Cristina Sempio1, L Cinnamon Bidwell2,3, Kent Hutchison2,3, Marilyn A Huestis4, Jost Klawitter1, Uwe Christians1, Thomas K Henthorn1,5. 1. Department of Anesthesiology, University of Colorado School of Medicine, Aurora, Colorado. 2. Institute of Cognitive Science, University of Colorado, Boulder, Colorado. 3. Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado. 4. Huestis & Smith Toxicology, LLC, Saverna Park, Maryland; and. 5. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, Colorado.
Abstract
BACKGROUND: Self-report questionnaires, weighing products consumed, and Δ9-tetrahydrocannabinol (THC) biomarkers are established techniques for estimating cannabis exposure. Population pharmacokinetic modeling of plasma THC and metabolite concentrations by incorporating self-reported and weighed products as covariates could improve estimates of THC exposure in regular cannabis users. METHODS: In this naturalistic study, blood samples were obtained from 36 regular smokers of cannabis for analysis of THC and its 2 metabolites at 4 time points: recruitment and during an experimental mobile laboratory assessment that included 3 time points: before, immediately after, and 1 hour after ad libitum legal market flower use. These data were analyzed using an established model of population pharmacokinetics developed from laboratory-controlled cannabis administration data. Elimination and metabolite production clearances were estimated for each subject as well as their daily THC doses and the dose consumed during the ad libitum event. RESULTS: A statistically significant correlation existed between the daily THC dose estimated by self-report questionnaire and population pharmacokinetic modeling (correlation coefficient = 0.79, P < 0.05) between the weighed cannabis smoked ad libitum and that estimated by population pharmacokinetic modeling (correlation coefficient = 0.71, P < 0.05). CONCLUSION: Inclusion of self-reported questionnaire data of THC consumption improved pharmacokinetic model-derived estimates based on measured THC and metabolite concentrations. In addition, the pharmacokinetic-derived dose estimates for the ad libitum smoking event underestimated the THC consumption compared with the weighed amount smoked. Thus, the subjects in this study, who smoked ad libitum and used cannabis products with high concentrations of THC, were less efficient (lower bioavailability) compared with computer-paced smokers of low potency, NIDA cannabis in a laboratory setting.
BACKGROUND: Self-report questionnaires, weighing products consumed, and Δ9-tetrahydrocannabinol (THC) biomarkers are established techniques for estimating cannabis exposure. Population pharmacokinetic modeling of plasma THC and metabolite concentrations by incorporating self-reported and weighed products as covariates could improve estimates of THC exposure in regular cannabis users. METHODS: In this naturalistic study, blood samples were obtained from 36 regular smokers of cannabis for analysis of THC and its 2 metabolites at 4 time points: recruitment and during an experimental mobile laboratory assessment that included 3 time points: before, immediately after, and 1 hour after ad libitum legal market flower use. These data were analyzed using an established model of population pharmacokinetics developed from laboratory-controlled cannabis administration data. Elimination and metabolite production clearances were estimated for each subject as well as their daily THC doses and the dose consumed during the ad libitum event. RESULTS: A statistically significant correlation existed between the daily THC dose estimated by self-report questionnaire and population pharmacokinetic modeling (correlation coefficient = 0.79, P < 0.05) between the weighed cannabis smoked ad libitum and that estimated by population pharmacokinetic modeling (correlation coefficient = 0.71, P < 0.05). CONCLUSION: Inclusion of self-reported questionnaire data of THC consumption improved pharmacokinetic model-derived estimates based on measured THC and metabolite concentrations. In addition, the pharmacokinetic-derived dose estimates for the ad libitum smoking event underestimated the THC consumption compared with the weighed amount smoked. Thus, the subjects in this study, who smoked ad libitum and used cannabis products with high concentrations of THC, were less efficient (lower bioavailability) compared with computer-paced smokers of low potency, NIDA cannabis in a laboratory setting.
Authors: Nathalie A Desrosiers; Sarah K Himes; Karl B Scheidweiler; Marta Concheiro-Guisan; David A Gorelick; Marilyn A Huestis Journal: Clin Chem Date: 2014-02-21 Impact factor: 8.327
Authors: Jules A A C Heuberger; Zheng Guan; Olubukayo-Opeyemi Oyetayo; Linda Klumpers; Paul D Morrison; Tim L Beumer; Joop M A van Gerven; Adam F Cohen; Jan Freijer Journal: Clin Pharmacokinet Date: 2015-02 Impact factor: 6.447
Authors: Cristina Sempio; Marilyn A Huestis; Susan K Mikulich-Gilbertson; Jost Klawitter; Uwe Christians; Thomas K Henthorn Journal: Br J Clin Pharmacol Date: 2020-01-20 Impact factor: 4.335
Authors: Jelena Klawitter; Cristina Sempio; Sophie Mörlein; Erik De Bloois; Jacek Klepacki; Thomas Henthorn; Maureen A Leehey; Edward J Hoffenberg; Kelly Knupp; George S Wang; Christian Hopfer; Greg Kinney; Russell Bowler; Nicholas Foreman; Jeffrey Galinkin; Uwe Christians; Jost Klawitter Journal: Ther Drug Monit Date: 2017-10 Impact factor: 3.681
Authors: David A Gorelick; Robert S Goodwin; Eugene Schwilke; David M Schwope; William D Darwin; Deanna L Kelly; Robert P McMahon; Fang Liu; Catherine Ortemann-Renon; Denis Bonnet; Marilyn A Huestis Journal: J Anal Toxicol Date: 2012-10-16 Impact factor: 3.367