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.