Development and Verification of a Linked Δ 9-THC/11-OH-THC Physiologically Based Pharmacokinetic Model in Healthy, Nonpregnant Population and Extrapolation to Pregnant Women

CBD and THC in Special Populations: Pharmacokinetics and Drug-Drug Interactions

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 impact of pregnancy and associated hormones on the pharmacokinetics of Δ9-tetrahydrocannabinol

INTRODUCTION

(-)-Δ9-tetrahydrocannabinol (THC) is the main psychoactive component of cannabis. Cannabis is the most widely used drug of abuse by pregnant individuals, but its maternal-fetal safety is still unclear. The changes in THC disposition during pregnancy may affect THC safety and pharmacology.

AREAS COVERED

This review summarizes the current literature on THC metabolism and pharmacokinetics in humans. It provides an analysis of how hormonal changes during pregnancy may alter the expression of cannabinoid metabolizing enzymes and THC and its metabolite pharmacokinetics. THC is predominately (>70%) cleared by hepatic metabolism to its psychoactive active metabolite, 11-OH-THC by cytochrome P450 (CYP) 2C9 and to other metabolites (<30%) by CYP3A4. Other physiological processes that change during pregnancy and may alter cannabinoid disposition are also reviewed.

EXPERT OPINION

THC and its metabolites disposition likely change during pregnancy. Hepatic CYP2C9 and CYP3A4 are induced in pregnant individuals and in vitro by pregnancy hormones. This induction of CYP2C9 and CYP3A4 is predicted to lead to altered THC and 11-OH-THC disposition and pharmacodynamic effects. More in vitro studies of THC metabolism and induction of the enzymes metabolizing cannabinoids are necessary to improve the prediction of THC pharmacokinetics in pregnant individuals.

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

Population Pharmacokinetics of Oral-Based Administration of Cannabidiol in Healthy Adults: Implications for Drug Development

Background and Objectives: Cannabidiol (CBD) is increasingly being studied as a therapeutic option for a range of health conditions; however, the pharmacokinetics of CBD is not well understood. This study characterized CBD pharmacokinetics in healthy adults using a population pharmacokinetic approach, informing drug development of oral-based dose forms of CBD. Materials and Methods: CBD concentration-time data were obtained from a phase I, randomized, open-label, four-way crossover study (n=12) and modeled using Phoenix NLME. Monte Carlo simulations were conducted to estimate CBD exposure with chronic dosing as intended for clinical use (50 mg b.i.d.). Results: A three-compartment pharmacokinetic model with a chain of absorption transit compartments and first-order elimination most adequately described CBD pharmacokinetics. Substantial variability in population pharmacokinetic parameters was identified (up to 60%CV), which could not be accounted for by any covariates. Simulations indicated a 3.6-fold difference in drug exposure at steady state with multiple dosing (AUCτ 95% prediction interval: 65.5-138 ng·h/mL), and variability in the time to reach steady state, which was predicted to be up to ∼3 weeks in some individuals (95% prediction interval: 18.6-297 h). Conclusions: The findings of this study have important implications for drug development. The lack of a clear dose-response relationship, due to large pharmacokinetic variability, indicates that a one-size-fits-all approach to CBD dosing may not be feasible, at least with current dosing approaches. Furthermore, an extended time to reach steady state means that the full effect of a selected dose level is not truly observed for some time and requires careful consideration in trial design.

Evaluation of Cytochrome P450-Mediated Cannabinoid-Drug Interactions in Healthy Adult Participants

