Physiologically-based pharmacokinetic modelling of a CYP2C19 substrate, BMS-823778, utilizing pharmacogenetic data

Area of Science:

• Pharmacokinetics
• Drug Metabolism
• Systems Pharmacology

Background:

• Previous research established a correlation between CYP2C19 genotype and BMS-823778 clearance.
• Understanding drug metabolism and interactions is crucial for optimizing therapeutic efficacy and safety.

Purpose of the Study:

• To develop a physiologically-based pharmacokinetic (PBPK) model for BMS-823778.
• To predict BMS-823778 pharmacokinetics (PK) and drug-drug interactions (DDIs) in virtual populations with varying genetic profiles.

Main Methods:

• A PBPK model for BMS-823778 was constructed and validated using existing clinical data.
• The model was utilized to simulate PK and predict DDIs with a strong CYP3A4 inhibitor in virtual subjects possessing diverse CYP2C19 and UGT1A4 genotypes.

Main Results:

• The validated PBPK model accurately reproduced observed BMS-823778 PK across different populations and captured genotype-specific exposure differences.
• Simulations predicted higher BMS-823778 exposure in CYP2C19 poor metabolizers lacking UGT1A4 activity.
• Drug-drug interactions with itraconazole were predicted to be moderate in subjects with wild-type CYP2C19 or UGT1A4, but significant in those lacking both.

Conclusions:

• A PBPK model effectively predicted human BMS-823778 PK and DDI based on CYP2C19 phenotypes.
• UGT1A4 appears to be a significant clearance pathway in CYP2C19 poor metabolizers.
• The extent of DDI with itraconazole is contingent upon both CYP2C19 and UGT1A4 genotypes.