Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In...

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Toxicology
• In Silico Modeling

Background:

• Physiologically Based Kinetic (PBK) models are crucial for predicting drug behavior in vivo.
• Accurate prediction of peak plasma concentrations (Cmax) is essential for drug development and risk assessment.
• Integrating in vitro and in silico data into PBK models can enhance predictive accuracy.

Purpose of the Study:

• To assess the predictive performance of a minimal generic rat PBK model for Cmax prediction after single oral dosing.
• To identify optimal combinations of in vitro and in silico methods for chemical parameterization within the PBK model.
• To compare PBK model predictions with reported in vivo pharmacokinetic data.

Main Methods:

• Development and application of a minimal generic rat PBK model.
• Generation of 3960 Cmax predictions for 44 compounds using various in vitro and in silico parameterization approaches.
• Comparison of predicted Cmax values against experimentally observed in vivo data.

Main Results:

• Best predictive performance was achieved using in vitro intrinsic clearance, Rodgers and Rowland partition coefficients, and in silico unbound fraction and Papp values.
• The model predicted the median Cmax within 10-fold for 32 out of 44 compounds, with 22 within 5-fold and 8 within 2-fold.
• Overestimations exceeding 10-fold occurred for 12 compounds; no significant underestimations were observed.

Conclusions:

• A minimal rat PBK model, parameterized with specific in vitro and in silico data, demonstrates valuable predictive capability for Cmax.
• The study highlights the importance of accurate parameterization, particularly for hepatic clearance and lipophilicity, in PBK modeling.
• These findings offer insights into the reliability of PBK models for early-stage pharmacokinetic assessments.