The Complexity of Drug Development: Translational Value and Limitations of Computational ADMET Assays Applied to...

Background: This study evaluates the translational relevance of free computer-assisted ADMET platforms in drug discovery by comparing SwissADME and FAF-Drugs4 predictions with regulatory and curated reference data for approved anticancer drugs. Methods: Fourteen approved anticancer agents representing diverse chemical classes were analyzed using SwissADME and FAF-Drugs4. We compared predicted physicochemical, pharmacokinetic, and toxicity-related properties with information extracted from FDA- and EMA-approved product information, DrugBank, and PubChem. We evaluated the concordance for oral absorption, permeability, metabolic stability, and toxicity-related trends. Results: The platforms showed good concordance for broad descriptor-driven properties, particularly oral suitability and physicochemical trends. Strong agreement was observed for intravenously administered taxanes, which displayed unfavorable oral drug characteristics, and for several orally active small molecules with generally compatible profiles. Partial concordance was observed for compounds such as temozolomide, whose clinical behavior is influenced by factors not fully captured by descriptor-based models. Toxicity outputs were informative as early warning signals, with vandetanib showing the clearest alignment between predicted elevated risk and documented safety concerns. Conclusions: Free computational ADMET tools are valuable computer-assisted drug discovery resources for early triage, predictive toxicology, and prioritization of repositioning candidates, but they should complement rigorous experimental and clinical evaluation.