Phytoconstituents of a Traditional Oil Formulation Inhibits IL-17A and TNF-α involved in Psoriasis: A Molecular Docking Study

Nayak Deeksha Dayanand¹, Rajasekhar Chinta², Shama Prasada Kabbekodu³, Arul Amuthan^4,5, Sathish Pai B⁶, K Sreedhara Ranganath Pai⁷, Suman Manandhar⁷, Vasudha Devi^1,8*

¹Department of Pharmacology, Kasturba Medical College,

Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

²Department of Pharmacology, Manipal University College, Melaka 75150, Malaysia.

³Department of Cell and Molecular Biology, Manipal School of Life Sciences,

Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

⁴Division of Pharmacology, Department of Basic Medical Sciences,

Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

⁵Division of Siddha, Centre for Integrative Medicine and Research,

Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

⁶Department of Dermatology, Venereology and Leprosy, Kasturba Medical College,

Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

⁷Department of Pharmacology, Manipal College of Pharmaceutical Sciences,

Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

⁸Centre for Cardiovascular Pharmacology (CCP),

Manipal Academy of Higher Education, Manipal 576104, Karnataka, India.

ABSTRACT:

Psoriasis is one of the chronic inflammatory conditions with multifactorial aetiology. Even though there are different treatments available, there is no cure for psoriasis. A Siddha polyherbal formulation, Sivanar vembu kuzhi thailam (SVKT), is used to treat various skin diseases. In this study, methanolic extract of SVKT was analysed using gas chromatography–mass spectrometry (GC-MS) which showed the presence of 86 compounds. They were further subjected to molecular docking to find the effect of SVKT on inflammatory proteins, IL-17A and TNF-α, involved in the pathogenesis of psoriasis. Four shortlisted compounds from SVKT exhibited their inhibitory potential on IL-17A with binding energy varying between -8.2 to -6.6 kcal/mol and three compounds on TNF-α with binding energy varying between -7.8 to -5.6 kcal/mol. Pharmacokinetic properties (Absorption, Distribution, Metabolism, Excretion and Toxicity-ADMET) were also evaluated in silico which showed favourable features. 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol and α-Lactose among the shortlisted constituents, inhibited both proteins through exhibiting multiple interactions. Hence this study provides valuable insights into the inhibitory effect of phytochemicals present in SVKT on IL-17A and TNF-α which may pave way to the discovery of new drugs to treat psoriasis.

INTRODUCTION:

Psoriasis is an inflammatory immune-mediated chronic skin condition that is impacted by both genetic and environmental factors. Around 1-3% of the population in the world is affected by psoriasis¹. According to an epidemiological study done in North India, the prevalence of psoriasis was found to be 0.44-2.8%². Though several treatments like topicals, systemic agents, biologicals, and phototherapies are available for psoriasis, none of these drugs offer complete cure. Methotrexate with topicals is majorly used controlling the disease, however, they are associated with shorter disease-free intervals, frequent relapse and adverse effects which include nausea, vomiting, rashes, headache, fatigue, liver toxicity, atrophy of gastrointestinal tracts, abnormal renal function, lung damage, pruritis, pneumonia and severe infections. Biological agents such as adalimumab, infliximab, etanercept (TNF-α antagonists) and secukinumab (Interleukin-17A antagonists) are prescribed for treating psoriasis. Even though these targeted therapies are more effective than other therapies, they are expensive and associated with several adverse effects like headache, back pain, arthralgia, abnormal liver function, diarrhoea, rhinitis, upper respiratory tract infection and other severe infections like cellulitis, bronchitis and viral meningitis^3-6. Hence, search for drug molecules which are not only effective in controlling the disease with longer remission period, but also cost effective with favourable toxicity profile still continues.

Recently herbal medicines are gaining importance in various diseases because they claim to have no side effects and are cost effective. In India, Ayurveda and Siddha medicines are being used to treat psoriasis⁷.  Siddha is an ancient Indian traditional system of medicine prevalent in Tamil Nadu, South India. Siddha has herbal and herbo-mineral composition that are used for treating various dermatological conditions like psoriasis, vitiligo, pemphigus, eczema, alopecia, diabetic ulcer, warts, pompholyx and leprosy⁸.

