoride (PVDF) filters were used for the filtration of samples and mobile phase. Samples were sonicated to dissolve by using an ultra sonicator (Model 13L300H, S.V Scientific, Bangalore, India). pH was observed by pH/ion analyser (Model LP139SA, model Polmon, Bangalore, India). Design-Expert® version-10 software was used during DoE studies to generate experimental designs and to analyse the obtained responses.

Chromatographic conditions and sample preparation:

The separation was achieved using Ascentis® Express C18 (150×4.6 mm, 2.7 μm) column and UV detector at 285 nm. Mobile phase buffer is OPA at a concentration of 0.1 % v/v and pH for initial trails is selected as 3.2, which is beyond the range of ±1 of pKa of ETO i.e. 4.5. Buffer pH across the selected range is adjusted with dilute phosphoric acid (30 %) or with dilute NaOH solution (0.1 M). The mobile phase consisted of OPA buffer, ACN and IPA in different proportions as suggested by experimental design at isocratic flow rate of 1.0 ml/min. Individual impurity solutions were prepared at 100 μg/ml concentration and all impurity mixture solutions were spiked to ETO (250 μg/ml) at a concentration of 1.0 μg/ml. Buffer, ACN and IPA in the ratio of 60:25:15 v/v is used as diluent. Final validated HPLC method for the separation of ETO in tablet dosage form for estimation of process and degradant impurities was performed on Ascentis® Express C18 (150×4.6 mm, 2.7 μm) column. The flow rate was 1.0 ml/min and column temperature was 35°. The ternary mobile phase consisted of component A, an aqueous solution of OPA at a concentration of 0.1 % v/v and pH for initial trails is selected as 3.6, component B, ACN and component C, IPA in the ratio of 70:23.5:6.5 v/v. Sample of ETO were dissolved in buffer, ACN and IPA in the ratio of 60:25:15 v/v is used as diluent at concentration of 500 μg/ml. Detection was by UV at 285 nm and UV spectra was collected by photodiode array detector for all forced degradation samples across the range of 200 to 400 nm.

Quality by Design (QbD) concepts and analytical target profile:

Entire method development programme was executed by aligning QbD principles to develop a quality method to consistently deliver indented results. QbD based method development employs more systematic approach to method development by including prior knowledge, results of One Factor at a Time (OFAT) studies, use of quality risk assessment and knowledge management throughout lifecycle management. QbD approach to development involves defining analytical target profile, identification of critical method attributes and evaluation of critical method parameters with quality risk management principles[37-39]. All the primary objectives of the method were methodically evaluated through analytical target profile. Table 1 presents a brief overview of analytical target profile. Combined assay and RS method, ternary mobile phase, solid core technology HPLC column and isocratic separation with targeted short run time were considered as the critical method quality attributes and were built in by design. Further to develop the method by QbD principle a structured, organized technique of determining relationship between factors and responses is adopted by means of experimental design.















ATP element Target Justification
Assay and RS method Measurement of potency and purity To monitor the drug assay and related substance characteristics of drug product
Design and mode of method RP-HPLC, isocratic elution and ternary mobile phase with ACN, IPA as dual organic modifier To target run time of less than 15 min with ternary mobile phase system to offer improved selectivity. To evaluate efficiency of secondary organic modifier. To separate multiple impurities with short run time
Mode of detection and stationary phase UV, Solid core technology with C18 stationary phase The molecule has chromophore. Solid core technology offers superior porosity, resolution and sensitivity. C18 stationary phase is used in multiple literature references
Analytical method validation criteria    
Specificity Placebo interference should not be observed As the method is for tablet dosage form, results shall not be affected by presence of excipient matrix
Selectivity To separate impurities and degradants To prove peak purity and stability indicating nature
Precision To establish repeatability As per requirements of International Conference on Harmonisation (ICH) Q2R1 guidelines and to obtain consistent and reproducible results
Accuracy To offer accurate results The % recovery of ETO and impurities in tablet matrix shall meet predefined criteria
Linearity Establish linearity across concentration range Linearity at different concentration levels should be obtained
Filter interference Prove filter compatibility To choose suitable filter membrane
Robustness Method shall be reliable to critical variable of the method Results shall not be affected by deliberate changes and analytical solutions shall be stable for known time and temperature conditions

Table 1: Analytical Target Profile (ATP)

Chromatography optimization:

It was observed that all impurities are structurally related with minor differences in specific functional groups for each impurity. Impurity A to impurity D is differing in structure by either addition or deletion of chlorine, methyl and oxygen group, whereas impurity E is a dimer of ETO. Impurity-A was formed by attacking oxygen group at nitrogen in methyl pyridine ring, impurity-B was formed by desmethylation, Impurity-C was formed by oxidation of carbon in chloro pyridine ring, impurity-D by removal of chlorine group and impurity-E with addition of methyl pyridine as an additional molecule. Preliminary screening experiments were conducted with OFAT approach by studying independently, the effect of different variables namely mobile phase composition, different proportions of organic modifiers (ACN and IPA) and pH of mobile phase. Different types of columns were evaluated to arrive at a single isocratic method for elution of all impurities. Zorbax Eclipse C18 and symmetry C18 (250×4.6 mm, 5 μ) columns are selected based on literature review, for all initial experiments, as most of the literature reported methods discussed use C18 based columns. Later the column is changed to Ascentis® Express C18 (150×4.6 mm, 2.7 μ) to integrate benefits of solid core technology. In each case the focus was to arrive at good resolution between all impurities and to have good peak shape with tailing factor around 1.0.

By the process of QbD, solid core technology columns are introduced to have sharp and symmetric peaks. Introduction of solid core technology columns helped us to reduce the peak width for all analytes which further enhanced the resolution. All preliminary experiments of mobile phase composition optimization were carried out with 0.1 % v/v OPA buffer adjusted to pH 3.2. Further trials were performed at mobile phase composition of 60 % buffer, 25 % ACN and 15 % IPA for influence of pH at 3.0, 3.2, 4.0 and 6.0. Peak symmetries were achieved by the method design with selection of solid core technology columns. All the observed values for United States Pharmacopeia (USP) tailing factor were observed around 1.1 and peaks were relatively sharp. It was observed that selectivity of the peaks was varying drastically with variation in chromatography conditions and hence theoretical plate count was considered as one of the responses. Objective during OFAT studies was confined to minimum possible retention time of ETO and maximum possible resolution between all analytes. All sample solutions of ETO are prepared by dissolving the crushed tablet powder in diluent. Diluent selection is arrived based on observed solubility of the molecule. ETO is insoluble in water but highly soluble in all organic solvents. The tablet formulation has calcium dihydrogen phosphate, magnesium stearate, microcrystalline cellulose and croscarmellose sodium as excipients. Majority of the excipients except for microcrystalline cellulose are soluble in water. Hence a combination of water and ACN in equal ratio is selected for extraction of drug from tablet dosage form. Later the diluent is modified to buffer, ACN and IPA in the ratio of 60:25:15 v/v to maintain solvent similarities with that of the mobile phase. A combined, split plot, D-optimal design was employed to develop combined assay and impurities method with 5 factors and 12 responses. The ranges for variables were selected with the knowledge gained from OFAT experiments. The variables are mixture components along with individual numerical variables with some variables treated as hard-to-change factors. To experiment both mixtures and individual components, a combined mixture design is the suitable option. pH is selected as hard-to-change factor and hence split-plot design is selected. Split-plot designs are useful to control the number of times a hard-tochange factor is randomised and still provide adequate power to the design model. D-optimal designs are more suitable when the design has mixture of variables which are mixtures, continuous, discrete and hard-to-change components and still offer better randomization with block effects. Mobile phase composition with different proportions of buffer, ACN and IPA were selected as mixture variables. pH of buffer and column temperature were selected as numeric variable with discrete as subtype for column temperature. The design consisted of 41 experiments, including the combinations of factors at different levels. The ranges studied for the five factors were 50.0 %-70.0 % of buffer, 5.0 %-40.0 % of ACN, 0 %-25.0 % of IPA, 3.0-5.5 for the buffer pH and 25°-35° for the column temperature. pH of mobile phase was selected as hard-to-change factor in order to limit variation of this factor to 3 different pre-specified levels (3.0, 4.3 and 5.5) with 4.3 selected as center point to deal with curvature effect, if any. The order of experiments was randomized to minimize systematic error and the experiments were divided into five blocks. A composite sample with mixture of all impurities and ETO was employed in the optimization of experiments. Experimental runs as obtained from DoE software are presented in Table 2.














































