IN-SILICO STUDIES ON CHEMICAL CONSTITUENTS OF CALOTROPIS SPECIES

IN-SILICO STUDIES ON CHEMICAL CONSTITUENTS OF CALOTROPIS SPECIES

Mounika Tummalapalli *, V. V. Sowjanya Appala, Sanny Gundugolanu, Kavya Komarapu, M. Trinadha Rao and G. Nagamani

Department of Pharmaceutical Chemistry, Adarsa College of Pharmacy, G. Kothapalli, Andhra Pradesh, India.

ABSTRACT: Nowadays, the use of herbal drugs is tremendously increased due to the lesser number of side effects when compared to pure form of drugs. Herbal medicine is a complex compound with multiple synergistic mechanisms of action which modulate pathophysiological functions. Even though few herbs exhibit toxicity, they possess many therapeutic values and curative principles; one such herb is calotropis. It exists in three species that produce pharmacological effects: cardiovascular, anti-cancer, antimicrobial, anticonvulsant, and smooth muscle relaxant. In this research, 24 chemical constituents of all calotropis species were selected. The molecular properties, bioactivity scores, ADMET profile of 24 calotropis species chemical constituents were predicted using computational tools, including mol inspiration, Pre ADMET, and structures drawn using chem sketch. The results drawn from the study were compounds C₈[Syriogenin] and C₁₆[urosolic acid] exhibited good oral bioavailability, bioactivity scores, intestinal absorption, renal absorption, decreased metabolism rate by inhibiting CYP3A4 and less toxic by Ames test.

Keywords: Calotropis, Chem sketch, Mol inspiration, Pre ADMET, Syriogenin

INTRODUCTION: Herbal medicine is an interdisciplinary branch that deals with herbs with medicinal values related to various branches, including botany, medicinal plant research, phytochemistry, pharmacognosy, ayurveda, natural chemistry, agriculture science, Unani medicines, biotechnology and biochemistry ¹. Herbal medicine is defined as the use of plants to prevent and treat illness or achieve good health and the drugs and tinctures used. The major use of herbal medicines is for health promotion and therapy for chronic as opposed to life-threatening conditions. Traditional medicines are widely perceived as natural and safe, that is not toxic ².

Currently, herbs are applied to treat chronic and acute conditions and various ailments and problems such as cardiovascular disease, prostate problems, depression, and inflammation and to boost the immune system, to name but a few ³. Various herbs have different pharmacological actions, including antiulcer, anticonvulsant, irregular heartbeat, anti-inflammatory insomnia, anxiety, CNS depressant effect migraine, etc. Among all the most widely used herbs, herbs belonging to the family Asteracae, Solanaceae, Umbelliferace, and Laminacae exhibit diverse pharmacological properties ^4-5.

An in-silico study is one performed via stimulation on a computer. The various methods include data-based quantitative structure, activity relationship, pharmacophores, homology, models and other molecular modeling approaches, machine leading, data mining, and network analysis tools we use a computer. Such models have seen frequent use in discovering and optimizing novel molecules with affinity to a target, classifying absorption, distribution, metabolism, excretion, and toxicity properties, as well as physicochemical characterization ^{6, 7}. It provides a platform for screening the activity of potential therapeutics against the molecular targets, which helps to select the one with the highest potential activity for further in-vitro and in-vivo experiments focusing on only selected targets will reduce the cost for laboratory trail, that requires financial and human resources. In-silico studies are recently widely used to study the complexities in oxidative stress-induced pathology ^8-10. Based on the need for herbal medicines to treat various pathological conditions and the importance of computational tools in drug discovery, the work was carried out on one of such herbs belonging to the family Apocynaceae. The selected herb was Calotropis; it exhibits various pharmacological properties despite its toxicity due to its latex ^11-14. The study aimed to perform in-silico screening by predicting the physicochemical and pharmacokinetic properties of chemical constituents of Calotropis species.

METHODOLOGY:

Chemical Structures and Generation of Smiles Notation: Among all the Calotropis species, only 24 compounds are selected based on the availability of structures in Pubchem and journals. Structures of chemical constituents of Calotropis species about 24 (C₁-C₂₄) were drawn by using chem sketch and Chem Draw ultra 12.0 computational tools. SMILES compound (C₁-C₂₄) notations were generated using chem sketch ^{15, 16}.

