LB-032 - PREDICTING THE POTENTIAL FOR CANNABIDIOL INDUCED DRUG-DRUG INTERACTIONS WITH MAJOR CYP450 ENZYMES USING MODEL-BASED APPROACHES

B. Eltanameli¹, S. Al Sahlawi², B. Cicali³, R. Cristofoletti³; ¹Univ of Florida Coll of Pharmacy, Orlando, Florida, United States of America, ²Univ of Florida Coll of Pharmacy, Orlando, Florida, United State of America, ³Univ of Florida Coll of Pharmacy, Orlando, FL, United States.

Background: Patients using medical marijuana often take other medications, increasing the risk of drug-drug interactions (DDIs). Cannabidiol (CBD) is one of the most abundant phytocannabinoids in marijuana. In vitro, CBD was found to precipitate several CYP-mediated DDIs via reversible and time-dependent inhibition (TDI). Our study aims to predict the magnitude of CBD-induced metabolic DDIs leveraging available in vitro and in vivo data.

Methods: We employed a forward stepwise, model-based approach as recommended by the FDA guidance to predict the potential for CBD-precipitated metabolic DDI. The basic model calculated R1 and R1,gut to address reversible inhibition in the liver and intestine, while R2 was computed to account for TDI. An R1 ≥ 1.02, R1,gut ≥11, or R2 value ≥1.25 indicated a potential risk for DDIs and warranted further investigation using the static mechanistic model. DDIs precipitated by CBD, as predicted by the static mechanistic model, were classified as weak (AUCR < 2), moderate (2 < AUCR < 5), or severe (AUCR > 5) following the FDA criteria. Subsequently, a physiological-based pharmacokinetic (PBPK) model for CBD was developed and validated using the Simcyp simulator (v.22). Model performance was evaluated by comparing simulated exposure metrics with corresponding clinical data.

Results: The basic model revealed that CBD had the potential to induce DDIs with all major CYP enzymes. The static mechanistic model demonstrated that orally administered CBD could lead to severe DDIs with drugs predominantly metabolized by CYPs 1A1, 1B1, 2C19, and 3A, as well as moderate DDIs with drugs metabolized by CYPs 1A2, 2C9, and 2E1. The CBD model was constructed based on its physicochemical properties, in vitro inhibition data, and information related to its absorption, distribution, metabolism, and elimination. The PBPK model successfully predicted CBD systemic exposure in healthy adults within two-fold of the observed values.

Conclusion: CBD exhibits the potential for inducing severe to moderate DDIs through major CYP-mediated metabolic pathways. The PBPK model effectively recapitulated CBD plasma concentrations and can be extrapolated to different populations to predict different CBD-induced DDIs scenarios.