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

Abstract

Vicasinabin is an oral cannabinoid receptor 2 (CB2) agonist showing anti-inflammatory effects and was developed for the treatment of chronic inflammatory diseases such as diabetic retinopathy. Vicasinabin is mainly metabolized by CYP3A4, with minor contributions from CYP2C19 and UGTs. The drug shows in vitro induction of CYP3A4, as well as inhibition of hepatic and renal transporters. Translation of in vitro data to a clinical drug-drug interaction (DDI) risk assessment has been challenging, with a potential role of CYP2C19 genotypes in the pharmacokinetics to be considered. A physiologically based pharmacokinetic (PBPK) model of vicasinabin based on a bottom-up approach predicted a moderate systemic exposure reduction for the selective CYP3A4 substrate midazolam. Neither the OATP1B1/P-gp/CYP3A4 inhibition effect on atorvastatin nor the OCT2/MATE1 inhibition effect on metformin was predicted to be of clinical relevance by PBPK modeling, as was confirmed by clinical DDI study data. After successful PBPK model prediction of itraconazole DDI using an in vitro fm,CYP3A4 of 0.6, the model was applied to simulate weak or moderate exposure changes of vicasinabin after co-administration with perpetrators for CYP3A4 and CYP2C19 (erythromycin, fluconazole, fluvoxamine, efavirenz, and rifampicin). A strong effect of induction due to rifampicin was also indicated. The CYP2C19 genotypes did not result in a significant impact on the victim DDI prediction for vicasinabin due to a low fm,CYP2C19 (∼0.2). The case study illustrated the usefulness of prospective PBPK predictions of clinical drug-drug interactions using in vitro data.