Run Variable Response
A B C D E R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12
1 50 40 10 5.5 25 0 0 7.17 -2.04 5.13 4.1 6.14 4.1 16846 5.13 0 5.13
2 60 40 0 5.5 35 0 -2.2 15.4 2.86 18.22 7.08 4.22 6.25 25131 16 2.23 16
3 70 5 25 5.5 25 0.74 -2.7 13.2 -4.17 9 6.81 10.98 5.88 13726 7.07 1.93 6.33
4 70 30 0 5.5 30 0 -2.1 27 22.23 49.19 13.84 -8.39 16.4 29373 47.1 2.09 47.1
5 50 25 25 5.5 25 0 0 4.41 -3.3 1.11 2.43 5.73 3.12 12573 1.11 0 1.11
6 70 17 13 5.5 35 4.05 0 20.2 7.49 27.65 11.49 4 10.3 25828 31.7 -4.05 27.7
7 50 25 25 5.5 35 0 0 4.44 -3.15 1.29 2.39 5.54 3 14565 1.29 0 1.29
8 70 30 0 5.5 25 0 -3.8 35.3 23.7 59.04 13.92 -9.78 17.5 30084 55.3 -3.76 55.3
9 50 40 10 3 35 -2.15 2.15 7.13 -7.13 0 0 7.13 3.25 15206 0 0 2.15
10 50 25 25 3 30 0 -1.3 4.54 -5.86 -1.32 0 5.86 2.78 10801 0 -1.32 0
11 70 30 0 3 25 -12.6 17 20.2 -25.93 -5.74 -11.6 14.36 6.37 21086 -1.3 -4.41 11.3
12 70 5 25 3 35 -1.36 4.46 7.16 -10.26 -3.1 -2.2 10.26 3.63 12632 0 -3.1 1.36
13 50 40 10 3 25 -2.5 2.5 5.94 -5.94 0 0 5.94 3.27 13868 0 0 2.5
14 62 24 15 3 30 -2.94 5.25 10.7 -12.97 -2.31 -2.6 10.37 4.12 15304 0 -2.31 2.94
15 60 40 0 3 35 -5.24 6.57 13.1 -14.39 -1.33 -3.48 10.91 4.08 18134 0 -1.33 5.24
16 60 21 19 3 25 -1.68 4.31 7.83 -10.46 -2.63 -1.84 8.62 3.51 12726 0 -2.63 1.68
17 50 25 25 4.3 35 0 0 4.39 -3.43 0.96 2.33 5.76 3.01 13882 0.96 0 0.96
18 50 40 10 4.3 25 0 0 8.6 -2.94 5.66 3.88 6.82 3.95 18746 5.66 0 5.66
19 70 19 11 4.3 30 2.29 3.18 24.8 6.2 30.95 8.9 2.7 11.8 23079 36.4 -5.47 34.1
20 70 5 25 4.3 25 1.25 2.08 11.5 -6.01 5.48 5.74 11.75 5.7 13246 8.81 -3.33 7.56
21 62 25 14 4.3 35 1.21 0.88 13.2 -1.33 11.83 6.99 8.32 5.78 21587 13.9 -2.09 12.7
22 60 40 0 4.3 25 -2.46 -1.6 19.1 0 19.07 5.97 5.97 6.25 23967 15.1 4.01 17.5
23 70 30 0 4.3 35 -1.06 0 32.9 16.95 49.87 11.56 -5.39 15 28275 48.8 1.06 49.9
24 57 18 25 4.3 30 0.55 0 6.14 -4.33 1.81 3.28 7.61 3.5 14617 2.36 -0.55 1.81
25 60 23 17 5.5 35 1.49 0 9.66 -2.17 7.49 6.07 8.24 4.84 18890 8.98 -1.49 7.49
26 62 24 14 5.5 25 1.68 0 9.62 0.36 9.98 5.25 4.89 6.52 19467 11.7 -1.68 9.98
27 60 40 0 5.5 25 -1.15 -3 20 2.61 22.61 7.4 4.79 6.39 26448 18.4 4.19 19.6
28 70 5 25 5.5 30 1.29 0.24 8.84 -4.95 3.89 6.3 11.25 5.18 15769 5.42 -1.53 4.13
29 70 30 0 5.5 35 1.31 -3.6 33.9 23.46 57.32 14.45 -9.01 15.9 31167 55 2.31 53.7
30 50 40 10 5.5 35 0 0 5.06 -2.13 4.93 4.31 6.44 3.84 19164 4.93 0 4.93
31 70 5 25 5.5 35 2.06 0 10.4 -4.79 5.61 6.9 11.69 5.2 17330 7.67 -2.06 5.61
32 50 40 10 5.5 30 0 0 7.26 -2.12 5.14 4.24 6.36 3.93 18923 5.14 0 5.14
33 60 40 0 3 30 -5.62 4.34 9.56 -8.53 1.03 -2.48 6.05 4.27 19493 -0.3 1.28 5.37
34 70 20 11 3 25 -7.33 16.6 15.3 -22.87 -7.57 -8.52 14.35 5.98 17562 1.65 -9.22 8.98
35 70 5 25 3 25 -0.94 5.24 7.77 -13.95 -6.18 -3.37 10.58 3.47 10798 -1.6 -4.66 -0.58
36 70 30 0 3 35 -12.5 15.9 21.3 -22.7 -1.42 -9.2 13.5 6.69 22906 2.01 -3.43 14.5
37 60 40 0 3 25 -7.27 5.71 13.6 -15.37 -1.79 -5.13 10.24 3.91 16759 -3.4 1.56 3.92
38 70 5 25 3 30 -1.61 5.65 7.84 -12.7 -4.86 -2.79 9.91 3.45 11642 -0.1 -4.04 0.79
39 62 24 14 3 35 -3.1 6.12 10.5 -12.58 -2.04 -1.85 10.73 4.08 15908 0.98 -3.02 4.08
40 50 25 25 3 35 0 0.73 4.18 -4.91 -0.73 0 4.91 2.78 11036 0 -0.73 0
41 50 25 25 3 25 0 1.26 4.31 -5.57 -1.26 0 5.57 2.8 10969 0 -1.26 0

Table 2: DoE for screening

As shown in Table 2, A is buffer concentration; B is ACN concentration; C is IPA concentration; D is pH of mobile phase; E is column oven temperature; R1 is resolution between impurity-C and impurity-D; R2 is resolution between impurity-D and impurity-A; R3 is resolution between impurity-A and impurity-B; R4 is resolution between impurity-B and impurity-E; R5 is resolution between impurity-A and impurity-E; R6 is resolution between impurity-B and ETO; R7 is resolution between impurity-E and ETO; R8 is retention time of ETO; R9 is plate count of ETO; R10 is resolution between impurity-C and impurity-E; R11 is resolution between impurity-A and impurity-C and R12 is resolution between impurity-D and impurity-E.

Selection of responses and elution pattern:

Major focus was set on resolution between all the six analytes (five impurities and ETO). With six analytes, there is a likelihood of five different responses as resolution criteria between adjacent peaks. However, it is pragmatic that elution order swap is observed with variation in pH and IPA concentration. As the entire method optimisation is performed by DoE, it is essential to capture all possible responses and feed the data to design for evaluation. Post execution of DoE, all the responses are thoroughly evaluated for elution order change, impact of variables on responses and then 12 responses were arrived by fixing a standard elution order. A reference elution pattern (Imp-C, Imp-D, Imp-A, Imp-E, ETO and Imp-B) observed with literature reported gradient method was fixed and for any different elution pattern other than the reference, a negative value was assigned to resolution between closely eluting peaks. Fig. 2 represents reference elution order. Therefore the twelve responses were resolution between impurity-C and impurity-D (R1), resolution between impurity-D and impurity-A (R2), resolution between impurity-A and impurity-B (R3), resolution between impurity-B and impurity-E (R4), resolution between impurity-A and impurity-E (R5), resolution between impurity-B and ETO (R6), resolution between impurity-E and ETO (R7), retention time of ETO (R8), plate count of ETO (R9), resolution between impurity-C and impurity-E (R10), resolution between impurity-A and impurity-C (R11) and resolution between impurity-D and impurity-E (R12).

IJPS-elution

Fig. 2: Reference elution pattern

Note: (1) Impurity-C; (2) Impurity-D; (3) Impurity-A; (4) Impurity-E; (5) ETO and (6) Impurity-B

Results and Discussion

Several trials were conducted by OFAT approach with ACN as solo organic modifier. Initial screening experiments were performed with gradient run of mobile phase by keeping pH 3.2 buffer as mobile phase A and ACN as mobile phase B. Quite a few experiments were conducted to predict the optimum concentration of ACN required for the isocratic experimentation. After testing several combinations, the gradient programme for final chromatography with buffer and ACN in mobile phase was kept as 78 % of buffer and 22 % of ACN at start of gradient with a linear change of ACN to 60 %, over 40 min. The mobile phase was then re-equilibrated to original condition over a period of 10 min. With the above conditions, ETO is eluting at 18 min and at an organic concentration of above 40 %. An exemplary chromatogram with buffer and ACN gradient programme as reference elution pattern is presented in fig. 2. Based on above experiments mobile phase composition for isocratic elution was studied at 60 %-70 % of buffer, 5 %-25 % of ACN and 15 %-25 % of IPA. It was observed that 40 % and above volume of ACN is required to elute all the impurities within 15 min. However, most of the analyte peaks are co-eluting and are merged with each other or with ETO peak. As a next step, isocratic runs were conducted with both ACN and IPA in the mobile phase with solvent ratio ranging from 5 %-25 % v/v, keeping buffer concentration constant at 70 % v/v. At all the combinations, observed retention time for ETO is 3-7 min and elution pattern change is observed for multiple impurities with poor resolution between closely eluting analytes. Evaluation of pH influence in the range of 3.0-6.0, by OFAT approach, revealed that elution pattern change is predominant with change in pH of mobile phase. As isocratic elution with OFAT trials is not conclusive. DoE as a tool for method optimization is selected.

Design adequacy was evaluated with Analysis of Variance (ANOVA) statistical parameters such as p-value, R-squared and adjusted R-squared (Table 3), p-values were observed below 0.01 which signifies adequacy of model. R-squared and adjusted R-squared values were above 0.90 for most responses indicating good correlation between variables and responses.

