Understanding cannabis-drug interactions is critical given regulatory changes that have increased access to and use of cannabis. Cannabidiol (CBD) and Δ-9-tetrahydrocannabinol (Δ9-THC), the most abundant phytocannabinoids, are in vitro reversible and time-dependent (CBD only) inhibitors of several cytochrome P450 (CYP) enzymes. Cannabis extracts were used to evaluate quantitatively potential pharmacokinetic cannabinoid-drug interactions in 18 healthy adults. Participant received, in a randomized cross-over manner (separated by ≥ 1 week), a brownie containing (i) no cannabis extract (ethanol/placebo), (ii) CBD-dominant cannabis extract (640 mg CBD + 20 mg Δ9-THC), or (iii) Δ9-THC-dominant cannabis extract (20 mg Δ9-THC and no CBD). After 30 minutes, participants consumed a cytochrome P450 (CYP) drug cocktail consisting of caffeine (CYP1A2), losartan (CYP2C9), omeprazole (CYP2C19), dextromethorphan (CYP2D6), and midazolam (CYP3A). Plasma and urine samples were collected (0-24 hours). The CBD + Δ9-THC brownie inhibited CYP2C19 > CYP2C9 > CYP3A > CYP1A2 (but not CYP2D6) activity, as evidenced by an increase in the geometric mean ratio of probe drug area under the plasma concentration-time curve (AUC) relative to placebo (AUCGMR ) of omeprazole, losartan, midazolam, and caffeine by 207%, 77%, 56%, and 39%, respectively. In contrast, the Δ9-THC brownie did not inhibit any of the CYPs. The CBD + Δ9-THC brownie increased Δ9-THC AUCGMR by 161%, consistent with CBD inhibiting CYP2C9-mediated oral Δ9-THC clearance. Except for caffeine, these interactions were well-predicted by our physiologically-based pharmacokinetic model (within 26% of observed interactions). Results can be used to help guide dose adjustment of drugs co-consumed with cannabis products and the dose of CBD in cannabis products to reduce interaction risk with Δ9-THC.

Physiologically based pharmacokinetic modeling and simulation of cannabinoids in human plasma and tissues

There has been an increased public interest in developing consumer products containing nonintoxicating cannabinoids, such as cannabidiol (CBD) and cannabigerol (CBG). At the present time, there is limited information available on the pharmacokinetics of cannabinoids in humans. Since pharmacokinetic profiles are important in understanding the pharmacological and toxicological effects at the target sites, physiologically based pharmacokinetic (PBPK) modeling was used to predict the plasma and tissue concentrations of 17 cannabinoids in humans. PBPK models were established using measured (in vitro) and predicted (in silico) physicochemical and pharmacokinetic properties, such as water solubility and effective human jejunal permeability. Initially, PBPK models were established for CBD and the model performance was evaluated using reported clinical data after intravenous and oral administration. PBPK models were then developed for 16 additional cannabinoids including CBG, and the plasma and tissue concentrations were predicted after 30 mg oral administration. The pharmacokinetic profiles of the 16 cannabinoids were similar to CBD, and the plasma concentration and time profiles of CBD agreed well with clinical data in the literature. Although low exposure was predicted in the plasma (maximum plasma concentrations < 15 nM), the predicted tissue concentrations, especially the liver (maximum liver concentrations 70-183 nM), were higher after oral administration of 30 mg cannabinoids. These predicted plasma and tissue concentrations could be used to guide further in vitro and in vivo testing.

Utilization of Physiologically Based Pharmacokinetic Modeling in Pharmacokinetic Study of Natural Medicine: An Overview

Natural medicine has been widely used for clinical treatment and health care in many countries and regions. Additionally, extracting active ingredients from traditional Chinese medicine and other natural plants, defining their chemical structure and pharmacological effects, and screening potential druggable candidates are also uprising directions in new drug research and development. Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique that simulates the absorption, distribution, metabolism, and elimination of drugs in various tissues and organs in vivo based on physiological and anatomical characteristics and physicochemical properties. PBPK modeling in drug research and development has gradually been recognized by regulatory authorities in recent years, including the U.S. Food and Drug Administration. This review summarizes the general situation and shortcomings of the current research on the pharmacokinetics of natural medicine and introduces the concept and the advantages of the PBPK model in the study of pharmacokinetics of natural medicine. Finally, the pharmacokinetic studies of natural medicine using the PBPK models are summed up, followed by discussions on the applications of PBPK modeling to the enzyme-mediated pharmacokinetic changes, special populations, new drug research and development, and new indication adding for natural medicine. This paper aims to provide a novel strategy for the preclinical research and clinical use of natural medicine.

