Why next-generation mechanistic models will transform drug discovery: integrating efficacy and safety

Drug discovery remains constrained by high attrition rates and the fragmented evaluation of exposure, efficacy, and safety. Mechanistic models offer a biologically grounded framework for connecting these determinants across multiple levels of biological organization. This may help improve translational decision-making by supporting earlier and more integrated assessment of candidate progression.

This narrative review examines the conceptual basis and current role of next-generation mechanistic models in drug discovery, with emphasis on physiologically based pharmacokinetic models, virtual cell-based assays, quantitative systems pharmacology, artificial intelligence (AI)-augmented mechanistic models, and emerging virtual-cell frameworks. It highlights how these approaches may connect efficacy and safety across biological scales, support in vitro-to-in vivo extrapolation, incorporate in silico predictions, and improve candidate prioritization. The literature was surveyed through PubMed searches conducted up to 25 May 2026.

Next-generation mechanistic models are unlikely to transform drug discovery simply by increasing biological detail or computational sophistication. Progress in this direction will depend on standardized data streams, robust validation, explicit model calibration, reproducibility, tighter integration between models, and careful alignment between model design and context of use. Under these conditions, mechanistic frameworks may become important components of a more predictive and less attrition-prone drug discovery pipeline.