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 two most extensively studied cannabinoids, cannabidiol (CBD) and delta‐9‐tetrahydrocannabinol (THC), are used for myriad conditions. THC is predominantly eliminated via the cytochromes P450 (CYPs), whereas CBD is eliminated through both CYPs and UDP‐glucuronosyltransferases (UGTs). The fractional contributions of these enzymes to cannabinoid metabolism have shown conflicting results among studies. Physiologically based pharmacokinetic (PBPK) models for CBD and THC and for drug–drug interaction studies involving CBD or THC as object drugs were developed and verified to improve estimates of these contributions. First, physicochemical and pharmacokinetic parameters for CBD, THC, and their metabolites (7‐OH‐CBD, 11‐OH‐THC, and 11‐COOH‐THC) were obtained from the literature or optimized. Second, PBPK base models were developed for CBD and THC after intravenous administration. Third, beginning with the intravenous models, absorption models were developed for CBD after oral and oromucosal spray administration and for THC after oral, inhalation, and oromucosal spray administration. The full models well‐captured the area under the concentration–time curve (AUC) and peak concentration (C_max) of CBD and THC from the verification dataset. Predicted AUC and C_max for CBD and 7‐OH‐CBD were within two‐fold of the observed data. For THC, 11‐OH‐THC, and 11‐COOH‐THC, 100%, 100%, and 83% of the predicted AUC values were within two‐fold, respectively, of the observed values; 100%, 92%, and 94% of the predicted C_max values, respectively, were within two‐fold of the observed values. The verified models could be used to help address critical public health needs, including assessing potential drug interaction risks involving CBD and THC.

Cannabidiol (CBD) is one of the most extensively studied cannabinoids and is used for myriad conditions. Its oral pharmacokinetics are complex, exhibiting non‐linear absorption, significant food effects, and variable exposure in hepatic impairment. Existing physiologically based pharmacokinetic (PBPK) models for oral CBD have largely relied on fitted first‐order absorption or fitted dissolution profiles, limiting their mechanistic and predictive capabilities and extrapolation, particularly regarding the mechanistic details of its absorption. This study developed and verified the first PBPK model for oral CBD with mechanism‐based oral absorption based on our prior published PBPK model. It incorporated mechanism‐based absorption using the multi‐layer gut wall within the advanced dissolution, absorption, and metabolism (M‐ADAM) model within Simcyp. Pharmacokinetic parameters for CBD or population parameters related to absorption were obtained from the literature or optimized. Some physiological parameters (e.g., luminal bile salt and lymph flow rate) were adjusted mechanistically to account for CBD's sesame oil formulation and meal characteristics. The model well captured the CBD concentration‐time profiles and key pharmacokinetic parameters, including area under the concentration‐time curve (AUC), peak concentration ( C _max ), and time to maximum concentration ( t _max ) across diverse doses, fed/fasted states, and in patients with hepatic impairment (mild, moderate, severe). Predictions were consistently within two‐fold of observed data. This work offers a robust foundation for the mechanistic understanding of CBD's complex oral absorption. The verified models can optimize CBD dosing strategies, predict potential drug–drug interactions, and evaluate CBD pharmacokinetics in various specific populations, ultimately contributing to safer and more effective clinical use.

Background: As cannabis becomes legal in several U.S. states, the risk of THC-induced tachycardia increases. This study aimed to develop and verify a physiologically based pharmacokinetic–pharmacodynamic (PBPK-PD) model to assess the impact of THC and its active metabolite, 11-hydroxy-THC (11-OH-THC), on the heart rate of healthy adults. Methods: A PBPK-PD model for intravenous (IV) 11-OH-THC administration was first developed. Secondly, a PBPK-PD model for IV THC, combined with the metabolized 11-OH-THC, was established, verified, and validated. Direct PD models driven by the plasma, brain, and heart concentrations of THC and 11-OH-THC predicted using our previously verified PBPK model were tested for model development. Finally, the risks of tachycardia at a rest condition from various doses of oral and inhaled THC were simulated for 500 individuals aged 18–65 years, with a sex ratio of 1:1 and a baseline heart rate of 70 beats per minute. Results: The PD model was best described by a direct nonlinear E_max model driven by the sum of the total THC and 11-OH-THC concentrations in their effect compartments linked to their heart compartments. In 42 simulated dosing regimens with THC doses ranging from 2 to 69.4 mg, 97% of the observed heart rates or heart rate changes following THC administration fell within the 5th to 95th percentiles of the model-predicted values. Similarly, for two simulated 11-OH-THC IV doses, 93% of the observations fell within this range. Simulations indicated that half of the simulated population would experience tachycardia at doses of 60 mg and 15 mg of THC for oral and inhaled administration, respectively. The simulated risks of tachycardia based on specific conditions should be interpreted with caution. Conclusions: Our verified PBPK-PD model successfully describes the heart rate changes in healthy adults after IV, oral, and inhaled THC administration. This model provides a tool to predict the effects of THC and its primary metabolite on heart rates, offering valuable insights for assessing the risk of tachycardia in both clinical and recreational cannabis use.

Cannabidiol (CBD) use continues to increase, particularly among older adults. However, knowledge of CBD disposition in this ever-growing and understudied p