Predicting the systemic exposure and lung concentration of nafamostat using physiologically-based pharmacokinetic modeling

Nafamostat has been actively studied for its neuroprotective activity and effect on various indications, such as coronavirus disease 2019 (COVID-19). Nafamostat has low water solubility at a specific pH and is rapidly metabolized in the blood. Therefore, it is administered only intravenously, and its distribution is not well known. The main purposes of this study are to predict and evaluate the pharmacokinetic (PK) profiles of nafamostat in a virtual healthy population under various dosing regimens. The most important parameters were assessed using a physiologically based pharmacokinetic (PBPK) approach and global sensitivity analysis with the Sobol sensitivity analysis. A PBPK model was constructed using the SimCYP^® simulator. Data regarding the in vitro metabolism and clinical studies were extracted from the literature to assess the predicted results. The model was verified using the arithmetic mean maximum concentration (C_max), the area under the curve from 0 to the last time point (AUC_0-t), and AUC from 0 to infinity (AUC_0-∞) ratio (predicted/observed), which were included in the 2-fold range. The simulation results suggested that the 2 dosing regimens for the treatment of COVID-19 used in the case reports could maintain the proposed effective concentration for inhibiting severe acute respiratory syndrome coronavirus 2 entry into the plasma and lung tissue. Global sensitivity analysis indicated that hematocrit, plasma half-life, and microsomal protein levels significantly influenced the systematic exposure prediction of nafamostat. Therefore, the PBPK modeling approach is valuable in predicting the PK profile and designing an appropriate dosage regimen.

Predicting Human Fetal Drug Exposure Through Maternal-Fetal PBPK Modeling and In Vitro or Ex Vivo Studies

Medication (drug) use in human pregnancy is prevalent. Determining fetal safety and efficacy of drugs is logistically challenging. However, predicting (not measuring) fetal drug exposure (systemic and tissue) throughout pregnancy is possible through maternal-fetal physiologically based pharmacokinetic (PBPK) modeling and simulation. Such prediction can inform fetal drug safety and efficacy. Fetal drug exposure can be quantified in 2 complementary ways. First, the ratio of the steady-state unbound plasma concentration in the fetal plasma (or area under the plasma concentration-time curve) to the corresponding maternal plasma concentration (ie, Kp,uu ). Second, the maximum unbound peak (Cu,max,ss,f ) and trough (Cu,min,ss,f ) fetal steady-state plasma concentrations. We (and others) have developed a maternal-fetal PBPK model that can successfully predict maternal drug exposure. To predict fetal drug exposure, the model needs to be populated with drug specific parameters, of which transplacental clearances (active and/or passive) and placental/fetal metabolism of the drug are critical. Herein, we describe in vitro studies in cells/tissue fractions or the perfused human placenta that can be used to determine these drug-specific parameters. In addition, we provide examples whereby this approach has successfully predicted systemic fetal exposure to drugs that passively or actively cross the placenta. Apart from maternal-fetal PBPK models, animal studies also have the potential to estimate fetal drug exposure by allometric scaling. Whether such scaling will be successful is yet to be determined. Here, we review the above approaches to predict fetal drug exposure, outline gaps in our knowledge to make such predictions and map out future research directions that could fill these gaps.