Response p value R-Squared Adjusted R-squared
Subplot Linear mixture
R1 <0.0001 <0.0001 0.98 0.97
R2 <0.0001 0.0013 0.97 0.95
R3 0.0004 0.0174 1 0.94
R4 <0.0001 <0.0001 0.97 0.96
R5 <0.0001 <0.0001 0.98 0.97
R6 <0.0001 0.0046 1 0.99
R7 <0.0001 <0.0001 0.94 0.91
R8 <0.0001 <0.0001 0.98 0.97
R9 <0.0001 <0.0001 0.99 0.98
R10 <0.0001 <0.0001 0.98 0.98
R11 <0.0001 <0.0001 0.82 0.74
R12 <0.0001 <0.0001 0.99 0.98

Table 3: ANOVA test results

In Table 3, R1 is resolution between impurity-C and impurity-D; R2 is resolution between impurity-D and impurity-A; R3 is resolution between impurity-A and impurity-B; Ris resolution between impurity-B and impurity-E; R5 is resolution between impurity-A and impurity-E; R6 is resolution between impurity-B and ETO; R7 is resolution between impurity-E and ETO; R8 is retention time of ETO; R9 is plate count of ETO; R10 is resolution between impurity-C and impurity-E; R11 is resolution between impurity-A and impurity-C and R12 is resolution between impurity-D and impurity-E, p-value shall be less than 0.001 and difference between R-squared and adjusted R-squared values shall be less than 2 to indicate significance model to evaluated response.

Model graphs evaluation revealed that all the responses were fitting into four different clusters. First cluster comprises R1 and R7 where resolution was improving with increase in volume of buffer and IPA from mid to highest value (fig. 3a). Second cluster was represented by R2 alone and all variables were positive at mid values of studied ranges and resolution was both in negative as well positive value, indicating shift in elution order (fig. 3b). Responses R3, R4, R5, R6, R8, R9, R10 and R12 fit into third cluster with positive response to increase in buffer and IPA concentration and minimal effect of ACN composition (fig. 3c). All the responses in cluster three were having positive response with an exception of R4 which exhibits elution order change. R11 alone was in fourth cluster and resolution was with negative value with increase in buffer and on positive value with increase in organic modifiers (fig. 3d).

IJPS-mobile

Fig. 3: Effect of mobile phase components on responses

Note: (a) R1; (b) R2; (c) R3 and (d) R11. X-axis represents coded values of variable with effect of increase and decrease and Y-axis represents resolution between impurities

As an outcome of trace plots evaluation, it was found that R1, R2, R4 and R11 were exhibiting elution pattern change and were sensitive to both buffer and IPA concentration. Readers are suggested to make a note that archetypal plot for each cluster is presented for enhanced debate, as all other plots look analogous in pattern. Evaluation of independent variables (pH and column temperature) was exercised with perturbation plots. Column temperature was having negligible impact on all responses and was always of positive impact. pH of mobile phase had substantial influence on all responses with both positive and negative impact (selected range of pH is around reported pKa of ETO 4.5). Decrease in pH of mobile phase was refining resolution with negative values for R1, R4 and R11 responses (fig. 4a) and with positive value for R2 (fig. 4b). Any pH was found to be acceptable for remaining responses (fig. 4c). R5 and R6 are favoring mid to high pH, however the resolution was optimal in most instances.

IJPS-temperature

Fig. 4: Effect of pH of mobile phase and column temperature on responses

Note: (a) R1; (b) R2 and (c) R3. X-axis represents coded values of variable with effect of increase and decrease and Y-axis represents resolution between impurities

Further evaluation was deliberated with three Dimensional (3D) surface graphs to study interaction effects with focus on pH and concentration of buffer, IPA in mobile phase. Interaction effect was profoundly seen during evaluation of responses with 3D surface graphs. All responses were following majorly three different patterns. R1 is exhibiting good resolution at lower pH and higher buffer concentration and values were negative (fig. 5a). R4, R5, R6, R8, R10 and R12 were exhibiting mixed effects with both negative and positive values (fig. 5b) where resolution was negative with low pH and low IPA concentration and positive with increase in pH and IPA concentration. R2, R3, R7, R9 and R11 were not showing much of interaction effects and surface plot was mostly flat with an exemption of R2, displaying edge of failure effect at extreme low of pH and IPA concentration (fig. 6a) and R7 displaying curvature effect (fig. 6b) at mid-range of mobile phase composition.

IJPS-interaction

Fig. 5: 3D plots for evaluation of interaction effects

Note: (a) R1 and (b) R4. X-axis, Y-axis represents range of variable, Z-axis represents resolution between impurities and inclination of graph surface indicates interaction effect

IJPS-curvature

Fig. 6: 3D plots for evaluation of curvature effects

Note: (a) R2 and (b) R7, X-axis, Y-axis represents range of variable and Z-axis represents resolution between impurities, surface curvature in the graph indicates interaction effect and impurity profile swap

Elution pattern changes were depicted with representative chromatograms at different pH and mobile phase composition (fig. 7). Buffer at pH 3.0 was good for reduction in the retention time of all analyte peaks (below 5 min), with less than 60 % buffer concentration all peaks were merging (fig. 7a). With increase in buffer concentration above 60 %, keeping IPA concentration above 10 %, most of the peaks were separated except dimer and impurity-C (fig. 7b). Comparative evaluation of chromatographs at pH 4.3 revealed importance of IPA and elution order changes. As can be seen in fig. 7c at pH 4.3 elution order for first three peaks in the chromatogram was impurity-C, impurity-D and impurity-A. If IPA is removed from mobile phase, the elution order changed to impurity-A, impurity-D and impurity-C (fig. 7d). A similar observation on role of IPA can be made at pH 5.0. With increase in IPA concentration the resolution and elution pattern between first three peaks was drastically effected (fig. 7e). Column temperature is having marginal impact on resolution whereas IPA composition at an optimum of 20 % is positive for retention properties (fig. 7f).

IJPS-chromatogram

Fig. 7: Representative chromatograms obtained during experimental design

As shown in fig. 7 chromatographic conditions used are (a) pH 3.0 buffer/ACN/IPA 50/40/10 (v/v), temperature 35° (b) pH 3.0 buffer/ACN/IPA 62/24/14 (v/v), temperature 30° (c) pH 4.3 buffer/ACN/IPA 70/19/11 (v/v), temperature 30° (d) pH 4.3 buffer/ACN/IPA 60/40/0 (v/v), temperature 35° (e) pH 5.5 buffer/ACN/ IPA 70/5/25 (v/v), temperature 25° (f) pH 5.3 buffer/ ACN/IPA 70/17/13 (v/v), temperature 35° . The peaks contained are represented as follows, (1) Impurity-A; (2) Impurity-B; (3) Impurity-C; (4) Impurity-D; (5) ETO and (6) Impurity-E

3D surface graph evaluation predicted that elution order change was predominant for R1, R2, R4, R5, R6, R7, R10 and R12 responses. Conversely low pH and low concentration of IPA was favorable for R1, R2, R4, R5, R6, R8 and R11, high pH and moderate concentration of IPA was favorable for R9, R3, R10 and R12, mid pH and moderate concentration of IPA was favorable for R7 and R9. Each response was fluctuating to different extremes of variable, majorly pH and IPA concentration, which were further critical for solution prediction and method operable region prediction becomes complicated as a result of this phenomenon[39-41]. Three elution patterns orderly, at pH 3.0 (impurity-D, impurity-C, dimer, impurity-A, ETO and impurity-B), at pH 4.3 (impurity-C, impurity-D, impurity-A, impurity-B, dimer and ETO) and pH 5.0 (impurity-C, impurity-D, impurity-A, dimer, impurity-B and ETO) were considered as reference for pivotal responses. Negative values were assigned to resolution between impurity-D and impurity-C, impurity-A and dimer, impurity-B and ETO and impurity-B and dimer with systematic elution pattern analysis with reference elution order as impurity-C, impurity-D, impurity-A, dimer and impurity-B followed by ETO. As most of the responses were having both negative and positive values as a result of elution order change, a novel approach of categorizing all observed responses against pH of mobile phase was experimented along with definitive remarks as basis for selection (Table 4). The article is mainly focused on dealing with impurity profile change with novel approach of response categorization, assigning negative values for resolution to deal with impurity profile change. Current research presented methodologies to deal with multiple responses to predict solutions/final chromatographic conditions from a complex separation goal, where multiple factors of the design are involved in interaction.


































Response* Result pH 5.5 pH 3.0 pH 4.3 Remarks
R1 – 8 4 2 pH 3.0 is favourable, Scattered effect at pH 5.5 and pH 4.3
+ve 6 0 4
-ve 2 13 2
R2 – 9 3 4 pH 3.0 is favourable, resolution not achieved at other pH
+ve 2 14 3
-ve 5 0 1
R3 – 1 0 0 Any pH is suitable with positive response
+ve 15 16 8
-ve 0 1 0
R4 – 0 0 1 pH 3.0 and pH 4.3 are favourable with negative response
+ve 7 2 2
-ve 9 15 5
R5 – 1 5 0 Positive response is desirable at pH 4.3 and 5.0
+ve 15 2 8
-ve 0 10 0
R6 – 0 6 0 pH 4.3 and 5.0 are favourable with positive response
+ve 16 0 8
-ve 0 11 0
R7 – 0 1 0 Positive response is observed at entire range of pH
+ve 13 10 7
-ve 3 0 1
R10 – 1 2 0 Mixed response at pH 3.0. Positive response at pH 4.3 and 5.0
+ve 15 6 8
-ve 0 9 0
R11 – 5 2 2 Negative response is favourable at pH 3.0 and 4.3
+ve 5 2 2
-ve 6 13 4
R12 – 0 3 0 Positive response is favourable at entire range of pH
+ve 16 13 8
-ve 0 1 0

Table 4: Influence of butter pH and categorisation of responses

Observed responses for each run were coded with positive (+ve), negative (-ve) and neutral (-, no resolution). Based on most populated approach of observed responses, ranges and signs, desired responses were selected. For example R1 has 13 negative values at pH 3.0, which indicates that the desired outcome shall be in negative value and accordingly a range of -50 to -3.0 as resolution criteria was given. It is noteworthy that R8 and R9 were not part of response categorization exercise as values for these responses are always positive. After thorough evaluation of responses final chromatographic conditions were predicted with the help of software. For this purpose, numerical optimization option of DoE software is used. The software is given specific ranges for all variable and desired ranges for responses. Table 5 explains ranges for all variables and responses for solution prediction as an outcome of above exercise. DoE model was able to predict several solutions as desired combination of variables to yield chromatography with resolution between all analytes. Predicted solution-2 with DoE software was able to cut down ACN concentration to 23 %, but the total run time was extended to 15 min (fig. 8a). Solution-6 was able to cut down ACN concentration to 29 % and has excessive benefit of total run time less than 10 min with all impurities well resolved from each other and ETO as well (fig. 8b). All the resolution values for the responses are compared against design predictions for the selected two solutions and observed that the experimental results are in close agreement with DoE predictions (Table 6). Thus, experimental design for the targeted purpose is said to be validated.





















