Characterizing and Quantifying Extrahepatic Metabolism of (-)-Δ9-Tetrahydrocannabinol (THC) and Its Psychoactive Metabolite, (±)-11-Hydroxy-Δ9-THC (11-OH-THC).

(-)-Δ9-Tetrahydrocannabinol (THC) is the psychoactive constituent of cannabis, a drug recreationally consumed orally or by inhalation. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict systemic and tissue exposure to THC and its psychoactive metabolite, (±)-11-hydroxy-Δ9-THC (11-OH-THC). To populate a THC/11-OH-THC PBPK model, we previously characterized the depletion clearance of THC (by CYP2C9) and 11-OH-THC (by UDP-glucuronosyltransferase (UGT), CYP3A, and CYP2C9) in adult human liver microsomes. Here we focused on quantifying extrahepatic depletion clearance of THC/11-OH-THC, important after oral (intestine) and inhalational (lung) consumption of THC as well as prenatal THC use (placenta and fetal liver). THC (500 nM) was metabolized in adult human intestinal microsomes (n = 3-5) by CYP2C9 [Vmax: 1.1 ± 0.38 nmol/min/mg; Michaelis-Menten constant (Km): 70 nM; intrinsic clearance (CLint): 15 ± 5.4 ml/min/mg; fraction metabolized (fm): 0.89 ± 0.31 at concentration ≪ 70 nM] and CYP3A (CLint: 2.0 ± 0.86 ml/min/mg; fm: 0.11 ± 0.050). 11-OH-THC (50 nM) was metabolized by CYP3A (CLint: 0.26 ± 0.058 ml/min/mg; fm: 0.51 ± 0.11) and UGT2B7 (CLint: 0.13 ± 0.027 ml/min/mg; fm: 0.25 ± 0.053). THC at 500 nM (CLint: 4.7 ± 0.22 ml/min/mg) and 11-OH-THC at 50 nM (CLint: 2.4 ± 0.13 ml/min/mg) were predominately (fm: 0.99 and 0.80, respectively) metabolized by CYP3A in human fetal liver microsomes (n = 3). However, we did not observe significant depletion of THC/11-OH-THC in adult lung, first trimester, second trimester, or term placentae microsomes. Using PBPK modeling and simulation, these data could be used in the future to predict systemic and tissue THC/11-OH-THC exposure in healthy and special populations. SIGNIFICANCE STATEMENT: This is the first characterization and quantification of (-)-Δ9-tetrahydrocannabinol (THC) and (±)-11-hydroxy-Δ9-THC (11-OH-THC) depletion clearance by cytochrome P450 and UDP-glucuronosyltransferase enzymes in extrahepatic human tissues: intestine, fetal liver, lung, and placenta. These data can be used to predict, through physiologically based pharmacokinetic modeling and simulation, systemic and tissue THC/11-OH-THC exposure after inhalational and oral THC use in both healthy and special populations (e.g., pregnant women).

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.

Conducting clinical trials to understand the exposure risk/benefit relationship of cannabis use is not always feasible. Alternatively, physiologically based pharmacokinetic (PBPK) models can be used to predict exposure of the psychoactive cannabinoid (-)-Δ9-tetrahydrocannabinol (THC) and its active metabolite 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-THC). Here, we first extrapolated in vitro mechanistic pharmacokinetic information previously quantified to build a linked THC/11-OH-THC PBPK model and verified the model with observed data after intravenous and inhalation administration of THC in a healthy, nonpregnant population. The in vitro to in vivo extrapolation of both THC and 11-OH-THC disposition was successful. The inhalation bioavailability (Finh) of THC after inhalation was higher in chronic versus casual cannabis users (Finh = 0.35 and 0.19, respectively). Sensitivity analysis demonstrated that 11-OH-THC but not THC exposure was sensitive to alterations in hepatic intrinsic clearance of the respective compound. Next, we extrapolated the linked THC/11-OH-THC PBPK model to pregnant women. Simulations showed that THC plasma area under the curve (AUC) does not change during pregnancy, but 11-OH-THC plasma AUC decreases by up to 41%. Using a maternal-fetal PBPK model, maternal and fetal THC serum concentrations were simulated and compared with the observed THC serum concentrations in pregnant women at term. To recapitulate the observed THC fetal serum concentrations, active placental efflux of THC needed to be invoked. In conclusion, we built and verified a linked THC/11-OH-THC PBPK model in healthy nonpregnant population and demonstrated how this mechanistic physiologic and pharmacokinetic platform can be extrapolated to a special population, such as pregnant women. SIGNIFICANCE STATEMENT: Although the pharmacokinetics of cannabinoids have been extensively studied clinically, limited mechanistic pharmacokinetic models exist. Here, we developed and verified a physiologically based pharmacokinetic (PBPK) model for (-)-Δ9-tetrahydrocannabinol (THC) and its active metabolite, 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-THC). The PBPK model was verified in healthy, nonpregnant population after intravenous and inhalation administration of THC, and then extrapolated to pregnant women. The THC/11-OH-THC PBPK model can be used to predict exposure in special populations, predict drug-drug interactions, or impact of genetic polymorphism.

