Analisis In Silico Molecular Initiating Events Remdesivir sebagai Dasar Identifikasi Jalur Toksisitas

Authors

  • Yuneka Saristiana INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
  • Nur Fahma Laili INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
    Competing Interests

    Farmasi

  • Fendy Prasetyawan INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
    Competing Interests

    Farmasi

  • Neni Probosiwi INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
    Competing Interests

    Farmasi

  • Tsamrotul Ilmi INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
    Competing Interests

    Farmasi

  • Novyananda Salmasfattah INDONESIA Prodi Pendidikan Profesi Apoteker, Fakultas Ilmu Kesehatan, Universitas Kadiri
    Competing Interests

    Farmasi

DOI:

https://doi.org/10.64595/jmpb.122026.734

Keywords:

Remdesivir; Molecular Initiating Events; toksikologi komputasi; ProTox-3.0; in silico.

Abstract

Remdesivir merupakan obat antivirus yang banyak digunakan dalam terapi Coronavirus Disease 2019 (COVID-19). Meskipun memiliki efektivitas yang baik dalam menghambat replikasi virus, evaluasi terhadap mekanisme awal toksisitasnya masih diperlukan untuk mendukung aspek keamanan penggunaan obat. Penelitian ini bertujuan menganalisis Molecular Initiating Events (MIE) Remdesivir menggunakan pendekatan in silico sebagai dasar identifikasi jalur toksisitas. Penelitian dilakukan secara deskriptif eksploratif dengan memanfaatkan platform ProTox-3.0. Struktur molekul Remdesivir dalam format SMILES digunakan sebagai data masukan untuk memprediksi target molekuler yang berperan sebagai MIE. Parameter yang dianalisis meliputi status aktivitas target dan probabilitas prediksi. Hasil penelitian menunjukkan bahwa seluruh target molekuler yang dianalisis, yaitu THRα, THRβ, TTR, RYR, GABAR, NMDAR, AMPAR, KAR, AChE, CAR, PXR, NADH-quinone oxidoreductase, VGSC, dan NIS diprediksi berada pada status Inactive dengan probabilitas 0,55–1,00. Probabilitas Inactive tertinggi diperoleh pada CAR (1,00), sedangkan terendah pada PXR (0,55). Temuan ini menunjukkan bahwa Remdesivir memiliki kecenderungan rendah untuk menginisiasi jalur toksisitas melalui target-target MIE yang dianalisis. Penelitian ini menyimpulkan bahwa pendekatan computational toxicology menggunakan ProTox-3.0 mampu memberikan informasi awal mengenai profil keamanan Remdesivir dan berpotensi menjadi metode skrining awal dalam identifikasi mekanisme toksisitas obat.

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References

1. Tutone M, Almerico AM. Computational strategies reshaping modern drug discovery. Molecules. 2026;31(2):200. https://doi.org/10.3390/molecules31020200 DOI: https://doi.org/10.3390/molecules31020200

2. Vasava P, Pérez-Santín E, Rodríguez Solana R, et al. Toxicity prediction based on artificial intelligence: a review. Drug Discov Today. 2023;28(7):103372. https://doi.org/10.1016/j.drudis.2023.103372

3. Amorim AMB, Piochi LF, Gaspar AT, Preto AJ, Rosário-Ferreira N, Moreira IS. Advancing drug safety in drug development: bridging computational predictions for enhanced toxicity prediction. Chem Res Toxicol. 2024;37(6):827–49. https://doi.org/10.1021/acs.chemrestox.3c00352 DOI: https://doi.org/10.1021/acs.chemrestox.3c00352

4. Ripa JD, Ali S, Field M, Smithson J, Wangchuk P. From AI-assisted in silico computational design to preclinical in vivo models: a multi-platform approach to small molecule anti-IBD drug discovery. Pharmaceuticals. 2025;18(10):1536. https://doi.org/10.3390/ph18101536 DOI: https://doi.org/10.3390/ph18101536

5. Khalifa HO, Al Ramahi YM. After the hurricane: anti-COVID-19 drugs development, molecular mechanisms of action and future perspectives. Int J Mol Sci. 2024;25(2):739. https://doi.org/10.3390/ijms25020739 DOI: https://doi.org/10.3390/ijms25020739