Name Goalc Lower limit Upper limit Importanced
Buffera Is in range 50 70 3
ACNa Is in range 5 40 3
IPAa Is in range 0 25 3
pH Is in range 3 5.5 3
Column temperatureb Is in range 25 35 3
R1 Is in range -50 -3 2
R2 Maximize 2 50 1
R3 Maximize 2 35.34 1
R4 Is in range -50 -3 3
R5 Maximize 1.7 59.04 3
R6 Maximize 1.7 14.45 3
R7 Maximize 1.7 14.36 2
R8 Minimize 3 17.5 1
R9 Maximize 10798 31167 1
R10 Maximize 1.7 55.28 3
R11 Is in range -9.22 -3 3
R12 Maximize 1.7 55.28 2

Table 5: Constraints for optimisation and solutions prediction

IJPS-solutions

Fig. 8: Chromatograms of selected solutions

Note: Chromatographic conditions: (a) pH 3.6 buffer/ACN/IPA 70/23.5/6.5 (v/v), temperature 35°; (b) pH 3.6 buffer/ACN/IPA 65.3/29/5.7 (v/v), temperature 29°; (1) Impurity-A, (2) Impurity-B; (3) Impurity-C; (4) Impurity-D; (5) ETO and (6) Impurity-E

In Table 6, S2 represents experimental conditions of OPA buffer adjusted to pH 3.6, ACN and IPA as mobile phase in the ratio of 70:23.5:6.5 (v/v) with 35° column temperature; S6 represents experimental conditions of OPA buffer adjusted to pH 3.6, ACN and IPA as mobile phase in the ratio of 65.3:29:5.7 (v/v) with 29° column temperature; R1 is resolution between impurity-C and impurity-D; R2 is resolution between impurity-D and impurity-A; R3 is resolution between impurity-A and impurity-B; R4 is resolution between impurity-B and impurity-E impurity; R5 is resolution between impurity-A and impurity-E; R6 is resolution between impurity-B and ETO; R7 is resolution between impurity-E and ETO; R8 is retention time of ETO; R9 is plate count of ETO; R10 is resolution between impurity-C and impurity-E; R11 is resolution between impurity-A and impurity-C and R12 is resolution between impurity-D and impurity-E.

















Response S2a S6b
Predicted Observed Predicted Observed
R1 -3.2 -2.4 -3 -2.6
R2 8.0 7.5 5.1 3.19
R3 25.7 27.4 18.2 21.94
R4 -3.0 1.3 -4 -3.77
R5 21.0 28.7 15.5 19.28
R6 4.4 5.1 2.5 2.96
R7 6.2 3.8 6.8 4.14
R8 10.2 11.7 7.8 8.1
R9 23939 24704 21722 20815
R10 25.9 33.8 17.8 14.18
R11 -4.0 -5.1 -3 -2.22
R12 29.1 36.3 20.4 18.62

Table 6: Design validation for selected solutions

Specificity of the method was carried out by evaluating different kinds of interferences, i.e. those produced by blank, placebo, known impurities and degradation products. For the first case, diluent as blank solution is tested. For placebo and known impurity interference, mixture of all excipients and all known impurities spiked to sample were evaluated. All impurities are well separated from ETO peak and no extra peaks were observed at retention of impurities as well ETO from diluent and placebo preparations[42]. Stability indicating nature of the method for degradation products was proven with forced degradation studies at different hydrolytic, oxidation, thermal and photolytic stress conditions. Several permutation and combinations of heat and additive concentrations were tested to arrive at suitable degradation conditions that will yield degradation in the range of 2 % to 20 %. Photolytic stress was performed both at visible stress (white fluorescent light-1.2 million lux h.), UV stress (200 Wh/m2) and the product is stable to light exposure and this observation is in contrary to the literature reported method observation of degradation to photostability and yielding two major degradants. One of the probable reasons can be that the literature method is applicable for ETO API, whereas current study majorly focuses on RS method for ETO tablets[36]. It is possible that the excipients and coating material of the tablet are giving extra protection from light exposure and hence no degradation is seen, while heat stress is not able to generate significant degradants at 80° for about 2 d. It was observed that acid (1 N HCl at 60° for 30 min), base (1 N NaOH at 60° for 30 min) and peroxide stress (3 % Hydrogen peroxide (H2O2) for 30 min) conditions were not able to generate considerable degradation and ETO tablets were found to be stable to all degradation conditions. Peak purity and mass balance studies confirm noninterference of degradation products and purity index for all degradation samples is observed above 1.0.

Linearity solutions, i.e. Limit of Quantitation (LOQ), 50 %, 100 % and 150 % levels for impurities and 50 %, 100 % and 150 % levels for assay method were injected into HPLC and chromatograms were recorded. The regression line analysis shows linear relationship between concentration and area response of ETO and all known impurities. Relative Response Factor (RRF) of each impurity against ETO diluted standard is calculated by comparing observed slope of linearity graph. Results of linearity and RRF are presented in Table 7. Readers shall make a note that impurity-E is not considered for method validation studies, because of insufficient quantity.























  Level Area
Impurity-A Impurity-B Impurity-C Impurity-D ETO
LOQ (25 %) 3360 1668 1672 3454 3137
50 % 6558 3553 3307 6805 6540
100 % 13202 7041 6686 13697 12385
150 % 19805 10049 10046 20519 18565
200 % 26405 13491 13394 27390 24309
Slope 12964.3 5992.74 6155.4 13162.9 11708.2
Intercept 15.22 144.402 -23.86 -0.86 324.738
Correlation (r) 1 0.9995 1 1 0.9998
RRF 1.11 0.51 0.53 1.12 1
ETO assay
25 %         1490456
50 %         2986504
75 %         4455100
100 %         5924335
150 %         9083406
Slope         12038.5
Intercept         -102066
Correlation (r)         0.9996

Table 7: Linearity, Range and RRF

As shown in Table 7, concentration range in related substances method for impurity-A is 0.254-2.035 μg/ ml; impurity-B is 0.279-2.230 μg/ml; impurity-D is 0.26-2.68 μg/ml; impurity-C is 0.273-2.18 μg/ml and ETO is 0.258-2.06 μg/ml. Concentration range for ETO in assay method is 127.5-756.0 μg/ml.

Accuracy of the method was performed at four different levels by spiking all known impurities at pre-determined concentration ranges to ETO tablets. Accuracy at 100 % of target impurities specification (0.2 % of sample concentration; 1.0 μg/ml) was performed in six replicates to evaluate method precision. Accuracy at LOQ level was also performed in six replicates to prove method range at low level. For all accuracy level, percentage (%) recovery of impurities and ETO were calculated along with % Coefficient of Variation (% CV) for replicate preparation at each level. All recoveries were calculated by applying RRF for impurities against an external standard prepared with ETO at concentration of 0.2 % with respect to sample preparation (1.0 μg/ml). All the tested levels were able to meet the acceptance criteria for recovery (85 %-115 %) and precision (% CV<15 %), indicating method suitability for routine use. It is sensible to make a note that, because of limitations in availability of sufficient quantity of impurity standards, the stock solutions prepared with all different impurities were stored at refrigerated condition and used for multiple days during the study. Storage of solutions for longer period and multiple passages of use for the stock solutions might have resulted in evaporation of solvent causing concentration of impurity. As a result of this, most of the recoveries for impurities during accuracy study are observed higher than 100 %. Accuracy for ETO for the purpose of assay test was proved at three levels ranging from 250 μg/ml to 750 μg/ml. Recovery ranges for ETO were in the range of 97.9 %-101.1 %. Observed % CV for replicate preparations at 50 % (n=3), 100 % (n=6) and 150 % (n=3) are 2.6, 1.0 and 1.2 respectively. Accuracy and precision results of RS test from the study are presented in Table 8. Chromatograms of finalized method conditions from validation study are shown in fig. 9. The present method uses short run time of 15 min with a Retention Time (RT) of ETO of about 8 min compared to a run time of 10-45 min of literature reported method. A detailed comparison of selected parameters with the present method is given in Table 9.
