Quantifying Hepatic Enzyme Kinetics of (-)-∆9-Tetrahydrocannabinol (THC) and Its Psychoactive Metabolite, 11-OH-THC, through In Vitro Modeling.

The prevalence of cannabis use and the concentrations of the psychoactive cannabinoid in cannabis, (-)-∆9-tetrahydrocannabinol (THC), are rising. Physiologically based pharmacokinetic modeling and simulations (PBPK M&S) can mechanistically predict exposure of THC and its major and active metabolite, 11-hydroxy-THC (11-OH-THC). To build a THC/11-OH-THC PBPK model, mechanistic information about the disposition of these compounds is necessary, including the drug-metabolizing enzymes (DMEs) involved and the fraction metabolized (fm) and metabolic kinetic parameters (intrinsic clearance, maximal formation rate, and Km) via the identified enzymes. We previously identified and quantified the fm of DMEs involved in hepatic depletion of THC and 11-OH-THC. In this study, we extend this work to characterize the enzyme kinetics of THC and 11-OH-THC by monitoring their depletion and formation of some of their metabolites in pooled human liver microsomes. A P450 and UDP-glucuronosyltransferase (UGT) kinetic model was fitted to the concentration-time depletion/formation profiles to establish the contribution and kinetics of the individual DME pathways. CYP2C9 pathway was the major pathway for depletion of THC (fm = 0.91, Km,u = 3 nM) and formation of 11-OH-THC. The remaining THC depletion pathway was attributed to CYP2D6. 11-OH-THC was depleted by UGTs (fm = 0.67 and Km,u = 39 nM), CYP3A4 (fm = 0.18, Km,u = 824 nM), and CYP2C9 (fm = 0.15, Km,u = 33 nM). These mechanistic in vitro data can be used to predict the exposure of THC and 11-OH-THC in healthy and special populations, including in the presence of drug-drug interactions, via PBPK M&S.

Hepatic Enzymes Relevant to the Disposition of (-)-∆9-Tetrahydrocannabinol (THC) and Its Psychoactive Metabolite, 11-OH-THC.

Marijuana use by pregnant women is increasing. To predict developmental risk to the fetus/neonate from such use, in utero fetal exposure to (-)-∆9-tetrahydrocannabinol (THC), the main psychoactive cannabinoid in marijuana and its active psychoactive metabolite, 11-hydroxy-∆9-tetrahydrocannabinol (11-OH-THC), needs to be determined. Since such measurement is not possible, physiologically based pharmacokinetic (PBPK) modeling and simulation can provide an alternative method to estimate fetal exposure to cannabinoids. To do so, pharmacokinetic parameters for the disposition of THC and 11-OH-THC need to be elucidated. Here, we report a first step to estimate these parameters, namely, those related to maternal metabolism of THC/11-OH-THC in human liver microsomes (HLMs) at plasma concentrations observed after smoking marijuana. Using recombinant cytochrome P450 (P450) and UDP-glucuronosyltransferase (UGT) enzymes, CYP1A1, 1A2, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7, and UGT1A9 and UGT2B7 were found to be involved in the disposition of THC/11-OH-THC. Using pooled HLMs, the fraction metabolized (f m) by relevant enzymes was measured using selective enzyme inhibitors, and then adjusted for enzyme cross-inhibition. As previously reported, CYP2C9 was the major enzyme responsible for depletion of THC and formation of 11-OH-THC with f m values of 0.82 ± 0.08 and 0.99 ± 0.10, respectively (mean ± S.D.), while CYP2D6 and CYP2C19 were minor contributors. 11-OH-THC was depleted by UGT and P450 enzymes with f m values of 0.60 ± 0.05 and 0.40 ± 0.05, respectively (mean ± S.D.), with UGT2B7, UGT1A9, CYP2C9, and CYP3A4 as contributors. These mechanistic data represent the first set of drug-dependent parameters necessary to predict maternal-fetal cannabinoid exposure during pregnancy using PBPK modeling.

