In Silico Analysis of Secondary Metabolites of Clerodendrum inerme as a Potential Antidiabetes Compounds

Fauzan Hilmy; Ahmad Shobrun Jamil; M Artabah Muchlisin

doi:10.18860/planar.v3i0.2472

Fauzan Hilmy Universitas Muhammadiyah Malang http://orcid.org/0009-0002-9145-5790
Ahmad Shobrun Jamil Universitas Muhammadiyah Malang
M Artabah Muchlisin Universitas Muhammadiyah Malang http://orcid.org/0000-0002-0257-4089

DOI: https://doi.org/10.18860/planar.v3i0.2472

Abstract

Clerodendrum inerme can potentially alleviate diabetes, but little is known about its molecular mechanisms. This study aimed to investigate the chemical compound of C. inerme and its molecular mechanism to treat diabetes. The KNApSAcK was used to find secondary metabolite of C. inerme. A screening was done to find compounds by estimating Absorption, Distribution, Metabolism, and Excretion (ADME) on the SwissADME. The SwissTargetPrediction tool connects predictions of target proteins from compounds that pass screening to various probable proteins and utilizing the StringDB to show the network between target proteins and associated diseases. After finding the target protein, continue docking the chemical compound to the target protein using PyRx with AutoDock 4.2.6. The result from StringDB found four chemical compounds ((Z)-3-Hexenyl beta-D-glucopyranoside, Rhodioloside, Sammangaoside B, Clerodermic acid) that can connected to 4 target proteins (DPP4, IL1B, PPARA, PPARG). According to the docking results, clerodermic acid has good protein binding properties with DPP4, IL1B, PPARA, PPARG, rammangaoside B with PPARG, and rhodioloside with DPP4. C. inerme contains clerodermic acid, rammangaoside B, and rhodioloside compounds, which can potentially treat diabetes mellitus.

References

Alfadul, H., Sabico, S. and Al-Daghri, N.M. (2022) ‘The role of interleukin-1β in type 2 diabetes mellitus: A systematic review and meta-analysis’, Frontiers in Endocrinology, 13(July), pp. 1–13. Available at: https://doi.org/10.3389/fendo.2022.901616
Daina, A. and Zoete, V. (2016) ‘A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules’, ChemMedChem, pp. 1117–1121. Available at: https://doi.org/10.1002/cmdc.201600182
Ferguson, D. and Finck, B.N. (2021) ‘Emerging therapeutic approaches for the treatment of NAFLD and type 2 diabetes mellitus’, Nature reviews. Endocrinology, 17(8), pp. 484–495. Available at: https://doi.org/10.1038/s41574-021-00507-z
Frkic, R.L., Richter, K. and Bruning, J.B. (2021) ‘The therapeutic potential of inhibiting PPARγ phosphorylation to treat type 2 diabetes.’, The Journal of biological chemistry, 297(3), p. 101030. Available at: https://doi.org/10.1016/j.jbc.2021.101030
Hairunnisa, H. (2019) ‘Sulitnya Menemukan Obat Baru di Indonesia’, Farmasetika.com (Online), 4(1), p. 16. Available at: https://doi.org/10.24198/farmasetika.v4i1.22517
Kar, P. et al. (2019) ‘The antioxidant rich active principles of Clerodendrum sp. controls haloalkane xenobiotic induced hepatic damage in murine model’, Saudi journal of biological sciences, 26(7), pp. 1539–1547. Available at: https://doi.org/10.1016/j.sjbs.2018.12.006
Kim, S. (2021) ‘Exploring Chemical Information in PubChem.’, Current protocols, 1(8), p. e217. Available at: https://doi.org/10.1002/cpz1.217
Lena, N. et al. (2023) ‘Analisis Jejaring Farmakologi Tanaman Jati Belanda (Guazuma ulmifolia Lamk.) Sebagai Imunomodulator’, Journal of Islamic Pharmacy, 8(1), pp. 1–6. Available at: https://doi.org/10.18860/jip.v8i1.20782.
Lin, Y., Wang, Y. and Li, P.F. (2022) ‘PPARα: An emerging target of metabolic syndrome, neurodegenerative and cardiovascular diseases’, Frontiers in Endocrinology, 13(December), pp. 1–13. Available at: https://doi.org/10.3389/fendo.2022.1074911
Muchlisin, M.A. et al. (2022) ‘PREDIKSI BIOAVAILABILITAS DAN INTERAKSI SENYAWA METABOLIT SEKUNDER BUAH PLUM (Prunus domestica) TERHADAP HMG-CoA REDUKTASE SECARA IN SILICO’, Journal of Pharmacopolium, 5(1), pp. 1–8. Available at: http://dx.doi.org/10.36465/jop.v5i1.875
Nistala, R. and Savin, V. (2017) ‘Diabetes, hypertension, and chronic kidney disease progression: role of DPP4.’, American journal of physiology. Renal physiology, 312(4), pp. F661–F670. Available at: https://doi.org/10.1152/ajprenal.00316.2016
Nogara, P.A. et al. (2015) ‘Virtual screening of acetylcholinesterase inhibitors using the Lipinski’s rule of five and ZINC databank’, BioMed research international, 2015. Available at: https://doi.org/10.1155/2015/870389
Oliver, J.C. (2015) Venny. An Interactive Tool for Comparing Lists with Venn’s Diagrams. Available at: https://bioinfogp.cnb.csic.es/tools/venny/index.html
Petersmann, A. et al. (2019) ‘Definition, Classification and Diagnosis of Diabetes Mellitus’, Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association, 127(S 01), pp. S1–S7. Available at: https://doi.org/10.1055/a-1018-9078
Setiabudy, R., Nafriadi and Instiaty (2016) Farmakologi dan Terapi Edisi 6. Ed.VI. Jakarta: Jakarta : FKUI.
Stelzer, G. et al. (2016) ‘The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses.’, Current protocols in bioinformatics, 54, pp. 1.30.1-1.30.33. Available at: https://doi.org/10.1002/cpbi.5
Szklarczyk, D. et al. (2021) ‘The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets’, Nucleic Acids Research, 49(D1), p. D605. Available at: https://doi.org/10.1093/nar/gkaa1074
Tanase, D.M. et al. (2020) ‘The Intricate Relationship between Type 2 Diabetes Mellitus (T2DM), Insulin Resistance (IR), and Nonalcoholic Fatty Liver Disease (NAFLD)’, Journal of diabetes research, 2020. Available at: https://doi.org/10.1155/2020/3920196
Webber, S. (2013) IDF Atlas 10th Edition 2021, Diabetes Research and Clinical Practice. 102 (2). p. 147-148. Available at: https://doi.org/10.1016/j.diabres.2013.10.013