Analyte name Level Nominal (µg/ml) Predicted (µg/ml) Average area % Recovery % CV
Impurity-A LOQ 0.251 0.276 3412 110.0 4.6
  50 0.502 0.526 6502 104.8 4.4
  100 1.004 1.114 13776 111.0 0.6
  150 1.506 1.584 19580 105.2 3.8
Impurity-B LOQ 0.262 0.298 1691 113.8 3.8
  50 0.524 0.493 2799 94.0 2.7
  100 1.048 1.107 6288 105.7 1.4
  150 1.572 1.643 9330 104.5 1.5
Impurity-C LOQ 0.268 0.292 1680 109.0 2.8
  50 0.536 0.568 3227 106.0 4.8
  100 1.072 1.164 6610 108.6 5.0
  150 1.608 1.691 9608 105.2 4.8
Impurity-D LOQ 0.253 0.263 3276 103.8 2.8
  50 0.506 0.531 6626 104.9 3.1
  100 1.012 1.059 13203 104.6 3.3
  150 1.518 1.664 20749 109.6 4.1
ETO assay 50 249.0 256.5 2978365 103.0 2.6
    241.0 243.5 2827109 101.0
    253.0 247.8 2876625 97.9
  100 503.4 507.6 5893625 100.8 1.0
    503.4 508.8 5907286 101.1
    503.4 502.3 5831657 99.8
    503.4 507 5886038 100.7
    511.9 515.1 5980152 100.6
    506.0 498 5781834 98.4
  150 743.0 756.4 8782134 101.8 1.2
    753.0 748.9 8694312.66 99.5
    735.0 741.3 8606491.32 100.9

Table 8: Accuracy and Precision











S. no. Column, elution process, mobile phase, flow rate, injection volume Sample linear range, detection Run time (RT of ETO) Intended use, no. of impurities covered in study Reference numbers
1 RP-C18 (250×4.6 mm, 5 µ), isocratic, 0.05 M ammonium acetate buffer pH 5.0:ACN (50:50 v/v), 1.0 ml/min, 20 µl 25.68-73.5 µg/ml, UV at 235 nm 10 min (5.5 min) For quantification of ETO in bulk and tablets, Nil [31]
2 Inertsil Octadecyl Silica (ODS)-4 (250×4.6 mm, 5 µ), isocratic, 0.01 M sodium perchlorate monohydrate buffer pH 5.0:ACN (48:52 v/v), 1.5 ml/min, 10 µl 4.67-63.96 µg/ml, UV at 235 nm 10 min (4.2 min) For quantification of ETO in tablets and in vitro release determination, Nil [32]
3 Xterra-RP-18 (150×3.5 mm, 5 µ), isocratic, 0.2 M phosphate buffer pH 5.0:ACN (60:40 v/v), 0.8 ml/min, 20 µl 1-6 µg/ml, UV at 242 nm 10 min (4.5 min) For quantification of paracetamol and ETO in tablets, Nil [33]
4 Zorbax-SB-CN (250×4.6 mm, 5 µ), isocratic, 0.02 M disodium hydrogen phosphate buffer pH 7.2: ACN (60:40 v/v), 0.8 ml/min, 10 µl 0.003-500 µg/ml, UV at 235 nm 30 min (11.2 min) For quantification of impurities, 3 [34]
5 Inertsil ODS-3V, (250×4.6 mm, 5 µ), Gradient, mobile phase A-0.01 M potassium dihydrogen phosphate buffer and ACN, 1.0 ml/min, 10 µl 0.02-1000 µg/ml, UV at 238 nm 45 min (21.5 min) For quantification of impurities, 2 [35]
6 YMC AQ-ODS (150×4.6 mm, 3 µ), Gradient, mobile phase A-0.01 M potassium dihydrogen phosphate buffer, pH 3.1 and B- ACN, 1.0 ml/min, 10 µl 0.02-1000 µg/ml, UV at 220 nm 45 min (12.5 min) For quantification of impurities, 13 [36]
7 Ascentis Express-C18 (150×4.6 mm, 2.7 µ), isocratic, 0.1 % OPA buffer pH 3.6:ACN:IPA (65.3:29:5.7 v/v), 1.0 ml/min, 10 µl 0.25-750 µg/ml, UV at 285 nm 15 min (11 min) For quantification of content and impurities in tablet formulation, 5 Current method

Table 9: Comparison of selected analytical methods of ETO

IJPS-eto

Fig. 9: Method validation chromatograms

Note: (a) Linearity-Limit of Detection (LOD); (b) Linearity-LOQ; (c) Impurities spiked to ETO tablets at LOQ; (d) Linearity-specification level; (e) Impurities spiked to ETO tablet at specification level; (1) Impurity-D; (2) Impurity-C; (3) Impurity-A, (4) Impurity- B and (5) ETO

Information gained from entire method development cycle is used for risk management of method and to define method design space. Risk assessment is sciencebased approach to identify potential critical aspects of method that help in identifying which method attributes and method parameters have an impact on the outcome of the method. Solvent purity and reagent interference are considered as critical material attributes for risk assessment. pH, composition of mobile phase, column temperature, flow rate and different types of filter membranes are considered as critical method parameters of evaluation. As the mobile phase is ternary mixture, IPA composition is considered as critical variable in mobile phase composition because IPA is in minor proportion. Robustness of the method was evaluated by showing the impact changes to critical method parameters, such as mobile phase composition, temperature, flow rate, pH of mobile phase, filter validation and solution stability. IPA variation is studied for mobile phase composition as the same is less proportional when compared to other two solvents. Different types of filters (PVDF, Nylon) are studied for filter validation and the results are well within the acceptance criteria. Assay, purity of ETO along with tailing factor and critical resolution between closely eluting impurities are the responses studied for robustness. All analytical solutions are stable for a period of 48 h at controlled room temperature condition. Robustness study results are presented in Table 10. Through implementation of risk management and robustness testing resulted in defining control strategy for the method. Control strategy is designed to ensure that the method of required quality will be produced consistently. Primary element of control strategy is identified as mobile phase composition. As the mobile phase is ternary solvent system it is decided to keep all the three solvents (buffer, ACN and IPA) in three different channels of HPLC system and the mixing is arranged through gradient valve. Another key element of control strategy is to maintain the pH of mobile phase in the range of ±0.10 units of targeted pH 3.6, as any variation beyond the defined range will lead to impurity profile swap. All other method variables are found to be not susceptible to variations and still can deliver a rugged chromatography even if, minor variations happen in the controlled method parameters.




























Method parameter ETO assay (% LC) Purity (% w/w) Tailing factor Critical resolution
% IPA (% v/v)        
6.0 99.4 99.92 1.02 1.6
6.5 100.5 99.92 1.07 1.5
7.0 98.2 99.91 1.01 1.6
Column temperature (°)        
30 101.6 99.92 1.15 1.5
35 100.5 99.92 1.07 1.5
40 100.7 99.91 1.02 1.7
Flow (ml/min)        
0.95 100.2 99.92 1.05 1.6
1.00 100.5 99.92 1.07 1.5
1.05 99.3 99.92 1.02 1.5
Mobile phase pH        
3.5 101.9 99.90 1.06 1.6
3.6 100.5 99.92 1.07 1.5
3.7 98.9 99.92 1.17 1.6
Filter study (Type)        
No filtration 101.2 99.89 1.11 1.7
PVDF 100.5 99.92 1.07 1.5
Nylon 99.5 99.91 1.17 1.6
Solution stability (Time in h)        
0 100.5 99.92 1.07 1.5
24 99.8 99.93 1.12 1.7
48 98.9 99.89 1.11 1.6

Table 10: Robustness of Method

Acknowledgements:

The authors wish to thank the management of GVK Biosciences Private Limited, Hyderabad, India for giving us an opportunity to carry out the dissertation work.

Conflict of interests:

The authors declared no conflict of interests.

References



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Pharmacophore Based Design, Synthesis and Theoretical Conformational Analysis of Some Extended Aryl Hydrazones as Potential Anticonvulsant

June 23, 2022 Published by


O. Goshain*, R. K. P. Tripathi1, A. Gupta2 and S. R. Ayyannan1

Department of Pharmaceutical Chemistry, College of Pharmacy, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh 244001, 1Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005, 2Department of Pharmacognosy, Hygia Institute of Pharmaceutical Education and Research, Lucknow, Uttar Pradesh 226013, India

*Corresponding Author:

O. Goshain

Department of Pharmaceutical Chemistry, College of Pharmacy, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh 244001

E-mail: [email protected]







Date of Received 28 May 2021
Date of Revision 03 August 2021
Date of Acceptance 03 June 2022
Indian J Pharm Sci 2022;84(3):703-711  

DOI: 10.36468/pharmaceutical-sciences.965

Abstract

A series of hydrazones were synthesized by condensing substituted hydrazides with appropriate carbonyl compounds. The chemical structures of the synthesized compounds were confirmed by infrared, proton nuclear magnetic resonance, carbon-13 nuclear magnetic resonance, mass ((4-chloro-phenylamino)-acetic acid [1-(4-chlorophenyl)-phenyl-methylene]-hydrazide and (2,4-dichloro-phenylamino)-acetic acid (3-oxo- 1,3-dihydro-indole-2-ylidene)-hydrazide only) spectral data and carbon hydrogen nitrogen analysis. The title compounds were screened for their anticonvulsant activity against mice at doses of 30 mg/kg, 100 mg/kg and 300 mg/kg. The preliminary anticonvulsant screening data of this series resulted in the identification of two lead compounds (2,4-dichloro-phenylamino)-acetic acid (3-oxo-1,3-dihydro-indole- 2-ylidene)-hydrazide and (2,4-dichloro-phenylamino)-acetic acid [1-(4-chlorophenyl)-phenyl-methylene]- hydrazide with excellent preliminary anticonvulsant profile with no neurotoxicity in maximal electroshock seizure and subcutaneous metrazol mice model.

Keywords

Hydrazides, semicarbazones, hydrazones, anticonvulsant, molecular modelling

Hydrazone derivatives have been reported to possess a wide range of biological properties including antimicrobial[1-3], antitubercular[4-6], antifungal[3-7], anticancer[8,9], anticonvulsant[10] and anti-inflammatory[11] activities. The hydrazones were synthesized from aryl amines (para (p)-chloroaniline, 2,4-dichloroaniline, p-nitroaniline and p-fluoroaniline) hydrazides. Both semicarbazones and hydrazones were well documented as novel templates for development of newer anticonvulsants and they almost share the common structural features (common binding sites) which are present in the currently available drugs such as phenytoin, zonisamide and rufinamide[12,13]. In order to identify and optimize the pharmacophore model of semicarbazone[14] as shown in fig. 1, several structural modifications were attempted by incorporating the appropriate fragments to fit to the model. In the present work, we attempted to design newer region–specific structural analogs based on the semicarbazone pharmacophore by doing the following modifications: Introduction of -CH2 between B and NH in the pharmacophore model of semicarbazone as shown in fig. 2; incorporation of chloro, nitro and fluoro substituted phenyl ring at position A of semicarbazone pharmacophore (fig. 3); inclusion of p-substituted or unsubstituted aryl ring at site (C) and R’ (fig. 4). These modifications resulted in the identification of two lead compounds 4E and 4H. The title compounds were investigated for their anticonvulsant activity against mice. In general most of the compounds have shown mild to good activity by protecting 25 % to 100 % tested animals. Few compounds like 4A, 4C and 4K were found to be inactive in all tests at all tested doses.