Prediction of Molecular Properties and Bioactivity Scores: All the selected 24 chemical constituents of Calotropis species were predicted for their molecular properties and bioactivity scores by using an online web server (www. molinspiration.com) mol inspiration in-silico tool ¹⁷. By using SMILES notation of all the selected chemical constituents (C₁-C₂₄), molecular properties and bioactivity scores were predicted.

Molecular properties including molecular weight, topological polar surface area (TPSA), number of Nitrogen atoms, number of oxygen atoms, number of OH atoms, number of NH atoms, partition coefficient (miLogP), number of rotatable bonds, volume, number of violations. According to Lipinski rule of five. Molecular weight less than 500 Daltons, milogP less than or equal to 5, no. of Hydrogen bond donors less than or equal to 5, no. of hydrogen bond acceptor less than or equal to 10 indicates the chemical compound possesses good oral bioavailability.

Bioactivity scores SMILES notation of 24 compounds was used to predict bioactivity were predicted based on the activity scores of G-protein coupled receptor, ion channel modulator, kinase I, nuclear receptor ligand, protease I, and enzyme I. Bioactivity scores range from 0 to -0.5 indicating moderately active; bioactivity scores less than -0.5 indicates less activity; bioactivity score greater than 0, indicates highly active ^{17, 18}.

Prediction of ADMET Properties: ADMET includes absorption, distribution, metabolism, elimination properties and toxicity profile of all the 24 chemical constituents (C₁-C₂₄) of Calotropis species were predicted by using online web server (www.PreADMET.com) PreADMET ^19-22 and admet SAR ^21-23.

Prediction of Absorption, Distribution and Elimination Properties: Pre-ADMET uses various models of in-vivo and in-vitro prediction techniques to predict oral absorption. Basic models include PSA, rapid PSA, and other complex models. Predicting tissue distribution can promote the investigation of pharmacodynamics and toxicokinetics.

Distribution prediction is mainly related to the blood-brain barrier (BBB) permeability, apparent volume of distribution (VD), and plasma protein binding (PPB). Many of these models are developed based on the three-dimensional crystal structure of albumin, which can be used for docking studies to predict the binding of molecules to albumin. Quantitative structure-activity relationship (QSAR) models are developed based on the existing data of various ligands known to bind albumin.

These computational models can accurately predict the interaction of molecules with human serum albumin.

Prediction of Metabolism and Toxicity Profile: The metabolic process is a very complex process involving various enzyme activities and differs due to different genetic factors; we use different calculation models to predict the metabolism of some drugs. CYP enzyme affinity towards the ligands can be predicted. After carrying out the drug excretion/elimination prediction, the collected information must be integrated into the predictive model to provide a complete model to describe the substance behaviour at different stages of drug discovery and development.

Toxicity prediction with professional knowledge in computational toxicity. Groups perform QSAR modeling through the use of toxicity databases. Drug toxicity prediction can effectively reduce the need for animal testing. Pre-ADMET can predict both systemic toxicity and the toxicity of certain organs in addition to carcinogenicity and genotoxicity.

RESULTS AND DISCUSSION:

Chemical Structures and Generation of Smiles Notation: All 24 selected chemical compounds C₁-C₂₄ structures were drawn, and smiles notations were generated using the computational tool chem sketch. The data was presented in Table 1 and structures are displayed in Fig. 1.

FIG. 1: MOLECULAR STRUCTURES OF 24 COMPOUNDS

TABLE 1: CHEMICAL STRUCTURES OF COMPOUNDS C₁-C₂₄

Molecular Properties and Bioactivity Scores: The molecular properties and bioactivity scores of compounds C₁-C₂₄ were predicted busing molinspiration and results were represented in Table 2 and Table 3, respectively. All the compounds obeyed Lipinski rule of five except C₄. The molecular weight of all compounds ranges from 115 to 665 Daltons, mi Log P values range from -0.46 to 8.79 and no violations indicate all the derivatives exhibited good oral bioavailability and drug-like properties. The bioactivity data revealed that all the chemical constituents were moderately active as enzyme and kinase inhibitors, inactive as ion channel modulators, and few chemical constituents displayed moderate scores towards protease inhibition, nuclear receptor inhibition, and GPCR ligand. Compounds C_13, C_18, C₂₄ showed less bioactivity scores remaining compounds exhibited moderate to high scores ranging from -0.48 to 9.58.

TABLE 2: MOLECULAR PROPERTIES OF CHEMICAL CONSTITUENTS C₁-C₂₄ OF CALOTROPIS SPECIES

Mi Log P: lipophilicity, TPSA: topological polar surface area, n: number of, Mol. wt: molecular weight, vio: violations, rtbs: rotatable bonds.

