Optimization of Bottom‐Up PBPK Model Development in SIMCYP via Retrospective Analysis of Clinical Human PK Data

Physiologically-based pharmacokinetic (PBPK) modeling has become a major tool in drug discovery and development. Here, we describe the bottom-up PBPK modeling approaches employed at AbbVie using Simcyp Simulator and evaluate the impact of three system parameters, GI physiology, P-gp Relative Expression Factor (REF), and recombinant CYP enzyme (rCYP) intersystem extrapolation factor (ISEF), independently and in combination, on PBPK prediction performance through retrospective analysis of 8 clinical assets. Overall, the application of New GI physiology resulted in a considerable improvement in the prediction of oral absorption for most compounds compared to the Original GI physiology (C_max: 76% vs. 43% within 3-fold) when using the default P-gp REF (1.5) and adjusted ISEF. Decreasing P-gp REF to 0.5 resulted in additional improvement in the predictions of C_max for P-gp substrates (86% within 3-fold). The observed plasma exposure-time profiles and AUC_INF are better predicted using the default rCYP ISEF values instead of individually adjusted values (48% vs. 43% within 3-fold) when using the Original GI and default P-gp REF (1.5). The combination of optimized parameters (New GI physiology, P-gp REF of 0.5 and rCYP default ISEF) predicted the plasma exposures (AUC_INF and C_max) within 3-fold for 81% and 86% of the tested simulations, respectively. In conclusion, the present study proposes an optimized strategy for bottom-up PBPK model development in Simcyp Simulator. Retrospective comparison with observed clinical PK data is vital for model verification as well as further improvement in prospective predictions for future drug candidates.