6. Temiz-Arpaci O, Ergoren MC, Manav-Agladioglu S, Nalbantoglu OU. A comprehensive review of remdesivir: clinical trials, applications, limitations, and future perspectives. Curr Pharm Des. 2023;29(25):1986–2001. https://doi.org/10.2174/1381612829666230721145603

7. Chiotos K, Hayes M, Kimberlin DW, Jones SB, James SH, Pinninti SG, et al. Multicenter initial guidance on use of antivirals for children with COVID-19/SARS-CoV-2 and remdesivir effectiveness in a real-world setting. J Pediatric Infect Dis Soc. 2024;13(1):1–11. https://doi.org/10.1093/jpids/piad106 DOI: https://doi.org/10.1093/jpids/piad106

8. Farsi Y, Tahvildari A, Arbabi M, Vaziri Z, Balistani B, Rahimian N, et al. Adverse drug events associated with remdesivir in real-world hospitalized patients with COVID-19, including vulnerable populations: a retrospective multicenter study. J Korean Med Sci. 2023;38(38):e346. https://doi.org/10.3346/jkms.2023.38.e346 DOI: https://doi.org/10.3346/jkms.2023.38.e346

9. Meri M, Alander L, Salmela B, Raiskila M, Lappalainen M, Juvonen T, et al. Remdesivir: effectiveness and safety in hospitalized COVID-19 patients—analysis of retrospectively collected data from daily practice in the Omicron variant era and comparison with the pre-Omicron period. Viruses. 2025;17(6):793. https://doi.org/10.3390/v17060793 DOI: https://doi.org/10.3390/v17060793

10. Zhang J, Li H, Zhang Y, Huang J, Ren L, Zhang C, et al. Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction. Brief Bioinform. 2025;26(5):bbaf533. https://doi.org/10.1093/bib/bbaf533 DOI: https://doi.org/10.1093/bib/bbaf533

11. Bai H, Ma T, Ma R, Li J, Wang Y, Zhou P, et al. Machine learning-enabled drug-induced toxicity prediction. Adv Sci. 2025;12(22):e2413405. https://doi.org/10.1002/advs.202413405 DOI: https://doi.org/10.1002/advs.202570112

12. Jeong J, Gasparyan M, Choi J. Advancing the quantitative understanding of adverse outcome pathways: current status, methodologies, and future directions. Environ Toxicol Chem. 2025;44(3):614–23. https://doi.org/10.1093/etojnl/vgae063 DOI: https://doi.org/10.1093/etojnl/vgae063

13. Crofton KM, Paparella M, Price A, Mangas I, Martino L, Terron A, et al. A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE). EFSA J. 2024;22(8):e8954. https://doi.org/10.2903/j.efsa.2024.8954 DOI: https://doi.org/10.2903/j.efsa.2024.8954

14. Ngo A, Mangu VR, Patel R. AOP Network Box: an integrative framework for bridging adverse outcome pathways and biological networks. Chem Res Toxicol. 2026;39(3):412–25. https://doi.org/10.1021/acs.chemrestox.6c00041 DOI: https://doi.org/10.1021/acs.chemrestox.6c00041

15. Masarone S, Beckwith KV, Wilkinson MR, Tuli S, Lane A, Windsor S, et al. Advancing predictive toxicology: overcoming hurdles and shaping the future. Digit Discov. 2025;4:303–15. https://doi.org/10.1039/D4DD00257A DOI: https://doi.org/10.1039/D4DD00257A

16. Ru J, Sun H, Pan L, Xue Y, Wang J. In silico methods for drug-target interaction prediction. Cell Rep Methods. 2025;5(9):101432. https://doi.org/10.1016/j.crmeth.2025.101432 DOI: https://doi.org/10.1016/j.crmeth.2025.101184

17. Kania MK, Zadora PK, Rogala MK. Application of bioinformatics tools in network pharmacology and in silico research: SwissTargetPrediction, PASS Online, STRING, GeneCards, and CTD for anti-inflammatory compound analysis. Comput Biol Med. 2025;178:108687. https://doi.org/10.1016/j.compbiomed.2025.108687

18. Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024;52(W1):W513–20. https://doi.org/10.1093/nar/gkae303 DOI: https://doi.org/10.1093/nar/gkae303