Drug Concentration Asymmetry in Tissues and Plasma for Small Molecule-Related Therapeutic Modalities.

The well accepted "free drug hypothesis" for small-molecule drugs assumes that only the free (unbound) drug concentration at the therapeutic target can elicit a pharmacologic effect. Unbound (free) drug concentrations in plasma are readily measurable and are often used as surrogates for the drug concentrations at the site of pharmacologic action in pharmacokinetic-pharmacodynamic analysis and clinical dose projection in drug discovery. Furthermore, for permeable compounds at pharmacokinetic steady state, the free drug concentration in tissue is likely a close approximation of that in plasma; however, several factors can create and maintain disequilibrium between the free drug concentration in plasma and tissue, leading to free drug concentration asymmetry. These factors include drug uptake and extrusion mechanisms involving the uptake and efflux drug transporters, intracellular biotransformation of prodrugs, membrane receptor-mediated uptake of antibody-drug conjugates, pH gradients, unique distribution properties (covalent binders, nanoparticles), and local drug delivery (e.g., inhalation). The impact of these factors on the free drug concentrations in tissues can be represented by K p,uu, the ratio of free drug concentration between tissue and plasma at steady state. This review focuses on situations in which free drug concentrations in tissues may differ from those in plasma (e.g., K p,uu > or <1) and discusses the limitations of the surrogate approach of using plasma-free drug concentration to predict free drug concentrations in tissue. This is an important consideration for novel therapeutic modalities since systemic exposure as a driver of pharmacologic effects may provide limited value in guiding compound optimization, selection, and advancement. Ultimately, a deeper understanding of the relationship between free drug concentrations in plasma and tissues is needed.

When Does the Rate-Determining Step in the Hepatic Clearance of a Drug Switch from Sinusoidal Uptake to All Hepatobiliary Clearances? Implications for Predicting Drug-Drug Interactions.

For dual transporter-enzyme substrate drugs, the extended clearance model can be used to predict the rate-determining step(s) (RDS) of a drug and hence predict its drug-drug interaction (DDI) liabilities (i.e., transport, metabolism, or both). If the RDS of the hepatic clearance of the drug is sinusoidal uptake clearance (CLs in), even if the drug is eliminated mainly by hepatic metabolism, its DDI liability (as viewed from changes to systemic drug concentrations) is expected to be inhibition or induction of uptake transporters but not hepatic enzymes; however, this is true only if the condition required to maintain CLs in as the RDS is maintained. Here, we illustrate through theoretical simulations that the RDS condition may be violated in the presence of a DDI. That is, the RDS of a drug can switch from CLs in to all hepatobiliary clearances [i.e., metabolic/biliary clearance (CLmet + bile) and CLs in], leading to unexpected systemic DDIs, such as metabolic DDIs, when only transporter DDIs were anticipated. As expected, these analyses revealed that the RDS switch depends on the ratio of CLmet + bile to sinusoidal efflux clearance (CLs ef). Additional analyses revealed that for intravenously administered drugs, the RDS switch also depends on the magnitude of CLs in We analyzed published in vitro quantified hepatobiliary clearances and observed that most drugs have a CLmet + bile/CLs ef ratio < 4; hence, in practice, the magnitude of CLs in must be considered when establishing the RDS. These analyses provide insights previously not appreciated and a theoretical framework to predict DDI liabilities for drugs that are dual transporter-enzyme substrates.

