Predicting human pharmacokinetics from preclinical data: clearance.

Area of Science:

• Pharmacokinetics and Drug Metabolism
• In Vitro Assays
• Physiologically Based Pharmacokinetic (PBPK) Modeling

Background:

• Predicting human drug clearance (CL) from in vitro data is complex.
• Existing in vitro methods for CL prediction are fragmented across literature.
• Drug development scientists often struggle to grasp the full picture of CL prediction.

Purpose of the Study:

• To provide a streamlined tutorial of known in vitro methods for predicting human clearance.
• To clarify the sequential steps from intrinsic CL measurement to predicted hepatic CL (CLH).
• To offer a practical approach for overcoming in vitro-in vivo discrepancies in CLH.

Main Methods:

• Demonstration of in vitro methods used in human CL prediction.
• Explanation of the chain of equations leading to hepatic CL (CLH) prediction.
• Integration of intrinsic CL measurements and well-stirred models.

Main Results:

• A simplified overview of the in vitro to in vivo CL prediction process.
• Identification of common challenges and complexities in CL prediction.
• A proposed method to correct discrepancies using animal data.

Conclusions:

• The tutorial simplifies the complex process of predicting human hepatic clearance (CLH).
• A strategy is presented to address in vitro-in vivo CLH discrepancies using animal species data.
• This work aims to improve the understanding and application of in vitro methods in drug development.