Comprehensive Predictions of Cytochrome P450 (P450)-Mediated In Vivo Cannabinoid-Drug Interactions Based on Reversible and Time-Dependent P450 Inhibition in Human Liver Microsomes

We previously reported the unbound reversible (IC50,u) and time-dependent (KI,u) inhibition potencies of cannabidiol (CBD), delta-9-tetrahydrocannabinol (THC), and THC metabolites 11-hydroxy THC (11-OH THC) and 11-nor-9-carboxy-delta-9-THC (11-COOH THC) against the major cytochrome P450 (P450) enzymes (1A2, 2C9, 2C19, 2D6, and 3A). Here, using human liver microsomes, we determined the CYP2A6, 2B6, and 2C8 IC50,u values of the aforementioned cannabinoids and the IC50,u and KI,u of the circulating CBD metabolites 7-hydroxy CBD (7-OH CBD) and 7-carboxy CBD (7-COOH CBD), against all the P450s listed above. The IC50,u of CBD, 7-OH CBD, THC, and 11-OH THC against CYP2B6 was 0.05, 0.34, 0.40, and 0.32 μM, respectively, and against CYP2C8 was 0.28, 1.02, 0.67, and 3.66 μM, respectively. 7-COOH CBD, but not 11-COOH THC, was a weak inhibitor of CYP2B6 and 2C8. All tested cannabinoids except 11-COOH THC were weak inhibitors of CYP2A6. 7-OH CBD inhibited all P450s examined (IC50,u<2.5 μM) except CYP1A2 and inactivated CYP2C19 and CYP3A, with inactivation efficiencies (kinact/KI,u) of 0.10 and 0.14 minutes-1 μM-1, respectively. Using several different static models, we predicted the following maximum pharmacokinetic interactions (affected P450 probe drug and area under the plasma concentration-time curve ratio) between oral CBD (700 mg) and drugs predominantly metabolized by CYP3A (midazolam, 14.8) > 2C9 (diclofenac, 9.6) > 2C19 (omeprazole, 7.3) > 1A2 (theophylline, 4.0) > 2B6 (ticlopidine, 2.2) > 2D6 (dextromethorphan, 2.1) > 2C8 (repaglinide, 1.6). Oral (130 mg) or inhaled (75 mg) THC was predicted to precipitate interactions with drugs predominately metabolized by CYP2C9 (diclofenac, 6.6 or 2.3, respectively) > 3A (midazolam, 1.8) > 1A2 (theophylline, 1.4). In vivo drug interaction studies are warranted to verify these predictions. SIGNIFICANCE STATEMENT: This study, combined with our previous findings, provides for the first time a comprehensive analysis of the potential for cannabidiol, delta-9-tetrahydrocannabinol, and their metabolites to inhibit cytochrome P450 enzymes in a reversible or time-dependent manner. These analyses enabled us to predict the potential of these cannabinoids to produce drug interactions in vivo at clinical or recreational doses.

A review of pregnancy-induced changes in opioid pharmacokinetics, placental transfer, and fetal exposure: Towards fetomaternal physiologically-based pharmacokinetic modeling to improve the treatment of neonatal opioid withdrawal syndrome

Physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to study pharmacokinetics (PK) in special populations, such as pregnant women, fetuses, and newborns, where practical hurdles severely limit the study of drug behavior. PK in pregnant women is variable and everchanging, differing greatly from that in their nonpregnant female and male counterparts typically enrolled in clinical trials. PBPK models can accommodate pregnancy-induced physiological and metabolic changes, thereby providing mechanistic insights into maternal drug disposition and fetal exposure. Fueled by the soaring opioid epidemic in the United States, opioid use during pregnancy continues to rise, leading to an increased incidence of neonatal opioid withdrawal syndrome (NOWS). The severity of NOWS is influenced by a complex interplay of extrinsic and intrinsic factors, and varies substantially between newborns, but the extent of prenatal opioid exposure is likely the primary driver. Fetomaternal PBPK modeling is an attractive approach to predict in utero opioid exposure. To facilitate the development of fetomaternal PBPK models of opioids, this review provides a detailed overview of pregnancy-induced changes affecting the PK of commonly used opioids during gestation. Moreover, the placental transfer of these opioids is described, along with their disposition in the fetus. Lastly, the implementation of these factors into PBPK models is discussed. Fetomaternal PBPK modeling of opioids is expected to provide improved insights in fetal opioid exposure, which allows for prediction of postnatal NOWS severity, thereby opening the way for precision postnatal treatment of these vulnerable infants.