IJPS-semicarbazones

Fig. 1: Pharmacophore model of semicarbazones, (A): Hydrophobic aryl ring; (B): hydrogen bond acceptor; (C): distal aryl ring and (D): electron donor group

IJPS-hydrazones

Fig. 2: General pharmacophore design of hydrazones by addition of CH2 in previously established semicarbazone pharmacophore model (hybrid approach)

IJPS-Synthesis

Fig. 3: Synthesis of hydrazides

IJPS-reagents

Fig. 4: Synthesis of hydrazones (chemicals and reagents C=R3COR4)

Materials and Methods

The melting points were determined in one side sealed melting point capillary tubes using Thomas Hoover melting point apparatus and are uncorrected. The reactions were monitored by Thin Layer Chromatography (TLC) using iodine vapours as visualizing agents. Infrared (IR) spectra were determined on JASCO FT/IR-5300 IR spectrophotometer by Potassium bromide (KBr) disc method. Proton Nuclear Magnetic Resonance (1H NMR) (300 MHz) and Carbon-13 Nuclear Magnetic Resonance (13C-NMR) (75.46 MHz) spectral studies were done on JEOL Fourier Transform Nuclear Magnetic Resonance (FTNMR) spectrophotometer using chloroform as solvent. The Mass Spectrometry (MS) data for compounds 4D and 4E were recorded on a Quattro micro™ API Waters’ mass spectrometer method-Electrospray Ionization Mass Spectrometry (ESI-MS). Elemental analyses were carried out on a Perkin Elmer 2400 Carbon Hydrogen Nitrogen (CHN) elemental analyzer.

Chemistry

Synthesis of hydrazides:

A mixture of arylamine (0.017 mol), ethylchloroacetate (0.017 mol) and Potassium carbonate (K2CO3) (0.017 mol) was dissolved in acetone and placed in a dried and cleaned round bottom flask and was refluxed for 10-12 h on water bath. After refluxing, benzene was added to the mixture and it was washed with water. Then organic layer was separated with the help of separating funnel and it was dried by using anhydrous sodium sulphate. After this, the solvent was evaporated and the resultant crude product was recrystallized from ethanol. Ester (0.017 mol) was dissolved in ethyl alcohol and placed in a dried round bottomed flask. Hydrazine hydrate[15] (0.017 mol) was added to the solution and then the reaction mixture was refluxed for 12-16 h. After cooling, the mixture was transferred in a dry petri dish and left it for simple evaporation. The residue obtained after evaporation was collected and recrystallized from ethanol.

Synthesis of hydrazones:

Hydrazide (0.017 mol) and carbonyl compound (0.034 mol) was taken in 1:2 molar ratios in a dried round bottom flask. Propanol was added to the flask as a solvent. Sodium acetate or glacial acetic acid was added to the reaction mixture as catalyst. Now reaction mixture was refluxed for 1-2 h. After cooling the mixture was transferred to a dry petri dish and left it for evaporation. The solid product obtained after evaporation was recrystallized from ethanol.

T(4-chloro-phenylamino)-acetic acid hydrazide (3A): Molecular formula: C8H10ClN3O; molecular weight: 199.64; yield: 74.20 %; melting point (m.p): 58°; IR (KBr, ν, cm-1): 3471 (-NH2), 3381 (NH-), 2924 (CH2), 1672 (-C=O), 823 (aryl, C-H) 638 (-Cl); 1H NMR (CDCl3-d1, Deuterium oxide (D2O) exchange, 300 MHz): δ 7.94 ( m, 1H, CONH), δ 6.37-7.05 (m, 4H, Ar-CH), δ 4.03 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 170.3 (CONH), δ 145.7 (Ar-CCl), δ 114.9 –129.7 (Ar- CH), δ 122.7 (Ar-CNH), δ 57.3 (CH2); anal: (calculated: C: 48.13, H: 05.05, N: 21.05; found: C: 48.24, H: 04.91, N: 21.32) %.

(2,4-dichloro-phenylamino)-acetic acid hydrazide (3B): Molecular formula: C8H9Cl2N3O; molecular weight: 234.08; yield: 73.24 %; m.p: 51°; IR (KBr, ν, cm-1): 3419 (NH-), 3319 (-NH2) 2934 (CH2), 1728 (-C=O), 812 (Aryl-CH) 640 (-Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37-7.56 (m, 8H, Ar-CH), δ 7.26 (s, 1H, CONH), δ 4.92 (s, 1H, Ar-NH). δ 3.91(m, 2H, methylene-H), δ 2.11 (m, 2H, amine-NH); 13C NMR (CDCl3-d1): δ 170.3 (CONH), δ 142.0 (Ar-CNH), δ 116.3–131.2 (Ar-CH), δ 123.8-124.1 (Ar-CCl) δ 56.8 (CH2); anal: (calculated: C: 41.05, H: 03.88, N: 17.95; found: C: 41.07, H: 04.17, N: 17.63) %.

(4-nitro-phenylamino)-acetic acid hydrazide (3C): Molecular formula: C8H10N4O3; molecular weight: 210.19; yield: 73.20 %; m.p: 110°; IR (KBr, ν, cm-1): 3419 (NH-), 3319 (-NH2) 2934 (CH2), 1633 (-C=O), 1535 (-NO2), 858 (aryl-CH); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37-7.56 (m, 8H, Ar-CH), δ 7.26 (s, 1H, CONH), δ 4.92 (s, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 170.3 (CONH), δ 153.7 (Ar-CNH), δ 114.4–121.9 (Ar-CH), δ 136.8 (Ar-CNO2), δ 57.3 (CH2); anal: (calculated: C: 45.71, H: 04.80, N: 22.84; found: C: 45.43, H: 04.39, N: 23.17) %.

(4-fluoro-phenylamino)-acetic acid hydrazide (3D): Molecular formula: C8H10FN3O; molecular weight: 183.18; yield: 78.36 %; m.p: 80°; IR (KBr, ν, cm-1): 3471 (-NH2), 3381 (NH-), 2924 (CH2), 1672 (-C=O), 823 (Aryl, C-H), 638 (-F); 1H NMR(CDCl3-d1, D2O exchange, 300 MHz): δ 6.37-7.56 (m, 8H, Ar-CH), δ 7.26 (s, 1H, CONH), δ 4.92 (s, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 170.3 (CONH), δ 151.3 (Ar-CF), δ 143.2 (Ar-CNH), 115.1– 116.3 (Ar-CH), δ 57.3 (CH2); anal: (calculated: C: 52.45, H: 05.50, N: 22.94; found: C: 52.15, H: 05.57, N: 21.83) %.

(4-chloro-phenylamino)-acetic acid (3-oxo-1,3-dihydro-indole-2-ylidene)-hydrazide (4A): Molecular formula: C16H13ClN4O2; molecular weight: 328.75; yield: 60.83 %; m.p: charred; IR (KBr, ν, cm-1): 3259 (NH), 2928 (-CH2-), 1726 (-C=O), 1570 (-C=N), 833 (aryl C-H), 644 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37-7.56 (m, 8H, Ar-CH), δ 7.26 (s, 1H, CONH), δ 4.92 (s, 1H, Ar-NH) δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 190.0 (CO), δ 173.0 (CONH), δ 156. 0 (CNH), δ 154.0 (CN), δ 145.7(Ar-CNH), δ 114.9–135.4 (Ar-CH), δ 122.7 (Ar-CCl), δ 112.5 (indole-C), δ 57.6 (CH2); anal: (calculated: C: 58.45, H: 03.99, N: 17.04; found: C: 57.94, H: 03.33, N: 18.34) %.

(4-chloro-phenylamino)-acetic acid (1-phenylethylidene)-hydrazide (4B): Molecular formula: C16H16ClN3O; molecular weight: 301.77; yield: 79.73 %; m.p: 96°; IR (KBr, ν, cm-1): 3377 (NH), 3001 (-CH2-), 1722 (-C=O), 1570 (-C=N), 838 (aryl, C-H), 646 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37- 7.60 (m, 9H, Ar-CH), δ 7.26 (s, 1H, CONH), δ 4.12 (s, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H), δ 0.93(s, 3H, methyl-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 145.7 (Ar-CNH), δ 131.1 (Ar-C), δ 114.9–131.1 (Ar-CH), δ 122.7 (Ar-CCl), δ 57.6 (CH2), δ 13.5 (CH3); anal: (calculated: C: 63.68, H: 05.34, N: 13.92; found: C: 63.41, H: 05.34, N: 13.69) %.

(4-chloro-phenylamino)-acetic acid [1-(4-nitrophenyl)-ethylidene)]-hydrazide (4C): Molecular formula: C16H15ClN4O3; molecular weight: 346.77; yield: 90.90 %; m.p: 56°; IR (KBr, ν, cm-1): 3362 (NH), 3001 (-CH2-), 1693 (-C=O), 1608 (-C=N), 1525 (NO2), 856 (aryl, C-H), 692 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37–8.12 (m, 8H, Ar- CH), δ 7.26 (s, 1H, CONH), δ 4.32 (m, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H) δ 0.93 (s, 3H, methyl-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 150.7 (Ar-CNO2), 145.7 (Ar-CNH), δ 122.7 (Ar- CCl), δ 57.6 (CH2), δ 13.5 (CH3); anal: (calculated: C: 55.42, H: 04.36, N: 16.16; found: C: 55.01, H: 04.73, N: 15.89) %.