19. Lee H, Kim J, Kim JW, Lee Y. Recent advances in AI-based toxicity prediction for drug discovery. Front Chem. 2025;13:1632046. https://doi.org/10.3389/fchem.2025.1632046 DOI: https://doi.org/10.3389/fchem.2025.1632046

20. Roman DL, Roman M, Isvoran A. Computational assessment of pharmacological profiles and toxicity of bioactive compounds: a comparative study using multiple in silico platforms. Toxics. 2024;12(9):621. https://doi.org/10.3390/toxics12090621 DOI: https://doi.org/10.3390/toxics12090621

21. Guo Y, Huang W, Zhu K, Huang L, Zhang J. Remdesivir in COVID-19: pros and cons. Front Pharmacol. 2026;17:1731244. https://doi.org/10.3389/fphar.2026.1731244 DOI: https://doi.org/10.3389/fphar.2026.1731244

22. Umemura T, Kato H, Mutoh Y, Hagihara M, Ikeda Y, Mikamo H. Safety evaluation of remdesivir administration in patients with severe renal impairment and coronavirus disease: a systematic review and meta-analysis. BMC Infect Dis. 2025;25:780. https://doi.org/10.1186/s12879-025-11153-5 DOI: https://doi.org/10.1186/s12879-025-11153-5

23. Braga RC, Alves VM, Muratov EN, Andrade CH, Tropsha A. Machine learning in toxicological sciences: opportunities for assessing drug toxicity. Front Drug Discov. 2024;4:1336025. https://doi.org/10.3389/fddsv.2024.1336025 DOI: https://doi.org/10.3389/fddsv.2024.1336025

24. Idakwo G, Luo M, Chen M, Hong H, Zhang C. AI-based toxicity prediction in drug discovery: recent advances, challenges, and future perspectives. J Chem Inf Model. 2023;63(20):6198–211. https://doi.org/10.1021/acs.jcim.3c00200 DOI: https://doi.org/10.1021/acs.jcim.3c00200

25. Madden JC, Pawar G, Cronin MTD, et al. Advancing human health risk assessment: the role of new approach methodologies. Front Toxicol. 2025;7:1632941. https://doi.org/10.3389/ftox.2025.1632941 DOI: https://doi.org/10.3389/ftox.2025.1632941

26. Rosolowski J, Weber T, Malakpour-Permlid A, Oredsson S. Revisiting 3Rs: rethinking replacement and new approach methodologies. Front Toxicol. 2025;7:1664209. https://doi.org/10.3389/ftox.2025.1664209 DOI: https://doi.org/10.3389/ftox.2025.1664209

27. Jain S, Manganelli S, Gryshkova V, Rodrigues MA, Magarkar A. Methods in predictive toxicology 2023. Front Pharmacol. 2025;16:1556352. https://doi.org/10.3389/fphar.2025.1556352 DOI: https://doi.org/10.3389/fphar.2025.1556352

28. Wu L, Yan B, Han J, Li R, Xiao J, He S, et al. TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res. 2023;51(D1):D1432–45. https://doi.org/10.1093/nar/gkac1074 DOI: https://doi.org/10.1093/nar/gkac1074

29. Gianquinto E, Aldegahi M, Cole DJ, Guedes RC, Wade RC, Cavalli A, et al. Toward integrative predictive toxicology: advanced methods for drug toxicity and safety prediction. WIREs Comput Mol Sci. 2026;16(1):e70065. https://doi.org/10.1002/wcms.70065 DOI: https://doi.org/10.1002/wcms.70065

30. Flynn NR, Miller GP, Swamidass SJ. Advancements in computational studies of drug toxicity. Front Pharmacol. 2023;14:1230409. https://doi.org/10.3389/fphar.2023.1230409 DOI: https://doi.org/10.3389/fphar.2023.1230409

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2026-08-01

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Vol. 1 No. 2 (2026): Journal of Marine Pharmacy and Biomedicine

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How to Cite

Analisis In Silico Molecular Initiating Events Remdesivir sebagai Dasar Identifikasi Jalur Toksisitas. (2026). Journal of Marine Pharmacy and Biomedicine, 1(2). https://doi.org/10.64595/jmpb.122026.734

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