TABLE 3: BIOACTIVITY SCORES OF COMPOUNDS C₁-C₂₄ OF CALOTROPIS SPECIES

GPCR: G-protein coupled receptors.

Absorption, Distribution and Elimination Properties: Absorption, distribution and elimination properties of all the 24 Compounds [C₁-C₂₄] were predicted by using PreADMET and data were listed in Table 4. Compound C₁-C₂₄ showed %HIA ranging from 75-100, CaCO2 cell permeability from 22-57, and skin permeability from 1.72-4.49 cm/s. All the compounds are strongly bound to about 35-100 % plasma protein. Some chemical constituents showed P-glycoprotein I ranging from 38-100 %. Compounds ­C_3, C_6, C_7, C_18, C₂₄ do not cross the blood-brain barrier [BBB] as their permeability is nearly 0.1; the compounds C₂, C₂₀, C₂₃, C₂₅ penetrate BBB.

TABLE 4: ABSORPTION, DISTRIBUTION AND ELIMINATION PROPERTIES OF COMPOUNDS C₁-C₂₄

MDCK- madin-darby canine kidney, HIA- Human intestinal absorption, P-gp- Plasma glycoprotein, I- Inhibitor, NI- Non-Inhibitor

Metabolism and Toxicity Profile: CYP450 enzymes metabolize most medications; the most important of these enzymes are CYP2C9, CYP2C19, and CYP3A4. Metabolism and toxicity of compound C₁-C₂₄ were reported in Table 5. All the compounds except C₆ acts as I for the enzyme CYP2C9, CYP2C19, CYP2D6 and CYP3A4, respectively and C₁, C₁₇, C₂₁ compounds displayed the all types of CYP2C,2D and 3A4 enzyme inhibition.

Mutagenicity was predicted to all the compounds by using the AMES test. Among all these 24 compounds C₁, C3, C₈, C₉, C₁₁, C₁₆, C₁₉, C₂₀, C₂₁, C₁₇ do not exhibited mutagenicity. All the constituents have low to medium risk associated with HERG inhibition.

TABLE 5: METABOLISM AND TOXICITY PROPERTIES OF COMPOUNDS C₁-C₂₄

CYP- cytochrome P 450 class of enzymes, LR-Low risk, MR- Medium risk.

CONCLUSION: The various computational tools used to predict molecular properties, Bioactivity scores, ADMET properties of the 24 chemical constituents [C₁-C₂₄] of calotropis species were selected based on their structure availability from pub chem databases and from articles published in journals. All the structures were drawn by using a chem sketch. Chemical constituents C₁-C₂₄ obeyed Lipinski’s rule of five and exhibited good oral bioavailability. All compounds displayed moderate to high bioactivity scores towards the GPCR ligand, ion channel modulator, kinase, and protease enzymes. Among all, only 19 compounds showed BBB penetration. Except C₃, C₄, C₁₀, C₁₂ remaining compounds are sensitive to MDCK, indicating good renal absorption. Mostly all the compounds C₁, C₂, C₅, C₈, C₉, C₁₁, C₁₃, C₁₄, C₁₅, C₁₆, C₁₉, C₂₀, C₂₂, C₂₃ possess 100% human intestinal absorption. All the compounds having CaCO₂ permeability and C₁, C₂, C₅, C₈, C₉, C₁₃, C₁₄, C₁₅, C₁₆, C₁₉, C₂₀,C₂₁, C₂₂, C₂₃ having 100% plasma protein binding. All the compounds except C₆, C₇, C₁₃, C₁₈ are P-gp inhibitors, indicating that the duration of action increases. Few compounds exhibited mutagenicity with AMES test and low to high risk produced with HERG inhibition.

ACKNOWLEDGEMENT: Authors are very thankful to the chairman P. Kanakaraju, co-ordinator P. Srinivas, Principal Dr. M. Trinadha Rao, Adarsa College of Pharmacy, G. Kothapalli for providing facilities to complete the work.

