Optimization of ADMET Properties Prediction for Remdesivir, Favipiravir, and their Metabolites Elimination Profiles - Anita Purnamayanti; Suharjono Suharjono; Mahardian Rahmadi (Borneo Journal of Pharmacy, 2026)
In silico methods have become crucial for the rapid preliminary assessment of drug compound absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, particularly for vital antivirals such as remdesivir and favipiravir, early in the drug development process. This study aimed to predict the pharmacokinetic profiles of remdesivir, favipiravir, and their respective metabolites, explicitly focusing on their interactions within the unique anatomy and physiology of human elimination organs. Compound summaries from PubChem were computationally analyzed using the pkCSM, ProTox-II, and ADMETLab 3.0 platforms. These predictions were then critically evaluated in the context of established hepatic and renal elimination mechanisms. Favipiravir and its metabolites generally exhibited a favorable ADMET profile, characterized by good oral absorption, wide distribution, efficient metabolism, and rapid excretion, albeit with a slight potential for blood-brain barrier penetration. In contrast, remdesivir, its nucleotide metabolite, and favipiravir showed the highest predicted likelihood of inducing hepatotoxicity. Concerning renal toxicity, remdesivir, remdesivir monophosphate, and the active triphosphate forms of both remdesivir and favipiravir presented a notable risk. This elevated renal risk was primarily attributed to their predicted low renal clearances, potentially resulting from insufficient penetration across the negatively charged glomerular filtration barrier. In conclusion, favipiravir and its metabolites demonstrated a more desirable ADMET profile than remdesivir. These preliminary findings suggest a differential safety and pharmacokinetic landscape between the two antiviral agents. Future research should prioritize leveraging advanced AI-based ADMET platforms to simulate complex human organ functions more accurately, refining these predictive models, and guiding subsequent in vivo investigations.