Prospective DDI Risk Assessment of Vicasinabin with PBPK Modeling by Integrating In Vitro Data

Area of Science:

• Pharmacology
• Drug Metabolism and Pharmacokinetics
• Computational Biology

Background:

• Vicasinabin is an oral CB2 agonist developed for chronic inflammatory diseases like diabetic retinopathy.
• Its metabolism involves CYP3A4, CYP2C19, and UGTs, with in vitro data suggesting CYP3A4 induction and transporter inhibition.
• Assessing clinical drug-drug interaction (DDI) risk from in vitro data presents challenges, with potential CYP2C19 genotype influences.

Purpose of the Study:

• To develop a physiologically based pharmacokinetic (PBPK) model for vicasinabin.
• To predict and assess the clinical relevance of vicasinabin's drug-drug interactions (DDIs).
• To evaluate the utility of PBPK modeling for prospective DDI risk assessment.

Main Methods:

• A bottom-up PBPK model was constructed for vicasinabin.
• The model predicted DDIs with CYP3A4 substrates (midazolam, atorvastatin) and inhibitors/inducers (itraconazole, erythromycin, fluconazole, etc.).
• The impact of CYP2C19 genotypes on vicasinabin pharmacokinetics was simulated.

Main Results:

• PBPK modeling predicted a moderate exposure reduction for midazolam.
• Inhibition effects on atorvastatin and metformin were predicted to be not clinically relevant, matching clinical data.
• Simulations accurately predicted itraconazole DDI and showed potential for moderate exposure changes with other CYP3A4/CYP2C19 perpetrators, including strong induction by rifampicin.
• CYP2C19 genotypes had minimal impact on DDI predictions.

Conclusions:

• PBPK modeling is a valuable tool for prospective prediction of clinical DDIs for vicasinabin.
• The developed PBPK model successfully translated in vitro findings to clinical DDI risk assessment.
• Vicasinabin's DDI profile appears manageable, with PBPK modeling aiding in its clinical development.