Studi In Silico Profil ADMET Senyawa Aktif Tanaman Telang (Clitoria ternateae L.)

Authors

  • Muhammad Algif Qolbun Salim Prodi Farmasi, Fakultas Farmasi dan Sains, Universitas Islam Bandung, Indonesia
  • Taufik Muhammad Fakih Prodi Farmasi, Fakultas Farmasi dan Sains, Universitas Islam Bandung, Indonesia
  • Ibnu Dharsono Faizal Prodi Farmasi, Fakultas Farmasi dan Sains, Universitas Islam Bandung, Indonesia

DOI:

https://doi.org/10.29313/bcsp.v6i2.25037

Keywords:

Clitoria ternatea, ADMET, In silico

Abstract

Abstract. Clitoria ternatea L. is a medicinal plant containing various bioactive compounds with potential for drug development. This study aimed to evaluate the ADMET profile of Clitoria ternatea L. active compounds using an in silico approach. Active compounds were identified through the Knapsack database, and their chemical structures were analyzed using SwissADME to predict physicochemical properties, pharmacokinetics, drug-likeness, and medicinal chemistry parameters. Toxicity profiles were subsequently assessed using ProTox-II. The results indicated that most compounds exhibited favorable physicochemical characteristics, good gastrointestinal absorption, and acceptable drug-likeness profiles, suggesting their potential as oral drug candidates. Toxicity prediction showed that the selected compound had an LD50 value of 5000 mg/kg and belonged to toxicity class 5. It was predicted to be non-hepatotoxic, non-neurotoxic, non-mutagenic, non-carcinogenic, non-immunotoxic, and non-cytotoxic, although a potential nephrotoxic effect was identified. These findings suggest that in silico analysis is a useful preliminary screening tool for identifying promising drug candidates from Clitoria ternatea L.; however, further in vitro and in vivo studies are required to confirm their efficacy and safety. 

Abstrak. Clitoria ternatea L. merupakan tanaman herbal yang mengandung berbagai senyawa bioaktif dan berpotensi dikembangkan sebagai kandidat obat. Penelitian ini bertujuan menganalisis profil ADMET senyawa aktif Clitoria ternatea L. menggunakan pendekatan in silico. Identifikasi senyawa dilakukan melalui basis data Knapsack, kemudian struktur senyawa dianalisis menggunakan SwissADME untuk mengevaluasi sifat fisikokimia, farmakokinetik, drug-likeness, dan medicinal chemistry. Selanjutnya, profil toksisitas diprediksi menggunakan ProTox-II. Hasil analisis menunjukkan bahwa sebagian besar senyawa memiliki karakteristik fisikokimia yang mendukung pengembangan sebagai kandidat obat oral, dengan prediksi absorpsi gastrointestinal yang baik dan memenuhi parameter drug-likeness. Analisis toksisitas menunjukkan bahwa senyawa yang dipilih memiliki nilai LD50 sebesar 5000 mg/kg dan termasuk kelas toksisitas 5, serta tidak diprediksi bersifat hepatotoksik, neurotoksik, mutagenik, karsinogenik, imunotoksik, maupun sitotoksik, meskipun terdapat potensi nefrotoksisitas yang memerlukan kajian lebih lanjut. Penelitian ini menunjukkan bahwa pendekatan in silico dapat digunakan sebagai metode penyaringan awal dalam mengidentifikasi senyawa aktif tanaman telang yang berpotensi dikembangkan sebagai kandidat obat, namun tetap memerlukan validasi melalui pengujian in vitro dan in vivo.

References

Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/NAR/GKY318
Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports 2017 7:1, 7(1), 42717-. https://doi.org/10.1038/srep42717
Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W364. https://doi.org/10.1093/NAR/GKZ382
Ferreira, L. L. G., & Andricopulo, A. D. (2019). ADMET modeling approaches in drug discovery. Drug Discovery Today, 24(5), 1157–1165. https://doi.org/10.1016/J.DRUDIS.2019.03.015
Jeyaraj, E. J., Lim, Y. Y., & Choo, W. S. (2020). Extraction methods of butterfly pea (Clitoria ternatea) flower and biological activities of its phytochemicals. Journal of Food Science and Technology, 58(6), 2054. https://doi.org/10.1007/S13197-020-04745-3
Kumar, A., Kini, S. G., & Rathi, E. (2021). A Recent Appraisal of Artificial Intelligence and In Silico ADMET Prediction in the Early Stages of Drug Discovery. Mini Reviews in Medicinal Chemistry, 21(18), 2788–2800. https://doi.org/10.2174/1389557521666210401091147
Lin, L., Yee, S. W., Kim, R. B., & Giacomini, K. M. (2015). SLC transporters as therapeutic targets: emerging opportunities. Nature Reviews. Drug Discovery, 14(8), 543–560. https://doi.org/10.1038/NRD4626
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1–3), 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
Mukherjee, P. K., Banerjee, S., Das Gupta, B., & Kar, A. (2022). Evidence-based validation of herbal medicine: Translational approach. Evidence-Based Validation of Herbal Medicine: Translational Research on Botanicals, 1–41. https://doi.org/10.1016/B978-0-323-85542-6.00025-1
Organization., W. H. (2019). WHO global report on traditional and complementary medicine 2019. World Health Organization, 1–228. https://apps.who.int/iris/bitstream/handle/10665/312342/9789241515436-eng.pdf?ua=1%0Ahttps://iris.who.int/handle/10665/312342
Panchabhai, Dr. V., Kalshetti, A., Shivankar, N., Belkunde, S., Atkare, K., Pinjare, V., Panchabhai, Dr. V., Kalshetti, A., Shivankar, N., Belkunde, S., Atkare, K., Pinjare, V., Panchabhai, Dr. V., Kalshetti, A., Shivankar, N., Belkunde, S., Atkare, K., & Pinjare, V. (2026). Clitoria ternatea L.: A review of its ethnobotany, phytochemistry and antidiabetic potential. International Journal of Pharmaceutical Sciences, 04(06), 6151–6158. https://doi.org/10.5281/ZENODO.20828149
Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2013). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395. https://doi.org/10.1124/PR.112.007336
Valerio, L. G. (2013). Predictive computational toxicology to support drug safety assessment. Methods in Molecular Biology (Clifton, N.J.), 930, 341–354. https://doi.org/10.1007/978-1-62703-059-5_15
van de Waterbeemd, H., & Gifford, E. (2003). ADMET in silico modelling: towards prediction paradise? Nature Reviews. Drug Discovery, 2(3), 192–204. https://doi.org/10.1038/NRD1032
Zanger, U. M., & Schwab, M. (2013). Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacology & Therapeutics, 138(1), 103–141. https://doi.org/10.1016/J.PHARMTHERA.2012.12.007

Published

2026-08-02