Drug metabolism is a major determinant of drug concentrations in the body. Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs can lead to alteration in the exposure of the victim drug, raising safety or effectiveness concerns. Assessment of the DDI potential starts with in vitro experiments to determine kinetic parameters and identify risks associated with the use of comedication that can inform future clinical studies. The diverse range of experimental models and techniques has significantly contributed to the examination of potential DDIs. Cytochrome P450 (CYP) enzymes are responsible for the biotransformation of many drugs on the market, making them frequently implicated in drug metabolism and DDIs. Consequently, there has been a growing focus on the assessment of DDI risk for CYPs. This review article provides mechanistic insights underlying CYP inhibition/induction and an overview of the in vitro assessment of CYP-mediated DDIs.

Cannabinoid use has surged in the past decade, with a growing interest in expanding cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) applications into special populations. Consequently, the increased use of CBD and THC raises the risk of drug–drug interactions (DDIs). Nevertheless, DDIs for cannabinoids, especially in special populations, remain inadequately investigated. While some clinical trials have explored DDIs between therapeutic drugs like antiepileptic drugs and CBD/THC, more potential interactions remain to be examined. This review summarizes the published studies on CBD and THC–drug interactions, outlines the mechanisms involved, discusses the physiological considerations in pharmacokinetics (PK) and DDI studies in special populations (including pregnant and lactating women, pediatrics, older adults, patients with hepatic or renal impairments, and others), and presents modeling approaches that can describe the DDIs associated with CBD and THC in special populations. The PK of CBD and THC in special populations remain poorly characterized, with limited studies investigating DDIs involving CBD/THC in these populations. Therefore, it is critical to evaluate potential DDIs between CBD/THC and medications that are commonly used in special populations. Modeling approaches can aid in understanding these interactions.

The cannabinoids cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) undergo extensive oxidative metabolism in the liver. Although cytochromes P450 form the primary, pharmacologically active, hydroxylated metabolites of CBD and THC, less is known about the enzymes that generate the major in vivo circulating metabolites of CBD and THC, 7-carboxy-CBD and 11-carboxy-THC, respectively. The purpose of this study was to elucidate the enzymes involved in forming these metabolites. Cofactor dependence experiments with human liver subcellular fractions revealed that 7-carboxy-CBD and 11-carboxy-THC formation is largely dependent on cytosolic NAD⁺-dependent enzymes, with lesser contributions from NADPH-dependent microsomal enzymes. Experiments with chemical inhibitors provided evidence that 7-carboxy-CBD formation is mainly dependent on aldehyde dehydrogenases and 11-carboxy-THC formation is mediated also in part by aldehyde oxidase. This study is the first to demonstrate the involvement of cytosolic drug-metabolizing enzymes in generating major in vivo metabolites of CBD and THC and addresses a knowledge gap in cannabinoid metabolism.

Cannabidiol (CBD) is a pharmacologically active metabolite of cannabis that is FDA-approved to treat seizures associated with Lennox-Gastaut syndrome, Dravet syndrome, and tuberous sclerosis complex in children aged one year and older. During clinical trials, CBD caused dose-dependent hepatocellular toxicity at therapeutic doses. The risk for toxicity was increased in patients taking valproate (VPA), another hepatotoxic antiepileptic drug, through an unknown mechanism. With the growing popularity of CBD in the consumer market, an improved understanding of the safety risks associated with CBD is needed to ensure public health. This review details current efforts to describe CBD pharmacokinetics and mechanisms of hepatotoxicity using both pharmacokinetic models and in vitro models of the liver. In addition, current evidence and knowledge gaps related to intracellular mechanisms of CBD-induced hepatotoxicity are described. The authors propose future directions that combine systems-based models with markers of CBD-induced hepatotoxicity to understand how CBD pharmacokinetics may influence the adverse effect profile and risk of liver injury for those taking CBD. Significance Statement This review describes current pharmacokinetic modeling approaches to capture the metabolic clearance and safety profile of cannabidiol (CBD). CBD is an increasingly popular natural product and FDA-approved antiepileptic drug known to cause clinically significant enzyme-mediated drug interactions and hepatotoxicity at therapeutic doses. CBD metabolism, pharmacokinetics, and putative mechanisms of CBD-induced liver injury are summarized from available preclinical data to inform future modeling efforts for understanding CBD toxicity.

Cannabidiol (CBD) is approved for treatment of seizures associated with two forms of epilepsy that become apparent in infancy or early childhood. To consider an adult physiologically‐based pharmacokinetic (PBPK) model for pediatric scaling, we assessed in vitro‐derived cytochrome P450 (CYP) and uridine 5′‐diphospho‐glucuronosyltransferase (UGT) enzyme contributions to CBD clearance in human. An i.v. PBPK model was constructed using CBD physicochemical properties and knowledge of disposition. The i.v. datasets were used for model building and evaluation. Oral PBPK models for CBD administered in fasted and fed states were developed using single dose oral datasets and parameters optimized from the i.v. model and evaluated with multiple dose datasets. Relative contributions of CBD metabolizing enzymes were partitioned according to in vitro studies. Clinical drug–drug interaction (DDI) studies were simulated using CBD fed state, itraconazole, fluconazole, and rifampicin PBPK models. Linear mixed effect modeling was used to estimate area under the concentration‐time curve from zero to infinity (AUC_0–∞) perpetrator + CBD versus CBD alone. The i.v. and oral datasets used in model evaluation produced acceptable average fold error (AFE) of 1.28 and absolute AFE of 1.65. Relative contributions of drug‐metabolizing enzymes to CBD clearance were proposed from in vitro data: UGT1A7 4%, UGT1A9 16%, UGT2B7 10%, CYP3A4 38%, CYP2C19 21%, and CYP2C9 11%. The simulated DDI studies using the in vitro‐derived values produced AUC_0–∞ treatment ratios comparable to observed: itraconazole 1.24 versus 1.07, fluconazole 1.45 versus 1.22, and rifampicin 0.49 versus 0.69. The constructed CBD PBPK models can predict adult exposures and have potential for use in pediatrics where exposure estimates are limited.