Thomas K. Henthorn (1), Cristina Sempio (1), Cinnamon Bidwell (2), Kent Hutchinson (2), Klawitter J (1), Christians U (1) and Marilyn A. Huestis (3)
(1) Department of Anesthesiology, University of Colorado School of Medicine, Aurora, CO, USA (2) Institute of Cognitive Science, University of Colorado, Boulder, CO, USA ( 3) The Lambert Center for the Study of Medicinal Cannabis and Hemp, Thomas Jefferson University, Philadelphia, PA, USA
Introduction: Population pharmacokinetic (popPK) modeling of delta-9-tetrahydrocannabinol (THC), but not including those of the major metabolites, has been performed in a clinical research setting with dense plasma sampling following a closely monitored administration by smoking and vaping.1-3 To interpret sparse, observational plasma THC and metabolite concentrations, we aimed to develop a comprehensive popPK model of THC and its metabolites as a Bayesian prior for further modeling of sparse, observational data.
Objectives:
- Develop a population PK model of THC, 11-OH-THC and THCCOOH from a Phase 1 clinical trial conducted at the National Institute of Drug Abuse (NIDA)
- Estimate daily consumption of THC from sparse data (plasma THC, 11-OH-THC, and THCCOOH concentrations) in a cohort of regular cannabis users in Boulder, Colorado.
Methods: Previously at NIDA, six sequestered subjects smoked in a rigorously-paced manner two different concentrations of marijuana cigarettes (1.75% and 3.55% THC) over 10 min one week apart in a within subject, randomized, crossover, placebo-controlled study. Frequent blood samples were obtained during and immediately after each smoking event and then less frequently for one week for the measurement of THC, 11-OH-THC and THCCOOH by GC-MS.4 A multicompartment popPK model was developed using Phoenix NMLE 8.1. In Colorado, blood samples were obtained from 16 regular users of cannabis at four time points: recruitment, in a mobile lab immediately before smoking in their home, upon returning to the mobile lab and then again one hour later for analysis of THC and metabolites by LC-MS/MS5. These data were analyzed with the Bayesian prior from the dense popPK analysis, including estimates of (1) daily THC consumption prior to recruitment, (2) daily THC consumption in the interval between recruitment and home-smoking and (3) the dose consumed during the home-smoking event.
Results: For the NIDA data, a 3-compartment PK model of THC was superior to 1- or 2-compartment models, assuming a fraction absorbed of 0.251, (typical values: V1=28.5L , V2=45.6L, V3=3372L, Q2=1.35L/min, Q3=1.31L/min and Cle=0.72 L/min) with extension to metabolite kinetics (typical values: Clethc->11-oh-thc=0.43L/min, Cle11-oh-thc-> thccooh=1.62L/min, Clethccooh=0.12 L/min). In the Colorado cohort, baseline daily THC consumption was estimated to be 2.56+3.27 (mean+SD) NIDA cannabis cigarette equivalents (5.6% THC). Consumption dropped to 0.87+0.97 cigarettes in the interval prior to home-smoking and 0.45+0.26 while in their home that was estimated to have begun 14 minutes prior to returning to the mobile lab. Correlations between these model-derived estimates and survey estimates of daily THC consumption and amount smoked in the home were significant (p<0.01).
Conclusion: Our 3-compartment THC model is very similar to those previously described.1-3 We have successfully extended population THC modeling to include two of its commonly measured metabolites. This modeling development is important because THC metabolites are often the only measurable constituents in blood and urine in observational cannabis studies because of low THC concentrations soon after consumption due its extensive tissue distribution. THCCOOH and THC-COOH-glucuronide concentrations are more persistent as their production clearances exceed their elimination clearances. A population model of THC and its metabolites provides a valid supplement to generally non-quantitative cannabis consumption questionaires. Without a population PK approach, often randomly obtained, sparse THC/metabolite blood concentration data from observational studies were difficult for investigators to interpret. We show that a popPK model of THC and its metabolites results in quantitative estimates of THC exposure even in observational studies with sparse data. Extending the current population PK modeling to include THCCOOH-glucuronide would be highly useful since this metabolite persists in blood and urine even longer than THCCOOH.
References:
[1] Heuberger JA, et al., Clin Pharmacokinet 2015; 54: 209-219
[2] Karschner EL, et al., Clin Chem 2011; 57: 66-75
[3] Newmeyer MN, et al., Clin Chem 2016; 62: 1579-1592
[4] Huestis MA, et al., J Anal Toxicol, 1992; 16:276-282
[5] Klawitter J, et al., Ther Drug Monit, 2017; 39:556-564
Reference: PAGE 28 (2019) Abstr 9015 [www.page-meeting.org/?abstract=9015]
Poster: Drug/Disease Modelling - CNS