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

Abstract

ABSTRACT Introduction 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. Areas covered 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. Expert opinion 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. GRAPHICAL ABSTRACTThe diagram illustrates how next-generation mechanistic models can be achieved through the integration of complementary modeling approaches, such as VCBA, PBPK, QSP, and AOP-based models, interconnected through artificial intelligence architectures. VCBA shows the cell partitioning component with intracellular distribution among structural proteins, aqueous fractions, and lipids, with uptake and elimination fluxes. PBPK represents whole-body drug disposition through arterial and venous circulation across major organs. QSP connects signaling networks, subcellular toxicity pathways, clinical pharmacodynamic responses, and trial design. The AOP-based panel illustrates the hierarchical cascade from a chemical stressor through the molecular initiating event (MIE), key events (KE₁, KE₂), to an organ-level adverse outcome depicted as a liver icon. Below these models, seven categories of fragmented data sources are shown: physicochemical properties, in vitro assays, omics, biomarkers, ADME, toxicity, and clinical data. These data streams connect to the models above, representing the integration of fragmented evidence into a unified mechanistic framework.Diagram of next-generation mechanistic models: VCBA, PBPK, QSP, AOP-based, linked to fragmented data sources.