CONFLICTS OF INTEREST: Nil

REFERENCES:

1. Sharma A, Pooja S and Rima D: Herbal medicine -an introduction to its history. Herbal Medicine in Andrology 2021.
2. Hassan H M, Taha H M, Olayinka O, Idris H M, Omonike O S, Tosin Y A and Abimbola KO: Traditional herbal medicine: overview of research indexed in the scopus database. Advances in Traditional Medicine 2022.
3. Wachtel-galor S and Benzie IFF: Herbal medicine- An introduction to its history, usage, regulation, Current trends and research need. Boca raton (FL): CRC Presstaylor & Francis, Second Edition 2011.
4. Rochester U: A guide to common medicine: Newyork. UR Medicine 2022.
5. Zahid: Introduction and importance of medicinal plants and herbs: India. National Health Portam 2016.
6. Ekins S, Mestres and Testa B: In-silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. British J of Pharmacology 2009; 152(01): 9-20.
7. Mohammad M, Reza G and Ali N: A contemporary review on the important role of in-silico approaches for managing different aspects of covid-19 crisis. Informatics in Medicine Unlocked 2022; 28: 100862.
8. Sieburg HB: Physiological studies in-silico. Studies in the Sciences of Complexity1990; 12: 321-342.
9. Danchin A, Medigue C, Gascuel O, Soldano H and Henaut A: From data banks to databases. Research in Microbiology 1992; 142: 913-916.
10. Danchin A and Quayle A Meyer: The Delphic Boat -what Genomes tell us. Harvard University Press: Cambridge, MA 2002; 422(6932): 564-564.
11. Suresh Kumar P, Suresh E and Kalavathy S: Review on a potential herb Calotropis gigantea (L.) R. Br. Scholar Academy Journal of Pharmacy 2013; 2(2): 135-143.
12. Sarkars, Raja Chakravarty and Ghosh A: Calotropis gigantea – A complete Busket of Indian traditional medicine. Int J of Pharma Res and Scie 2014; 02(1): 7-17.
13. Ali Esmail Al- Snafi: The constitutents and pharmacological properties of Calotropis procera – an overview. International Journal of Pharmaceutical sciences Review and Research 2015; 5(3): 259-275.
14. Abdulrahman MD: A systematic review on the biological evaluation of Calotropis procera (aiton) dryand. Future Journal of Pharmaceutial Sciences 2023; 9(16): 1-9.
15. Moradi Md and Reza Gol Md: A contemporary review on the important role of in-silico approaches for managing different aspects of COVID-19 crisis. Informatics in Medicine Unlocked 2022; 28: 100862.
16. Shashank Shekhar M, Neeraj Kumar, Pratap Singh H, Shashi R and Chandra Shekar S: In-silico pharmacokinetic, bioactivity and toxicity study of some selected anti-asthmatic agents. Inte J of Pharmaceutical Sciences and Drug Research 2018; 10(4): 278-282.
17. Nur Aziz, Mi-yeon Kim and Jae Youlcho: Anti-inflammatory effects of luteolin: A review of in-vitro and in-vivo and in-silico Journal of Ethnopharmacology 2018; 225: 342-358.
18. Sri Mounika, Mounika T, Vidya Rani M and Rajitha G: Design and in-silico studies of molecular properties and bioactivity and toxicity of N-(A-Cyano substituted cinnamoyl)-2- Biphenyl hydrazone Derivatives. Journal of Cardiovascular Disease Research 2021; 12(06): 584-593.
19. Radha R, Nivya P, Mounika T, Bharathi K and Bala Sree K: In-silico studies of heterocyclic derivatives as COX-2 inhibitors. International Journal of Current Sciences PUB 2022; 12(4): 2250-1770.
20. Lemessa E B, Gemechu B T, Kim G, Kwon O and Lee D: In-silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models. Scientific Reports 2022; 12: 1-19.
21. Vivek S, Ankush Y and Paratpar S: Molecularr docking and ADMET study of bioactive compounds of Glycyrrhiza glabra against main protease of SARS-CoV2. Materials Today: Proceedings 2020; 49: 2999-3007.
22. Sudha K, Rani S and Vishwanath BA: In-silico molecular docking and ADME /T analysis of some selected compounds of Lepisanthes tetraphylla (vahl) radlk against type 2 diabetes mellitus. International Journal of Biology, Pharmacy and Allied Sciences 2020; 9(8): 1963-1970.
23. Rahman MM, Islam MR and Akash S: In-silico investigation and potential therapeutic approaches of natural products for covid-19: computer-aided drug design prospective. Frontiers in Cellular and Infection Microbiology 2022; 12: 929430.
    

    ---

    How to cite this article:

    Tummalapalli M, Appala VVS, Gundugolanu S, Komarapu K, Rao MT and Nagamani G: In-silico studies on chemical constituents of Calotropis species. Int J Pharm Sci & Res 2023; 14(8): 3935-41. doi: 10.13040/IJPSR.0975-8232.14(8).3935-41.

    ---

    All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.