Bassma Eltanameli
University of Florida
Co-Authors: Sulafa Al Sahlawi1, Rodrigo Cristofoletti1
1University of Florida
Introduction
Patients receiving medical marijuana are likely to be taking concomitant drugs and thus, have a higher risk for drug-drug interactions (DDI). Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) are the most abundant phytocannabinoids in marijuana. In vitro, Δ9-THC and CBD were found to precipitate several CYP-mediated DDIs via reversible and time-dependent inhibition (TDI). We aim to predict the magnitude of CBD and Δ9-THC-induced metabolic DDIs leveraging available in vitro and in vivo data and using a forward stepwise, model-based approach as recommended by the FDA guidance.
Method
A comprehensive literature review was carried out to collect in vitro reversible inhibition (Ki) and inactivation (KI,u and kinact) parameters for CBD and Δ9-THC in human liver microsomes. The basic model was applied to predict the potential for metabolic DDI precipitated by CBD and Δ9-THC. R1 and R1,gut were calculated to address reversible inhibition in the liver and intestine, and R2 was calculated to address TDI. An R1 ≥ 1.02, R1,gut ≥11, or R2 value ≥1.25 was considered a potential risk for DDI and was further investigated using the static mechanistic model. Resulting DDIs from the static model were classified as weak (AUCR <2), moderate (25) as per FDA criteria.
Results
The basic model showed a DDI potential of CBD with all tested major CYP enzymes, whereas, for Δ9-THC, DDI risk was detected with CYPs 1A1, 1A2, and 2C9. Using the static mechanistic model, orally administered CBD precipitated severe DDIs with drugs predominantly metabolized by CYPs 1A1, 1B1, 2C19, and 3A; and moderate DDIs with drugs metabolized by CYPs 1A2, 2C9, and 2E1. On the other hand, orally administered Δ9-THC showed a moderate risk for DDIs with drugs extensively metabolized by CYPs 1A1, and 2C9.
Conclusion
CBD and, to a lesser extent, Δ9-THC showed potential for severe to moderate DDIs on major CYP-mediated metabolic pathways. Further confirmation of these DDIs is warranted using a dynamic physiological-based pharmacokinetics model.