Sivanar vembu kuzhi thailam (SVKT) is a polyherbal Siddha drug used for various skincare ailments. It is composed of Indigofera aspalathoides, Celastrus paniculatus and Corallocarpus epigaeus⁹.  SVKT has polyphenols and flavonoids which could be responsible for its antioxidant activity⁹. Subchronic exposure of SVKT at a dose of 40 and 130mg/kg, when given orally to Sprague Dawley rats, did not show any side effects or toxic effects, indicating the safety of the drug¹⁰. Another study using SVKT along with Agasthyar kuzhampu, karbogi mathirai and Raktha suddhi mathirai was done in a patient with severe psoriasis. In this study, psoriatic lesions completely disappeared within three months of treatment¹¹. This study focuses on Gas Chromatography–Mass Spectrometry (GC-MS) analysis of SVKT to identify its phytochemicals and in silico molecular docking of phytoconstituents on inflammatory target proteins involved in the pathogenesis of psoriasis namely Tumour Necrosis Factor-alpha (TNF- α) and Interleukin-17 (IL-17A) to identify lead molecules with antipsoriatic potential. Antipsoriatic potential followed by their pharmacokinetic/pharmacodynamic (PK/PD) prediction analysis.

MATERIALS AND METHOD:

GCMS Analysis:

SVKT was obtained from GMP, ISO certified company, Sivasakthi Pharmaceutical Pvt Ltd, Coimbatore, Tamil Nadu, India. GC-MS analysis was performed at Analytical  Research and Metallurgical Laboratories Pvt. Ltd. (ARML), Bengaluru, Karnataka, India. To summarise, the active components of SVKT were extracted by dissolving SVKT in methanol (1mg/mL of final concentration). The Shimadzu GCMS-QP2010S instrument was used for GC-MS. The column utilised was RTX-5, with dimensions of (30m length, 0.25mm internal diameter, and 0.25µm film thickness). The ion source temperature was 200°C, while the interface temperature was 280°C. Helium was employed as the carrier gas, with a flow rate of 1mL/min. 1µL sample was injected for analysis. The mass spectrum generated by GC-MS was interpreted using the database National Institute Standard and Technology (NIST) and PubChem. The name, molecular weight, chemical formula, retention time (R. time), percentage area, and CAS number of individual compounds were extracted.

Molecular docking:

To predict the bioactive confirmation of the compounds identified from the GCMS analysis, molecular docking was performed on two major proteins of psoriatic inflammatory pathway, IL-17A and TNF-α using AutoDock Vina software (Scripps Research Institute, USA). The protein was loaded into the AutoDock tool, polar hydrogen residues and Kollman atomic charges were added, radii was assigned, and saved as PDBQT. All bonds in phytoconstituents were kept rotatable. All calculations were set for fixed protein and flexible ligand docking using the Lamarckian Genetic Algorithm (LGA) method. Grid boxes were set at two binding sites of the TNF- α and IL-17A, and their XYZ coordinates include X: 107.640 Y: -29.892 Z: 1.991 Å and X: 102.640 Y: 9.850 Z: 29.741 Å respectively with a grid size of 40. Command prompt was used to run the Vina, and the best conformation out of ten runs with the lowest docked energy was analysed for a protein-ligand complex of each phytoconstituent. The average affinity for best poses was taken as the final affinity value. The interactions of TNF- α and IL-17A protein-ligand conformations, including hydrogen and hydrophobic bonds, and their interacting residues were analysed using BIOVIA Discovery Studio Visualizer.

Ligand preparation:

The PubChem Compound database was used to collect the 2D structures of phytoconstituents found in SVKT. Using ChemSketch 2019 2.2 (created by ACD laboratories), 86 compound structures were sketched, prepared, and saved in MDL mol format. The structures were then converted to PDB format with the Open Babel GUI (3.1.1). The SPDBV (SwissPDB visualiser) was used to minimise energy in each structure. These 2D structures were also stored as PDBQT for docking.

