Hepatic Enzymes Relevant to the Disposition of (-)-∆9-Tetrahydrocannabinol (THC) and Its Psychoactive Metabolite, 11-OH-THC.
Gabriela I Patilea-VranaOlena AnoshchenkoJashvant D UnadkatPublished in: Drug metabolism and disposition: the biological fate of chemicals (2018)
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.