Human-Focused Multiparameter Optimization Scores for Rank Ordering Compounds during Early Drug Discovery: Validation of PBPK Models Based on Clinical PK Data

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug discovery to prioritize compounds that meet the desired pharmacokinetic (PK) profiles. We developed a generalized PBPK model using only early discovery in vitro data and validated it across 18 Genentech compounds without compound-specific fitting. The model effectively rank-ordered compounds based on hypothetical PK drivers of pharmacodynamics, including minimum and maximum unbound concentrations (Cminu and Cmaxu) and unbound area under the curve (AUCu). In contrast, ranking based on any single in vitro parameter alone was less predictive. Additionally, the model provided reasonable predictions of clinical PK parameters such as apparent clearance, volume of distribution, Cmax, AUCinf, and full concentration–time profiles. This work represents the first validation of clinical PK prediction using early discovery data in a bottom-up manner and demonstrates the potential of PBPK modeling as a multiparameter optimization tool to guide the selection and optimization of compounds in the early stages of drug discovery.