Preparation of TNF- α (2AZ5) and IL-17A (5HI5) for docking:

The 3D X-ray crystallographic structures of IL-17A (RCSB PDB id: 5HI5) and TNF- α (RCSB PDB id: 2AZ5) were obtained and saved in PDB format from the Protein Data Bank (RCSB PDB). The protein file was optimised by deleting the complexed ligand's HETATOM coordinates. The protein file was then visualised using the Swiss Protein Data Bank Viewer (SPDBV), subjected for energy minimisation and the optimised file was used for molecular docking.

ADMET analysis:

ADMET prediction was performed for all the shortlisted compounds using Swiss ADME and ADMETlab2.0 web tools to understand their pharmacokinetic behaviour. Physicochemical properties such as TPSA, Molecular weight, number of rotatable bonds, H-bond acceptors and donors, Lipinski’s rule and bioactivity prediction scores, were documented.

RESULTS:

GCMS analysis of SVKT:

Methanolic extract of SVKT has shown the presence of 86 compounds. The identified compounds with their retention time (RT), percentage area, molecular weight, molecular formula, and CAS number are shown in Table 1, where phytoconstituents are arranged from higher percentage area to lower percentage area.

Table 1: Phytoconstituents identified in the methanolic extract of SVKT

Docking studies:

Interaction analysis of shortlisted ligands of SVKT with IL-17A and TNF-α protein structures:

Docking was performed on all the 86 compounds and based on the dock score top 8 compounds were shortlisted for further analysis. After the computational analysis best compounds were shortlisted separately for IL-17A and TNF-α. Four shortlisted compounds showed better interaction profile against IL-17A, and three compounds showed good interaction profiling against TNF-α. Two compounds showed interactions with both the proteins. The shortlisted compounds were tabulated along with their interactions in table 2 and 3. Further, physicochemical characterisations, pharmacokinetic features, and possible toxicity predictions for shortlisted compounds were predicted using ADMET prediction analysis.

It is noted that the co-crystallized ligand (45, 20R)-7-chloro-N-methyl-4-f[(1-methyl-1H-pyrazol-5-y)carbonyl]amino)-3, 18-dioxo-2, 19-diazatetracyclo [20.2.2.1~6.10~.1~11, 15-]octacosa-1(24), 6(28), 7, 9, 11(27), 12, 14, 22, 25-nonaene-20-carboxamide) within the crystal structure of IL-17A (PDB ID: 5HI5) made a majority of interactions with Leu 97 and Pro 63 residues and fewer interactions with Tyr 67 of IL-17A dimer. The interaction analysis of compounds of SVKT against IL-17A revealed favourable interactions with the residues similar to that of the native ligand. The key residues involved in IL-17A interactions were Leu 97 followed by Pro 63 (Figure 1). Among these interactions, N1-(m-Tolyl)-N2-(tetrahydrofurfuryl) oxamide with -6.76 kcal/mol binding energy (BE) formed five hydrogen interactions, two hydrophobic interactions with Leu 97 and a couple of hydrophobic interactions with Pro 63 (Table 2). It could be the principal compound among the four shortlisted compounds as it has made the highest number of interactions with IL-17A among the four. 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol successfully formed four hydrogen interactions with Leu 97, one with Pro 63 and it played a pivotal role in making the complex more stable. It showed a total of nine interactions including both hydrogen and hydrophobic with the binding surface of IL-17A with the lowest BE of -8.28 kcal/mol amongst the four shortlisted compounds. Lastly, α-Lactose showed two hydrogen interactions with Leu 97 with a minimum BE of -6.67 kcal/mol and it did not make any hydrophobic interactions (Table 2).

Table 2: Interaction profiles of top four constituents of SVKT that showed better binding energy and interactions with IL-17A binding site.

Figure 1: Displaying the interactions made by the shortlisted phytoconstituents of SVKT with the binding site of IL-17A. Light and dark green lines: hydrogen interactions (< 5 Å distance); purple & pink lines: hydrophobic interactions.

