Practical permeability-based hepatic clearance classification system (HepCCS) in drug discovery.

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Drug Discovery and Development

Background:

• In vitro methods using liver microsomes and hepatocytes are standard for predicting in vivo clearance.
• Metabolic stability data alone may not accurately predict in vivo clearance.
• Discrepancies between in vitro and in vivo clearance can impact drug development timelines.

Purpose of the Study:

• To develop a systematic approach for predicting in vivo clearance.
• To address the limitations of using metabolic stability data alone.
• To help drug discovery scientists identify and understand in vitro-in vivo clearance prediction disconnects early.

Main Methods:

• Apparent permeability was measured using Mardin-Darby canine kidney cells.
• Rat hepatocyte uptake was assessed for 33 discovery compounds.
• A classification system and decision tree were developed.

Main Results:

• Compounds with low apparent permeability (< 3 × 10(-6) cm/s) showed hepatic uptake when in vivo clearance was underpredicted.
• The developed classification system and decision tree provide a structured approach to analysis.

Conclusions:

• A novel hepatic clearance classification system aids in early identification of in vitro-in vivo prediction disconnects.
• This systematic approach improves the understanding of clearance prediction challenges in drug discovery.
• The proposed decision tree assists scientists in navigating complex pharmacokinetic data.