A novel strategy for physiologically based predictions of human pharmacokinetics

Area of Science:

• Pharmacokinetics
• Drug Development
• Computational Biology

Background:

• Developing accurate human pharmacokinetic (PK) prediction strategies is crucial for drug development.
• Physiologically based pharmacokinetic (PBPK) modeling and allometry (Dedrick approach) are key methods for PK prediction.

Purpose of the Study:

• To develop and evaluate a PBPK modeling strategy for predicting human pharmacokinetics.
• To compare the predictive accuracy of PBPK modeling against the Dedrick approach using preclinical data.

Main Methods:

• PBPK and Dedrick models were applied to 19 F. Hoffmann-La Roche compounds.
• Predicted pharmacokinetic parameters and plasma concentrations were compared with observed human data.
• A specific PBPK strategy was proposed and assessed for prospective prediction accuracy.

Main Results:

• The proposed PBPK strategy achieved prospective prediction for approximately 70% of compounds with high accuracy (e.g., 83% for oral clearance).
• For the remaining 30% of compounds, poor animal prediction accuracy precluded prospective human PK prediction.
• PBPK modeling generally provided more accurate predictions of PK parameters and profiles than the Dedrick approach.

Conclusions:

• PBPK modeling is a recommended approach for predicting human pharmacokinetics from preclinical data, demonstrating reasonable accuracy.
• PBPK modeling serves as a valuable tool for understanding compound properties and guiding preclinical research.
• Implementing PBPK modeling can enhance prediction accuracy and inform drug development decisions.