On the other hand, the co-crystallized ligand (6,7-Dimethyl-3-[[methyl-[2-[methyl-[[1-[4-(trifluoromethyl) phenyl] indol-3-yl] methyl] amino] ethyl] amino] methyl] chromen-4-one, PubChem ID: 101506407) that bound to the crystal structure of TNF-α (PDB ID: 2AZ5) by making interactions with Gly 121, Tyr 119, Leu 57, Tyr 59, and Tyr 151 residues of the binding site. Similarly, the shortlisted ligands of SVKT showed interactions with the above-mentioned residues along with the other residues of the binding site which include Ser 60, Leu 120, and Gln 61 (Figure 2). The α-Lactose formed three hydrogen interactions with Gln 61, two with Tyr 151, and an interaction with each Leu 120, Ser 60, and Tyr 119 with a minimum BE of -7.851 kcal/mol (Table 3). Secondly, 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol with a minimum BE of -6.636kcal/mol able to form two hydrogen interactions with Leu 120 and an interaction with Tyr 151. Additionally, it also made a couple of interactions with each of the Leu 57 and Tyr 59 residues of TNF-α. D-allose compound documented with least BE of -5.65 kcal/mol among the three compounds however, it succeeded to make a couple of interactions with Tyr 151 and an interaction with each of Gly 121 and Ser 60 residues of TNF-α (Table 3).

Table 3:  Interaction profiles of top three constituents of SVKT that showed better BE and interactions with TNF-α binding site.

Figure 2: Displaying the interactions made by the shortlisted phytoconstituents of SVKT with the binding site of TNF-α. Light and dark green lines: hydrogen interactions (< 5 Å distance); purple & pink lines: hydrophobic interactions.

The ADMET analysis of shortlisted ligands from SVKT:

When IL-17A and TNF-α 3D protein structures were subjected to molecular docking with the ligands of SVKT, four ligands (2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol, α-Lactose, (3S,8aS)-3-(2-methylpropyl)-2,3,6,7,8,8a-hexahydropyrrolo[1,2-a] pyrazine-1,4-dione, N1-(m-Tolyl)-N2-(tetrahydrofurfuryl) oxamide) with IL-17A and three ligands (2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol, α-Lactose, D-Allose) with TNF-α showed superior interactions and those were shortlisted. The ADMET prediction analysis revealed that among the shortlisted ligands, 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol and α-Lactose showed superior interactions both with IL-17A and TNF-α. Except for α-Lactose, the other three shortlisted ligands of SVKT met Lipinski’s rule of five (Table 4). It is predicted that none of the shortlisted ligands showed favourable scores for kinase inhibition. However, 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol and α-Lactose showed favourable scores for enzyme inhibition, whereas (3S,8aS)-3-(2-methylpropyl)-2,3,6,7,8,8a-hexahydropyrrolo[1,2-a] pyrazine-1,4-dione showed scores in favour of protease inhibition (Table 4). All shortlisted ligands exhibited favourable bioavailability scores except α-Lactose (Table 4). 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol was predicted to have high gastrointestinal absorption also high plasma protein binding ability over the other three ligands. The (3S,8aS)-3-(2-methylpropyl)-2,3,6,7,8,8a-hexahydropyrrolo[1,2-a] pyrazine-1,4-dione and N1-(m-Tolyl)-N2-(tetrahydrofurfuryl) oxamide were predicted to have BBB crossing ability (Table 5). Further, these two ligands are forecasted to have a high renal clearance rate over the other two ligands (Table 5). All shortlisted ligands are predicted to have shorter half-lives as per the t ½ scores (Table 5). The highest LD50 value was documented for 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol whereas the lowest was for α-Lactose (Table 5). In addition, D-Allose that showed interactions with TNF-α fulfilled Lipinski’s rule of five, predicted to have acceptable bioavailability and low plasma protein binding ability. However, it showed poor gastrointestinal absorption ability. Further, the scores of renal clearance and half-life revealed a low clearance rate and shorter half-life. Interestingly, the D-Allose was noted to be the safest amongst all shortlisted ligands against both proteins together as it has the highest LD50 value.

