Integrating Static and Dynamic Models to Predict Cannabidiol-Mediated Metabolic Drug-Drug Interactions

Cannabidiol (CBD) inhibits multiple cytochrome P450 (CYP450) enzymes in vitro via reversible and time-dependent mechanisms, raising concerns for metabolic drug-drug interactions (DDIs). Clinical DDI data for CBD are limited, and the translation of in vitro derived inhibition parameters into in vivo DDI predictions remains uncertain. We implemented a stepwise, model-informed framework to evaluate CBD-mediated metabolic DDIs using (i) basic, (ii) mechanistic static (MSM), and (iii) physiologically based pharmacokinetic (PBPK) models. The basic model and MSM incorporated in vitro inhibition parameters for CBD and its primary active metabolite, 7-hydroxycannabidiol (7-OH-CBD), to identify sensitive metabolic pathways. A PBPK model for CBD and 7-OH-CBD was developed and validated against clinical pharmacokinetic data, then used to simulate DDIs. Where necessary, inhibition parameters were refined using the PBPK model to capture clinical observations, and the optimized parameters were used to revisit the basic model and MSM, facilitating comparison across modeling approaches. The MSM predicted 3.6-, 4.1-, and 2.2-fold increases in exposure of sensitive CYP1A2, CYP2C9, and CYP3A4 substrates, respectively, and a strong CYP2C19-mediated interaction. In contrast, the PBPK model predicted lower DDI magnitudes consistent with clinical data, identifying CYP2C19 inhibition as the most clinically relevant liability, with a moderate interaction risk for sensitive substrates, while CYP1A2 inhibition increased sensitive substrate exposure twofold. The validated PBPK model was applied to evaluate exposure in special populations and DDI risk with commonly co-prescribed medications. Overall, this framework reconciles in vitro and clinical DDI data and supports population-specific assessment of CBD-mediated DDIs across diverse clinical scenarios.