Integrative Biomedical Research | Online ISSN  2207-872X
RESEARCH ARTICLE   (Open Access)

In Silico Determination of Tinospora cordifolia Phytochemicals as Potential DPP-4 Inhibitors for Type 2 Diabetes Management

Ebenezer A. Oni1*, Temitope Ogunmola2, Aminat A. Alowonle3, Damilola A. Omoboyowa1

+ Author Affiliations

Journal of Angiotherapy 8(12) 1-8 https://doi.org/10.25163/angiotherapy.81210119

Submitted: 09 October 2024  Revised: 22 December 2024  Published: 23 December 2024 

Abstract

Background: Despite existing treatments, the prevalence of Type 2 diabetes mellitus (T2D) continues to rise globally, underscoring the need for novel therapeutic strategies. Medicinal plants like Tinospora cordifolia have shown potential in traditional medicine for managing various ailments, including diabetes. This study investigates the antidiabetic potential of T. cordifolia phytochemicals by targeting dipeptidyl peptidase-4 (DPP-4), a key enzyme in glucose metabolism. Methods: A library of 141 bioactive compounds from T. cordifolia was compiled and their structures retrieved from PubChem. Ligand preparation was conducted using the Schrödinger Suite, and the crystallographic structure of DPP-4 (PDB ID: 2HHA) was prepared for docking. Molecular docking, pharmacophore modeling, MM/GBSA binding energy calculations, and QSAR modeling were performed to assess binding affinities and predict inhibitory activities. Additionally, the QSAR-Toxicity Estimation Software Tool (TEST) was used to evaluate the toxicity profiles of the hit compounds. Results: Molecular docking revealed that five T. cordifolia compounds exhibited higher binding affinities than the standard drug rosiglitazone, with saponarin showing the highest affinity (-10.40 kcal/mol). MM/GBSA calculations confirmed favorable binding free energies, with saponarin exhibiting a ΔG_bind of -44.22 kcal/mol. QSAR modeling predicted that saponarin, astragalin, and tinosinenside had better pIC50 values (5.593 µM, 5.593 µM, and 5.659 µM, respectively) than rosiglitazone (5.059 µM). Pharmacophore modeling identified tinosinenside as having the highest fitness score (0.942). Toxicity assessment indicated that while tinosinenside showed potential for bioaccumulation, other compounds demonstrated moderate toxicity profiles. Conclusion: The findings suggest that saponarin, tinosinenside, and astragalin are promising candidates for DPP-4 inhibition and could be developed as novel therapeutic agents for T2D management. Further in vitro and in vivo studies are recommended to validate these computational predictions and explore the clinical potential of these phytochemicals.

Keywords: Tinospora cordifolia, DPP-4 inhibitors, type 2 diabetes, molecular docking, In Silico

References

Balogun, T.A., Iqbal, M.N., Saibu, O.A, Akintubosun, M.O., Lateef, O.M., Nneka, U.C., Abdullateef, O.T. & Omoboyowa, D.A. (2021). Discovery of potential HER2 inhibitors from Mangifera indica for the treatment of HER2-Positive breast cancer: an integrated computational approach. J Biomol Struct Dynam, 39, 1–12. DOI: 10.1080/07391102.2021.1975570

Bishayi, B., Roychowdhury, S., Ghosh, S. & Sengupta, M. (2002). Hepatoprotective and immunomodulatory properties of Tinospora cordifolia in CCl4 intoxicated mature albino rats. J Toxicol Sci. 27(3),139-46. doi: 10.2131/jts.27.139. PMID: 12238138.

Bodun, D.S., Omoboyowa, D.A., Olofinlade, V.F., Ayodeji, A.O., Mauri, A., Ogbodo, U.C. & Balogun, T.A. (2025). In-silico-based lead optimization of hit compounds targeting mitotic kinesin Eg5 for cancer management. In Silico Pharmacology, 13,9  https://doi.org/10.1007/s40203-024-00300-6

Dhama, K., Sachan, S., Khandia, R., Munjal, A., Iqbal, H.M.N., Latheef, S.K., Karthik, K., Samad, H.A., Tiwari, R. & Dadar, M. (2017). Medicinal and Beneficial Health Applications of Tinospora cordifolia (Guduchi): A Miraculous Herb Countering Various Diseases/Disorders and its Immuno-modulatory Effects. Recent Pat Endocr Metab Immune Drug Discov, 10(2), 96-111. doi: 10.2174/1872214811666170301105101.

ECHA (2017). Guidance on information requirements and chemical safety assessment, Version 6.0. Chapter R.7a: Endpoint specific guidance. Helsinki: European Chemicals Agency. 

