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Evaluation of cannabidiol-drug interactions using static and dynamic models

Bassma Eltanameli
University of Florida

Co-Authors: Sulafa Al Sahlawi, Brian Cicali, Rodrigo Cristofoletti
University of Florida

Introduction: Patients using medical marijuana often take other medications, increasing their risk of drug-drug interactions (DDIs). In vitro, cannabidiol (CBD) and its active metabolite 7-Hydroxycannabidiol (7-OH- CBD) were found to precipitate several CYP-mediated metabolic DDIs via reversible and time-dependent inhibition (TDI). We aim to leverage available in vitro and in vivo data to predict the magnitude of CBD-induced metabolic DDIs using a forward stepwise, model-based approach recommended by the FDA guidance.

Methods: The basic model was applied to predict the potential of DDIs precipitated by CBD and 7-OH-CBD. R1 and R1, gut were used to address reversible inhibition in the liver and intestine, respectively, while R2 was used to address TDI. Enzymes showing potential risks for DDIs were further investigated using the static mechanistic model. DDIs were classified as weak (AUCR < 2), moderate (2 ≤ AUCR < 5), or severe (AUCR ≥ 5) as per the FDA criteria. Subsequently, a Physiological-Based Pharmacokinetic (PBPK) model for CBD and 7-OH-CBD was developed and validated using the Simcyp™ Simulator (v.22) and was used to predict available clinical DDI studies.

Results: CBD and, to a lesser extent, 7-OH-CBD had the potential to precipitate DDIs with all major CYP enzymes when evaluated using the basic model. The static mechanistic model showed that CBD and 7-OH-CBD could lead to severe DDIs with drugs metabolized by CYPs 2C19, 3A, and moderate DDIs with drugs metabolized by CYPs 2C9 and 1A2. The PBPK model successfully predicted CBD and 7-OH-CBD systemic exposure in healthy adults following intravenous and oral administration. Predicted plasma concentrations were within the 95% confidence interval of the observed values, and the PBPK model was further validated using additional datasets. All predicted pharmacokinetics parameters were within two-fold of the observed values. The PBPK model predicted clinical DDIs after fitting the in vitro ki values to recapitulate observed DDIs. CBD increased omeprazole AUCR by 211% after single administration and caffeine AUCR by 85% after multiple administration. CBD did not significantly inhibit all other tested enzymes.

Conclusion: Although CBD showed an inhibitory effect on major CYP enzymes in vitro, it was not clinically evident, except for CYP2C19. CBD administration did not significantly inhibit other CYP enzymes (AUCR < 2). The validated PBPK model for CBD will be extended to simulate the DDI potential with clobazam and stiripentol and real-world scenarios, including the impact of age, food consumption, and liver and kidney function on the magnitude of DDIs.