AIMS: To develop and validate empirically a mathematical model for identifying new cannabis use in chronic, daily cannabis smokers. DESIGN: Models were based on urinary creatinine-normalized (CN) cannabinoid excretion in chronic cannabis smokers. SETTING: For model development, participants resided on a secure research unit for 30 days. For model validation, participants were abstinent with daily observed urine specimens for 28 days. PARTICIPANTS: A total of 48 (model development) and 67 (model validation) daily cannabis smokers were recruited. MEASUREMENTS: All voided urine was collected and analyzed for 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) by gas chromatography-mass spectrometry (GCMS; limit of quantification 2.5 ng/ml) and creatinine (mg/ml). Urine THCCOOH was normalized to creatinine, yielding ng/mg CN-THCCOOH concentrations. Urine concentration ratios were determined from 123,513 specimen pairs collected 2-30 days apart. FINDINGS: A mono-exponential model (with two parameters, initial urine specimen CN-THCCOOH concentration and time between specimens), based on the Marquardt-Levenberg algorithm, provided a reasonable data fit. Prediction intervals with varying probability levels (80, 90, 95, 99%) provide upper ratio limits for each urine specimen pair. Ratios above these limits suggest cannabis re-use. Disproportionate numbers of ratios were higher than expected for some participants, prompting development of two additional rules that avoid misidentification of re-use in participants with unusual CN-THCCOOH excretion patterns. CONCLUSIONS: For the first time, a validated model is available to aid in the differentiation of new cannabis use from residual creatinine-normalized 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (CN-THCCOOH) excretion in chronic, daily cannabis users. These models are valuable for clinicians, toxicologists and drug treatment staff and work-place, military and criminal justice drug-testing programs.
AIMS: To develop and validate empirically a mathematical model for identifying new cannabis use in chronic, daily cannabis smokers. DESIGN: Models were based on urinary creatinine-normalized (CN) cannabinoid excretion in chronic cannabis smokers. SETTING: For model development, participants resided on a secure research unit for 30 days. For model validation, participants were abstinent with daily observed urine specimens for 28 days. PARTICIPANTS: A total of 48 (model development) and 67 (model validation) daily cannabis smokers were recruited. MEASUREMENTS: All voided urine was collected and analyzed for 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) by gas chromatography-mass spectrometry (GCMS; limit of quantification 2.5 ng/ml) and creatinine (mg/ml). Urine THCCOOH was normalized to creatinine, yielding ng/mg CN-THCCOOH concentrations. Urine concentration ratios were determined from 123,513 specimen pairs collected 2-30 days apart. FINDINGS: A mono-exponential model (with two parameters, initial urine specimen CN-THCCOOH concentration and time between specimens), based on the Marquardt-Levenberg algorithm, provided a reasonable data fit. Prediction intervals with varying probability levels (80, 90, 95, 99%) provide upper ratio limits for each urine specimen pair. Ratios above these limits suggest cannabis re-use. Disproportionate numbers of ratios were higher than expected for some participants, prompting development of two additional rules that avoid misidentification of re-use in participants with unusual CN-THCCOOH excretion patterns. CONCLUSIONS: For the first time, a validated model is available to aid in the differentiation of new cannabis use from residual creatinine-normalized 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (CN-THCCOOH) excretion in chronic, daily cannabis users. These models are valuable for clinicians, toxicologists and drug treatment staff and work-place, military and criminal justice drug-testing programs.
Authors: Ross H Lowe; Tsadik T Abraham; William D Darwin; Ronald Herning; Jean Lud Cadet; Marilyn A Huestis Journal: Drug Alcohol Depend Date: 2009-07-23 Impact factor: 4.492
Authors: Dustin C Lee; Nicolas J Schlienz; Erica N Peters; Robert H Dworkin; Dennis C Turk; Eric C Strain; Ryan Vandrey Journal: Drug Alcohol Depend Date: 2018-11-15 Impact factor: 4.492
Authors: Marilyn A Huestis; Cristina Sempio; Matthew N Newmeyer; Maria Andersson; Allan J Barnes; Osama A Abulseoud; Benjamin C Blount; Jennifer Schroeder; Michael L Smith Journal: J Anal Toxicol Date: 2020-10-12 Impact factor: 3.367
Authors: Mallory J E Loflin; Brian D Kiluk; Marilyn A Huestis; Will M Aklin; Alan J Budney; Kathleen M Carroll; Deepak Cyril D'Souza; Robert H Dworkin; Kevin M Gray; Deborah S Hasin; Dustin C Lee; Bernard Le Foll; Frances R Levin; Joshua A Lile; Barbara J Mason; Aimee L McRae-Clark; Ivan Montoya; Erica N Peters; Tatiana Ramey; Dennis C Turk; Ryan Vandrey; Roger D Weiss; Eric C Strain Journal: Drug Alcohol Depend Date: 2020-04-26 Impact factor: 4.492
Authors: Rachel A Rabin; Mera S Barr; Michelle S Goodman; Yarissa Herman; Konstantine K Zakzanis; Stephen J Kish; Michael Kiang; Gary Remington; Tony P George Journal: Neuropsychopharmacology Date: 2017-04-26 Impact factor: 7.853
Authors: Kathleen M Carroll; Brian D Kiluk; Charla Nich; Elise E DeVito; Suzanne Decker; Donna LaPaglia; Dianne Duffey; Theresa A Babuscio; Samuel A Ball Journal: Drug Alcohol Depend Date: 2014-01-31 Impact factor: 4.492
Authors: Nathalie A Desrosiers; Dayong Lee; Karl B Scheidweiler; Marta Concheiro-Guisan; David A Gorelick; Marilyn A Huestis Journal: Anal Bioanal Chem Date: 2013-12-01 Impact factor: 4.142