Evaluation of a basic physiologically based pharmacokinetic model for simulating the first-time-in-animal study.

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Chemistry
• Preclinical Drug Development

Background:

• Physiologically based pharmacokinetic (PBPK) models are crucial for predicting drug behavior in vivo.
• Early prediction of pharmacokinetic (PK) profiles streamlines drug discovery and reduces animal testing.
• Integrating in silico predictions with high-throughput screening data enhances preclinical assessments.

Purpose of the Study:

• To evaluate a PBPK modeling approach for predicting rodent plasma concentration-time curves after intravenous drug administration.
• To assess the accuracy of PBPK predictions using a diverse dataset of known drugs and novel candidates.
• To identify key drug properties influencing prediction accuracy for model refinement.

Main Methods:

• Utilized a PBPK model incorporating structure-based properties (logP, pKa) and high-throughput screening data (fu, CLint).
• Validated predictions against observed PK data for 45 molecules, including 6 drugs and 39 drug candidates.
• Employed multivariate analysis to pinpoint properties linked to prediction biases.

Main Results:

• PBPK model accurately predicted plasma concentration-time profiles, with PK parameters averaging 2-3 fold of observed values.
• The model demonstrated good performance across a range of molecules, including those from active discovery programs.
• Multivariate analysis identified specific molecular properties affecting prediction accuracy.

Conclusions:

• The PBPK approach provides reliable predictions for rodent pharmacokinetics, supporting early-stage drug development.
• This method aids in prioritizing in vivo pharmacokinetic screening and optimizing first-time-in-animal study designs.
• The PBPK model serves as a valuable tool for informed decision-making in preclinical drug discovery.