Table 4: Physicochemical properties, fulfilment of Lipinski’s rule of five and bioavailability prediction of shortlisted ligands from SVKT

Ion channel modulator

Protease inhibitor

Note: TPSA=Topological polar surface area, Lipinski’s rule – MW ≤ 500, M logP - ≤ 4.15, No of H bond acceptor - ≤ 10, No of H bond donor - ≤ 5 (If two properties are out of range a poor absorption or permeability is possible, one is acceptable). Cut-off value for possible bioactivity is ≥ 0.5, GPCR - G protein-coupled receptors

Table 5: Permeability, absorption, distribution, excretion and toxicity predictions of shortlisted ligands from SVKT

Note: MolLogP - Logarithm of partitioning coefficient between n-octanol and water phases (0-3), Mol LogS –Logarithm of aqueous solubility (-4 – 0.5logmoL/L), BBB - (blood–brain barrier) permeation, PPB - Plasma Protein Binding (optimal <90%), CaCO2 – human colon adenocarcinoma cell line (optimal >-5.15 log unit), Cl – Clearance

(>15mL/min/kg – High, 5-15mL/min/kg – moderate, <5mL/min/kg - low), T1/2 – Half-life ( >3 long half-life, <3 short half-life)

DISCUSSION:

The current study was intended to identify the bioactive compounds present in SVKT and to evaluate the interaction and inhibitory ability against two key proteins of inflammatory pathway using computational tools. SVKT is a polyherbal formulation composed of 3 herbs, Indigofera aspalathoides, Celastrus paniculatus, and Corallocarpus epigaeus. I. aspalathoides had shown significant pharmacological activities like free radical scavenging activity¹², antifungal¹³, antioxidant, antibacterial¹⁴, antiproliferative, anticancer¹⁵ and neuroprotective properties¹⁶. C. paniculatus had showed anti-inflammatory, antinociceptive¹⁷, neuroprotective¹⁸, anxiolytic¹⁹ and anticonvulsant properties²⁰. C. epigaeus had revealed antioxidant, anti-inflammatory²¹, neuroprotective²², analgesic and anti-arthritic properties²³. In Siddha medicine, the plant Indigofera aspalathoides is prescribed for psoriasis, wounds, eczema, ulcers, burns, and boils²⁴. 

Psoriasis pathogenesis involves dynamic interactions between several cell types and cytokines²⁵. Psoriasis is initiated by multiple mechanisms that may include trauma, infection, medications like beta blockers, NSAIDS (non-steroidal anti-inflammatory drugs), Antimalarials, tetracyclines²⁶ leading to the release of antimicrobial peptides (AMPs) such as LL-37. This LL-37 forms a complex with self-DNA and RNA and activates plasmacytoid dermal dendritic cells(pDCs)^27,28. Further, pDCs secrete TNF-α, type I interferons (IFN), IL-6, IL-1β and activate myeloid dendritic cells (mDCs) to promote T cell-mediated inflammation. Cytokines are released by activated mDC, promoting resident T cell differentiation into Th1, Th17, and Th22 cells. These effector T cells secrete cytokines and thus stimulate keratinocytes and recruit inflammatory cells such as neutrophils by releasing chemokines²⁹.

Two inflammatory proteins IL-17A and TNF-α play major roles in various inflammatory conditions including psoriasis. In the current study the phytocompounds obtained from GCMS analysis were subjected to molecular docking and four constituents were shortlisted that showed favourable interactions with the binding site of IL-17A protein, and three constituents exhibited promising interactions with the binding surface of TNF-α protein (Table 2 and 3). It is also noted that two (2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol and α-Lactose) among the shortlisted constituents can inhibit both proteins as they exhibited multiple interactions with both proteins. Several researchers have studied the druggable potential of several small molecules against IL-17A and TNF-α structures using computational strategies. Khaledi et al³⁰, in their study, mentioned several terpenoid-derived metabolites from marine sponges that showed inhibitory potential against IL-17A protein with minimum BE varying between -5.7 to - 6.8kcal/mol³⁰. In the present study, the four shortlisted compounds from SVKT exhibited their inhibitory potential with BE varying between -8.2 to - 6.6kcal/mol. The Leu 97, Leu 99, Leu 112, Tyr  62, Gln 94, and Glu 95 were common interacting residues in the above mentioned and the current study. The other similarity between the current study and the above-mentioned study is that they both exhibited the highest number of interactions with Leu 97 residue of the IL-17A protein binding site.