Elekofehinti, O.O. (2023). Computer-aided identification of bioactive compounds from Gongronema latifolium leaf with therapeutic potential against GSK3β, PTB1B and SGLT2. Informatics in Medicine Unlocked, 38, 101202. https://doi.org/10.1016/j.imu.2023.101202

Kwon, S., Bae, H. & Jo, J. (2019) Comprehensive ensemble in QSAR pre­diction for drug discovery. BMC Bioinf 20:521–530. https://doi.org/10.1186/s12859-019-3135-4

Macalalad, M.A.B. & Gonzales, A.A., (2023). In Silico Screening and Identification of Antidiabetic Inhibitors Sourced from Phytochemicals of Philippine Plants against Four Protein Targets of Diabetes (PTP1B, DPP-4, SGLT-2, and FBPase). Molecules, 28, 5301. https://doi.org/10.3390/molecules28145301

Martin, T. (2016). User’s guide for TEST (version 4.2) (Toxicity Estimation Software Tool): a program to estimate toxicity from molecular structure. EPA/600/R-16/058. Available from: https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-tes

Omoboyowa, D. A. (2024). Deciphering phosphodiesterase-5 inhibitors from Aframemum melegueta: computational models against erectile dysfunction. In Silico Pharmacology, 12, 101. https://doi.org/10.1007/s40203-024-00284-3

Omoboyowa, D.A. (2022). Exploring molecular docking with E-phar­macophore and QSAR models to predict potent inhibitors of 14-α-demethylase protease from Moringa spp. Pharmacol Res- Modern Chin Med 4,100147. https://doi.org/10.1016/j.prmcm.2022.100147

Omoboyowa, D.A., Agoi, M.D., Shodehinde, S.A., Saibu, O.A., & Saliu, J.A. (2023). Antidiabetes study of Spondias mombin (Linn) stem bark fractions in high-sucrose diet-induced diabetes in Drosophila melanogaster. Journal of Taibah University Medical Sciences, 18(4), 663e675. DOI: 10.1016/j.jtumed.2023.01.011

Pace, C.N., Scholtz, J.M. & Grimsley, G.R. (2014). Forces stabilizing proteins. FEBS Lett., 588(14), 2177-84. doi: 10.1016/j.febslet.2014.05.006.

Petoumenou, M.I., Pizzo, F., Cester, J., Fernández, A. & Benfenati, E. (2015). Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models. EnvironmentalResearch142(2015)529–534. DOI: 10.1016/j.envres.2015.08.008

Rajalakshmi, M., Eliza, J., Priya, C.E., Nirmala, A.K., & Daisy, P. (2009). Anti-diabetic properties of Tinospora cordifolia stem extracts on streptozotocin-induced diabetic rats. African Journal of Pharmacy and Pharmacology, 3, 171-180.

Roglic, G. (2016). WHO Global Report on Diabetes: A Summary. Int. J. Noncommun. Dis. 1, 3

Saini, J., Marino, D., Badalov, N., Vugelman, M. & Tenner, S. (2023). Drug-Induced Acute Pancreatitis: An Evidence-Based Classification (Revised). Clin Transl Gastroenterol. 14(8), e00621. doi: 10.14309/ctg.0000000000000621.

Sangeetha, M.K.. Priya, C.D. M. and Vasanthi, H.R. (2013). Anti-diabetic property of Tinospora cordifolia and its active compound is mediated through the expression of Glut-4 in L6 myotubes, Phytomedicine, 20, 3-4. https://doi.org/10.1016/j.phymed.2012.11.006.

Singh, R.P., Banerjee, S., Kumar, P.V., Raveesha, K.A. & Rao, A.R. (2006). Tinospora cordifolia induces enzymes of carcinogen/drug metabolism and antioxidant system, and inhibits lipid peroxidation in mice. Phytomedicine. 13(1-2),74-84. doi: 10.1016/j.phymed.2004.02.013.

Sripriya, N., Ranjith, K.M., Ashwin, K.N., Bhuvaneswari, S. & Udaya, P.N.K. (2019). In silico evaluation of multispecies toxicity of natural compounds, Drug and Chemical Toxicology, 44(5), 480-486 DOI: 10.1080/01480545.2019.1614023

Stanley, P., Prince, M. & Menon, V.P. (2000). Hypoglycemic and other related actions of Tinospora cordifolia roots in alloxan induced diabetic rats. J. Ethnopharmacol. 70,9-15. doi: 10.1016/s0378-8741(99)00136-1.

Sussman, N.B., Arena, V.C., Yu, S., Mazumdar, S. & Thampatty, B.P. (2003). Decision tree SAR models for developmental toxicity based on an FDA/TERIS database. SAR QSAR Environ Res. 14(2), 83-96. doi: 10.1080/1062936031000073126.

Tyagi, R., Singh, A., Chaudhary, K.K. & Yadav, M.K. (2022). Pharmacophore modeling and its applications, Editor(s): Dev Bukhsh Singh, Rajesh Kumar Pathak, Bioinformatics, Academic Press, Pp: 269-289, https://doi.org/10.1016/B978-0-323-89775-4.00009-2.

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