Development of a Novel Maternal-Fetal Physiologically Based Pharmacokinetic Model I: Insights into Factors that Determine Fetal Drug Exposure through Simulations and Sensitivity Analyses.

Determining fetal drug exposure (except at the time of birth) is not possible for both logistical and ethical reasons. Therefore, we developed a novel maternal-fetal physiologically based pharmacokinetic (m-f-PBPK) model to predict fetal exposure to drugs and populated this model with gestational age-dependent changes in maternal-fetal physiology. Then, we used this m-f-PBPK to: 1) perform a series of sensitivity analyses to quantitatively demonstrate the impact of fetoplacental metabolism and placental transport on fetal drug exposure for various drug-dosing regimens administered to the mother; 2) predict the impact of gestational age on fetal drug exposure; and 3) demonstrate that a single umbilical venous (UV)/maternal plasma (MP) ratio (even after multiple-dose oral administration to steady state) does not necessarily reflect fetal drug exposure. In addition, we verified the implementation of this m-f-PBPK model by comparing the predicted UV/MP and fetal/MP AUC ratios with those predicted at steady state after an intravenous infusion. Our simulations yielded novel insights into the quantitative contribution of fetoplacental metabolism and/or placental transport on gestational age-dependent fetal drug exposure. Through sensitivity analyses, we demonstrated that the UV/MP ratio does not measure the extent of fetal drug exposure unless obtained at steady state after an intravenous infusion or when there is little or no fluctuation in MP drug concentrations after multiple-dose oral administration. The proposed m-f-PBPK model can be used to predict fetal exposure to drugs across gestational ages and therefore provide the necessary information to assess the risk of drug toxicity to the fetus.

SGN-B6A: A New Vedotin Antibody-Drug Conjugate Directed to Integrin Beta-6 for Multiple Carcinoma Indications.

Integrin beta-6, a component of the heterodimeric adhesion receptor alpha-v/beta-6, is overexpressed in numerous solid tumors. Its expression has been shown by multiple investigators to be a negative prognostic indicator in diverse cancers including colorectal, non-small cell lung, gastric, and cervical. We developed SGN-B6A as an antibody-drug conjugate (ADC) directed to integrin beta-6 to deliver the clinically validated payload monomethyl auristatin E (MMAE) to cancer cells. The antibody component of SGN-B6A is specific for integrin beta-6 and does not bind other alpha-v family members. In preclinical studies, this ADC has demonstrated activity in vivo in models derived from non-small cell lung, pancreatic, pharyngeal, and bladder carcinomas spanning a range of antigen expression levels. In nonclinical toxicology studies in cynomolgus monkeys, doses of up to 5 mg/kg weekly for four doses or 6 mg/kg every 3 weeks for two doses were tolerated. Hematologic toxicities typical of MMAE ADCs were dose limiting, and no significant target-mediated toxicity was observed. A phase I first-in-human study is in progress to evaluate the safety and antitumor activity of SGN-B6A in a variety of solid tumors known to express integrin beta-6 (NCT04389632).

A marijuana-drug interaction primer: Precipitants, pharmacology, and pharmacokinetics.

In the United States, the evolving landscape of state-legal marijuana use for recreational and/or medical purposes has given rise to flourishing markets for marijuana and derivative products. The popularity of these products highlights the relative absence of safety, pharmacokinetic, and drug interaction data for marijuana and its constituents, most notably the cannabinoids. This review articulates current issues surrounding marijuana terminology, taxonomy, and dosing; summarizes cannabinoid pharmacology and pharmacokinetics; and assesses the drug interaction risks associated with co-consuming marijuana with conventional medications. Existing pharmacokinetic data are currently insufficient to fully characterize potential drug interactions precipitated by marijuana constituents. As such, increasing awareness among researchers, clinicians, and federal agencies regarding the need to conduct well-designed in vitro and clinical studies is imperative. Mechanisms that help researchers navigate the legal and regulatory barriers to conducting these studies would promote rigorous evaluation of potential marijuana-drug interactions and inform health care providers and consumers about the possible risks of co-consuming marijuana products with conventional medications.