(4-chloro-phenylamino)-acetic acid [1-(4-chlorophenyl)-phenyl-methylene]-hydrazide (4D): Molecular formula: C21H17Cl2N3O; molecular weight: 398.29; yield: 83.69 %; m.p: 54°; IR (KBr, ν, cm-1): 3417 (NH), 2924 (-CH2-), 1651 (-C=O), 1585 (-C=N), 844 (aryl-CH), 696 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.37–7.84 (m, 13H, Ar- CH), δ 7.13 (s, 1H, CONH), δ 4.17 (m, 1H, Ar-NH). δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 170.3 (CONH), δ 145.7 (Ar-CCl), δ 114.9–129.7 (Ar- CH), δ 122.7 (Ar-CNH), δ 57.3 (CH2); ESI-MS m/z 399.29 [M+1]+; anal: (calculated: C: 63.33, H: 04.30, N: 10.55; found: C: 63.31, H: 03.98, N: 10.11) %.

(2,4-dichloro-phenylamino)-acetic acid (3-oxo-1,3-dihydro-indole-2-ylidene)-hydrazide (4E): Molecular formula: C16H12Cl2N4O2; molecular weight: 363.20; yield: 98.79 %; m.p: 111°; IR (KBr, ν, cm-1): 3348 (-NH), 2927 (-CH2-), 1745 (-C=O), 1618 (-C=N),813 (aryl, C-H), 661 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.31–7.56 (m, 7H, Ar-CH), δ 7.13 ( s, 1H, CONH), δ 4.11 (m, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 190.0 (CO), δ 173.0 (CONH), δ 156. 0 (CNH), δ 154.0 (CN), δ 142.0 (Ar-CNH), δ 116.3–135.4 (Ar-CH), δ 124.1 (ArpCCl), δ 123.8 (Ar-oCCl), δ 112.5 (indole-C), δ 57.1 (CH2); ESI-MS m/z 382.21 [M+1]+ anal: (calculated: C: 52.91, H: 03.33, N: 15.43; found: C: 53.21, H: 02.95, N: 15.71) %.

(2,4-dichloro-phenylamino)-acetic acid (1-phenylethylidene)-hydrazide (4F): Molecular formula: C16H15Cl2N3O; molecular weight: 336.22; yield: 29.32 %; m.p: 56°; IR (KBr, ν, cm-1): 3319 (NH), 2951(-CH2-), 1728 (-C=O), 1624 (-C=N), 862 (aryl, C-H), 646 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.31– 7.60 (m, 8H, Ar-CH), δ 7.13 (s, 1H, CONH), δ 4.01 (m, 1H, Ar-NH), δ 3.91 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 142.0 (Ar-CNH), δ 134.0 (Ar-C), δ 116.3–131.2 (Ar-CH), δ 124.1 (Ar-pCCl), δ 123.8 (Ar-oCCl), δ 57.1 (CH2), δ 13.5 (CH3); anal: (calculated: C: 57.16, H: 04.50, N: 12.50; found: C: 57.16, H: 04.10, N: 12.22) %.

(2,4-dichloro-phenylamino)-acetic acid [1-(4-nitrophenyl)-ethylidene)]-hydrazide (4G): Molecular formula: C16H14Cl2N4O3; molecular weight: 381.21; yield: 75.78 %; m.p: 56°; IR (KBr, ν, cm-1): 3362 (NH), 2922 (-CH2-), 1693 (-C=O), 1608 (-C=N), 1527 (NO2), 856 (aryl-CH), 692 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.34-8.10 (m, 7H, Ar-CH), δ 7.16 (s, 1H, CONH), δ 4.01 (m, 1H, Ar-NH), δ 3.87 (m, 2H, methylene-H) δ 0.91 (s, 3H, methyl-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 150.7 (Ar-CNO2), δ 142.0 (Ar-CNH), δ 116.3-131.2 (Ar-CH), δ 124.1 (Ar-pCCl), δ 123.8 (AroCCl), δ 57.1 (CH2), δ 13.5 (CH3); anal: (calculated: C: 50.41, H: 03.70, N: 14.70; found: C: 50.76, H: 04.02, N: 14.39) %.

(2,4-dichloro-phenylamino)-acetic acid [1-(4-chlorophenyl)–phenyl-methylene]-hydrazide (4H): Molecular formula: C21H16Cl3N3O; molecular weight: 432.13; yield: 92.56 %; m.p: 56°; IR (KBr, ν, cm-1): 3419 (NH), 2924 (-CH2-), 1651 (-C=O), 1597 (-C=N), 844 (Aryl, C-H), 696 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.31-7.84 (m, 12H, Ar- CH), δ 7.13 (s, 1H, CONH), δ 4.15 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 142.0 (Ar-CNH), δ 116.3-131.1 (Ar- CH), δ 136.6 (R4-pCCl), δ 124.1 (Ar-pCCl), δ 123.8 (Ar-oCCl), δ 57.1 (CH2); anal: (calculated: C: 58.29, H: 03.73, N: 09.71; found: C: 57.84, H: 04.09, N: 10.03) %.

(4-nitro-phenylamino)-acetic acid (3-oxo-1,3-dihydro-indole-2-ylidene)-hydrazide (4I): Molecular formula: C16H13N5O4; molecular weight: 339.31; yield: 98.81 %; m.p: 122°; IR (KBr, ν, cm-1): 3363 (NH), 2920 (-CH2-), 1693 (-C=O), 1600 (-C=N), 1525 (NO2), 856 (aryl, C-H); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.63–7.98 (m, 7H, Ar-CH), δ 7.13 (s, 1H, CONH), δ 4.05 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 190.0 (CO), δ 173.0 (CONH), δ 156.0 (CNH), δ 154.0 (CN), δ 153.7 (Ar-CNH), δ 136.8 (Ar-CNO2), δ 114.4–135.4 (Ar- CH) δ 57.6 (CH2); anal: (calculated: C: 51.42, H: 03.24, N: 18.74; found: C: 51.00, H: 02.77, N: 18.74) %.

(4-nitro-phenylamino)-acetic acid (1-phenylethylidene)-hydrazide (4J): Molecular formula: C16H16N4O3; molecular weight: 312.32; yield: 96.05 %; m.p: 99°; IR (KBr, ν, cm-1): 3363 (NH), 2924 (-CH2-), 1634 (-C=O), 1600 (-C=N), 1535(NO2), 840 (aryl, C-H); 1H NMR(CDCl3-d1, D2O exchange, 300 MHz): δ 6.61–7.18 (m, 8H, Ar-CH), δ 7.03 (s, 1H, CONH), δ 4.07 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 153.7 (Ar-CNH), δ 136.8 (Ar-CNO2), δ 114.4–131.1 (Ar-CH), δ 134.0 (Ar-C), δ 57.6 (CH2), δ 13.5 (CH3); anal: (calculated: C: 55.42, H: 04.36, N: 16.16; found: C: 55.10, H: 03.99, N: 16.16) %.

(4-nitro-phenylamino)-acetic acid [1-(4-chlorophenyl)–phenyl-methylene]-hydrazide (4K): Molecular formula: C21H17ClN4O3; molecular weight: 408.84; yield: 73.38 %; m.p: 50°; IR (KBr, ν, cm-1): 3363 (NH), 2920 (-CH2-), 1651 (-C=O), 1585 (-C=N), 1535 (NO2), 844 (aryl, C-H), 696 (Cl); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.69– 7.84 (m, 13H, Ar-CH), δ 7.01 (s, 1H, CONH), δ 4.00 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 173 (CONH), δ 153.7 (Ar-CNH), δ 136.8 (Ar-CNO2), δ 136.6 (Ar-CCl) δ, δ 114.4–131.1 (Ar-CH), 57.6 (CH2); anal: (calculated: C: 61.69, H: 04.19, N: 13.70; found: C: 61.67, H: 04.19, N: 13.78) %.

(4-fluoro-phenylamino)-acetic acid (3-oxo-1,3-dihydro-indole-2-ylidene)-hydrazide (4L): Molecular formula: C16H13FN4O2; molecular weight: 312.31; yield: 38.42 %; m.p: 99°; IR (KBr, ν, cm-1): 3408 (NH), 2926 (-CH2-), 1697 (-C=O), 1585 (-C=N), 1521 (NO2), 856 (aryl-CH), ,756 (F); 1H NMR (CDCl3-d1, D2O exchange, 300 MHz): δ 6.41–7.56 (m, 8H, Ar-CH), δ 7.04 (s, 1H, CONH), δ 4.00 (m, 1H, Ar-NH). δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 190.0 (CO), δ 173.0 (CONH), δ 156.0 (CNH), 154.0 (CN) δ, δ 151.3 (Ar-CF), δ 143.2 (Ar-CNH), δ 115.1-135.4 (Ar-CH), δ 112.5 (indole-C), δ 57.6 (CH2); anal: (calculated: C: 61.53, H: 04.20, N: 17.94; found: C: 61.49, H: 03.89, N: 18.11) %.