There were nine residues identified to which the native ligand formed hydrogen and hydrophobic interactions. All three shortlisted compounds from SVKT displayed the combination of hydrogen and hydrophobic interactions with seven residues in the binding pocket of the TNF-α protein. (Figure 2). The common interacting residues were Ser 60, Gly 121, Glu 61, Leu 120, Tyr 59, Tyr 121, and Tyr 151. A study by Zia et al³¹ showcased the inhibitory ability of small molecules identified through pharmacophore modelling from plant origin natural product database and the BE was varying from -8.4 to -7.0kcal/mol. Whereas, the shortlisted compounds of SVKT formed interactions with BE ranging from -7.8 to -5.6kcal/mol. The difference between these two studies is that the compounds from SVKT did not show any interactions with Leu 57 whereas, compounds 1 to compound 4 in the above-mentioned study have made interactions with Leu 57³¹. Another study by Shivaleela et al³² exhibited the inhibitory potential of thalidomide-based compounds against TNF-α protein with binding energies ranging from -7.1 to -5.3 kcal/mol³². They also displayed interactions with similar residues of the binding interface of TNF-α protein like native ligands and shortlisted ligands of SVKT.

Overall, all shortlisted components of SVKT fulfilled Lipinski’s rule of five except α-Lactose. Nevertheless, these compounds were predicted not to have kinase inhibitory activity could have become an advantage for these compounds. However, 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol, and α-Lactose were expected to have enzyme inhibition capacity as their bioactivity scores for enzyme inhibition were above 0.5 (Table 4). As per this observation, it can be hypothesised that constituents of SVKT might inhibit only IL-17A and TNF-α proteins but not interfere with Janus-kinase phosphorylation. The ADMET analysis revealed that except for 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol, others have shown a low gastrointestinal absorption rate. Hence their absorption rate needs to be improved further using lead hopping methods in the future. None of the shortlisted constituents were shown the ability to cross BBB and it is an advantage and does not cause any kind of central side effects when they are administrated. Only 2-(hydroxymethyl)-6-octylsulfanyloxane-3,4,5-triol was predicted to have high protein binding ability, and the rest four ligands showed low protein binding ability evidenced by their low PPB scores. Except for α-Lactose, other compounds were predicted to have high LD50 doses and safer compounds (Table 5).

Our study clearly indicates that the shortlisted bioactive compounds present in SVKT were able to interact and form complexes and could inhibit the inflammatory actions of the target proteins IL-17A and TNF-α that are mainly involved in psoriasis, and this provides an insight on how SVKT can be a source of potential antipsoriatic agents. However further validation of this activity is required by using in-vitro and in-vivo methods to explore their molecular mechanisms and to ascertain their efficacy. 

CONCLUSION:

In conclusion, the GCMS analysis of SVKT revealed that it is a source of several phytoconstituents. Further, molecular docking and ADMET analysis disclosed excellent binding affinity of shortlisted compounds with active sites of the target proteins (TNF-α and IL-17A) suggesting that these molecules can be important inhibitors of TNF-α and IL-17A and play a major role in interrupting the pathogenesis of psoriasis. These in-silico findings to be validated using cell lines and animal models.

CONFLICT OF INERESTS:

The authors have no conflicts of interest.

ACKNOWLEDGEMENTS:

All authors are grateful to Sivasakthi Pharmaceutical Pvt Ltd, Coimbatore, Tamil Nadu, India for providing us the Siddha drug to carry out the research and to Analytical Research and Metallurgical Laboratories Pvt. Ltd. (ARML), Bengaluru, Karnataka, India for performing the GCMS analysis. Late Dr. Alex Joseph, Associate Professor, Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, MAHE, Manipal for his subject expertise and reviewing the manuscript.

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Accepted on 06.03.2024           © RJPT All right reserved

Research J. Pharm. and Tech 2024; 17(8):3707-3716.