Exploratory population pharmacokinetics (e-PPK) analysis for predicting human PK using exploratory ADME data during...

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Biology
• Translational Pharmacology

Background:

• Accurate prediction of human drug concentration time-courses is crucial for effective drug development.
• Traditional methods often require extensive compound-specific data, limiting early-stage research.
• Bridging the gap between preclinical and clinical pharmacokinetic data remains a challenge.

Purpose of the Study:

• To develop and validate a novel population pharmacokinetics (PPK) method for forecasting human drug concentration time-courses.
• To integrate in vitro-in vivo extrapolation (IVIVE) with non-linear mixed-effects modeling (NONMEM) for enhanced predictive accuracy.
• To establish a scalable approach for predicting pharmacokinetic profiles early in drug discovery.

Main Methods:

• Utilized population pharmacokinetics analysis with the NONMEM software and IVIVE.
• Retrospectively analyzed eleven clinically tested compounds, incorporating in vivo data from rats, dogs, monkeys, and humans.
• Employed a two-compartment model (ADVAN4 TRANS3) and incorporated species-specific parameters (hepatic plasma flow, plasma volume) into the model.
• Simulated human pharmacokinetic profiles by substituting species-specific random effects (eta) into the human PPK model.

Main Results:

• The developed e-PPK approach successfully simulated drug concentration-time courses that closely matched actual clinical data.
• Predictions fell within the dynamic range of observed clinical pharmacokinetic profiles.
• The method demonstrated the ability to generate time-courses without requiring highly drug-specific parameters, enabling determination of elimination half-time.

Conclusions:

• The proposed exploratory population pharmacokinetic (e-PPK) approach is a valuable and progressive tool for early drug discovery.
• This method offers a robust strategy for forecasting human drug concentration time-courses by integrating preclinical and in vitro data.
• The e-PPK approach facilitates efficient prediction of pharmacokinetic properties, supporting informed decision-making in preclinical research.