(4-fluoro-phenylamino)-acetic acid (1-phenylethylidene)-hydrazide (4M): Molecular formula: C16H16FN3O; molecular weight: 285.32; yield: 46.26 %; m.p: 124°; IR (KBr, ν, cm-1): 3383 (NH), 2908 (-CH2-), 1668 (-C=O), 1585 (-C=N), 1521 (NO2), 815 (aryl-CH), 788 (F); 1H NMR(CDCl3-d1, D2O exchange, 300 MHz): δ 6.41–7.60 (m, 9H, Ar-CH), δ 7.00 (s, 1H, CONH), δ 4.00 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H), δ 0.9 (methyl-CH); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 151.3 (Ar-CF), δ 143.2 (Ar- CNH), δ 134.0 (Ar-C), δ 115.1–131.1 (Ar-CH) δ 57.6 (CH2), δ 13.5 (CH3); anal: (calculated: C: 60.10, H: 04.73, N: 13.14; found: C: 59.88, H: 05.01, N: 13.11) %.

(4-fluoro-phenylamino)-acetic acid [1-(4-chlorophenyl)–phenyl-methylene]-hydrazide (4N): Molecular formula: C21H17ClFN3O; molecular weight: 381.83; yield: 96.02 %; m.p: 49°; IR (KBr, ν, cm-1): 3344 (NH), 2926 (-CH2-), 1651 (-C=O), 1583 (-C=N), 1518 (NO2), 844 (aryl-CH), 788 (F); 1H NMR(CDCl3-d1, D2O exchange, 300 MHz): δ 6.69–7.97 (m, 8H, Ar-CH), δ 7.01 (s, 1H, CONH), δ 4.00 (m, 1H, Ar-NH), δ 3.95 (m, 2H, methylene-H); 13C NMR (CDCl3-d1): δ 173.0 (CONH), δ 168.7 (CN), δ 151.3 (Ar-CF), δ 143.2 (Ar- CNH), δ 134.0 (Ar-C), δ 115.1–131.1 (Ar-CH), δ 57.6 (CH2), δ 13.5 (CH3); anal: (calculated: C: 61.69, H: 04.19, N: 13.70; found: C: 62.11, H: 04.19, N: 13.71) %.

Anticonvulsant screening:

The synthesized compounds were evaluated for their anticonvulsant activity under “The National Institute of Health (NIH) Anticonvulsant Drug Development (ADD) program preclinical Anticonvulsant Screening Project (ASP)”; Bethesda, USA. A battery of tests determines the anticonvulsant activity of the compounds. The synthesized compound were evaluated for their primary anticonvulsant activity by electrically induced seizures (Maximal Electroshock Seizure (MES), 6 Hz psychomotor seizure) test and chemically induced seizure (subcutaneous Metrazol (scMET)) test[16] against mice and was compared with the standard drugs phenytoin, carbamazepine, valproic acid and levetiracetam.

Conformational analysis and pharmacophore modelling:

The lead compounds 4E and 4H were selected for the modeling studies. In addition some inactive compounds (4A and 4K) were also chosen for the comparative structural analysis. All selected compounds were subjected to the conformational analysis and pharmacophore modeling studies with the following assumptions.

The compounds are assumed to act via inactivation of voltage gated sodium channels by binding to a hypothetical binding site. Two aryl rings (aromatic sextets) namely A and C in the ligands contribute to the hydrophobic interactions with the hypothetical binding site and are essential for activity. Such interactions are highly influenced by electronegative substituents such as bromo, chloro etc., on the rings.

The optimum interaction of these ligands with receptors also involves hydrogen bonding of pharmacophoric atoms with unshared electron pairs or protons (-CONH- , HBD) and an electron (pi) donor which are present in the linker (NH-CH2-CO-NH-N=C-) between the two aryl rings.

Initially, the conformational search was carried out to yield at least 10 low energy conformers. The selected conformers were then subjected to energy minimization by using Merck Molecular Force Field 94 (MMFF94) model available and the best aligning low energy conformer was selected for the manual pharmacophore modeling studies using Marvin. A pharmacophore model comprising all essential sites was generated manually to study their spatial relationships. The alignment, distances, molecular surface (electrostatic gradient maps) and the spatial relationship of pharmacophoric points are presented in fig. 5a and fig. 5b.

IJPS-generated

Fig. 5: Distances between (A): Major pharmacophoric groups and (B) the generated Van der Waals molecular surface showing the electrostatic gradients in colors in selected active hydrazones

Based on the pharmacophore ensemble of active compound (4E) and its spatial arrangements along with distant constraints a simple five point Two- Dimensional (2D) pharmacophore model as shown in fig. 6 was generated which is in well agreement with our previously proposed four point pharmacophore model for semicarbazones[14] as shown in fig. 1.

IJPS-model

Fig. 6: Proposed graphical five point 2D pharmacophore model for the novel hydrazones (distances in Å)

Results and Discussion

Synthesis of aryl hydrazones was carried out by first esterification of the aryl amines (p-chloroaniline 2,4-dichloroaniline, p-nitroaniline and p-fluoroaniline) with ethylchloroaetate in presence of K2CO3, which gave the ethyl ester. The ethyl ester and hydrazine hydrate was refluxed with stirring in ethanol to give precipitates of hydrazide in good yield (78 %-81 %). Then refluxed the ethanolic solution of the hydrazides with appropriate carbonyl compounds which yielded the aryl hydrazones as shown in fig. 3 and fig. 4. The hydrazones were purified by recrystallization from appropriate solvent. IR, 1H-NMR, 13C NMR, mass spectra and elemental (CHN) data of all the synthesized compounds were consistent with the assigned structures. All the synthesized compounds were screened for anticonvulsant activity by MES, scMET and neurotoxicity (TOX) tests after Intraperitoneal (IP) injection to mice at doses of 30, 100 and 300 mg/kg (at “National Institutes of Health”, Maryland, USA). Also included the activity of the reference compounds phenytoin, carbamazepine and valproic acid. The result of anticonvulsant screening was obtained from NIH and is presented in Table 1.


















































Compound code Dose (IP; mg/kg) MES scMET TOX
0.5 h 4 h 0.5 h 4 h 0.5 h 4 h
4A 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 0/1 0/1 0/1 0/1 0/4 0/2
4B 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 0/1 0/1 4/5 0/1 0/4 0/2
4C 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 0/1 0/1 0/1 0/1 0/4 0/2
4D 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 1/1 0/1 0/1 0/1 1/4 0/2
4E 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 1/3 0/3 0/1 0/1 0/8 0/4
  300 1/1 1/1 0/1 0/1 1/4 0/2
4F 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 1/3 0/1 0/1 0/8 0/4
  300 0/1 1/1 1/5 0/1 1/4 1/2
4G 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 1/3 1/3 0/1 0/8 0/4
  300 0/1 1/1 4/5 0/1 0/4 0/2
4H 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 1/3 0/1 0/1 0/8 0/4
  300 0/1 1/1 3/5 0/1 1/4 0/2
4I 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 1/1 0/1 0/1 0/1 0/4 0/2
4J 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 1/3 0/1 0/1 0/8 0/4
  300 0/1 1/1 0/1 0/1 0/4 0/2
4K 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 0/1 0/1 0/1 0/1 0/4 0/2
4L 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 1/3 0/3 0/1 0/1 0/8 0/4
  300 1/1 0/1 0/1 0/1 0/4 0/2
  30 0/1 0/1 0/1 0/1 0/4 0/2
4M 100 0/3 1/3 0/1 0/1 0/8 0/4
  300 0/1 1/1 3/5 0/1 0/4 0/2
4N 30 0/1 0/1 0/1 0/1 0/4 0/2
  100 0/3 0/3 0/1 0/1 0/8 0/4
  300 1/1 1/1 0/1 0/1 0/4 0/2
PHT 30* 100* – – 100* 100*
CBZ 30* – 100* – 100* 300*
VPA – – 300* – – –

Table 1: Preliminary Anticonvulsant Screening Data of Hydrazones (4A To 4N) in Mice.

All the 14 newly synthesized hydrazones were screened for preliminary anticonvulsant activity in mice at the dose of 300, 100 and 30 mg/kg IP (Table 2). In general most of the compounds showed mild to good activity by protecting 25 % to 100 % tested animals.

In preliminary MES screening, compounds 4E, 4F, 4G, 4H, 4J,4L and 4M showed good activity at both 100 and 300 mg/kg and were more active than VPA with 33 %-100 % protection. Compound 4E, 4F, 4G, 4H, 4J, 4M and 4N have shown long duration of activity i.e. up to 4 h at 300 mg/kg. All the active compounds except 4D, 4E, 4F and 4H have exhibited neurotoxicity at their higher anticonvulsant dose i.e. 300 mg/kg. In scMET screening, most of the compounds were inactive except 4B, 4F, 4G, 4H and 4M and these active compounds showed protection ranging from 20 % to 80 %. One of the active compounds from the 60 Hz MES screening 4E (since 4E showed better result with maximum protection in MES screening) was further evaluated in 6 Hz MES screening at 100 mg/kg (Table 2). This compound exhibited 75 % protection up to 4 h without causing neurotoxicity. From the above results, compounds 4E and 4H has been identified as lead compounds as they have displayed excellent anticonvulsant activity in mice.







Compound code MES TOX
0.25 h 0.5 h 1 h 2 h 4 h 0.25 h 0.5 h 1 h 2 h 4 h
4E 1/4 1/4 0/4 0/4 3/4 0/4 0/4 0/4 0/4 0/4
Levetiracetam 0/4 2/4 4/4 0/4 0/4 0/4 0/4 0/4 0/4 0/4

Table 2: Anticonvulsant Screening Data of 4E in 6hz Mes Mice Model At 100 Mg/Kg (IP)

It can be summarized that the modifications made in the semicarbazone pharmacophore model resulted in the identification of two lead compound (4E and 4H) with excellent preliminary anticonvulsant profile with no neurotoxicity in MES and scMET mice model. Future studies on the molecules 4E and 4H and on its analogs, on advanced animal models (oral MES and quantitative MES) are essential to establish the extent of their potency and future prospective for the development of these molecules as novel antiepileptic agents.

Conflict of interests:

The authors declared no conflicts of interest.

References



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