Complexity of Translating Analytics to Recent Cannabis Use and Impairment

Abstract

Abstract While current analytical methodologies can readily identify cannabis use, definitively establishing recent use within the impairment window has proven to be far more complex, requiring a new approach. Recent studies have shown no direct relationship between impairment and Δ9-tetra-hydrocannabinol (Δ9-THC) concentrations in blood or saliva, making legal “per se” Δ9-THC limits scientifically unjustified. Current methods that focus on Δ9-THC and/or metabolite concentrations in blood, saliva, urine, or exhaled breath can lead to false-positive results for recent use due to the persistence of Δ9-THC well outside of the typical 3–4 h window of potential impairment following cannabis inhalation. There is also the issue of impairment due to other intoxicating substances—just because a subject exhibits signs of impairment and cannabis use is detected does not rule out the involvement of other drugs. Compounding the matter is the increasing popularity of hemp-derived cannabidiol (CBD) products following passage of the 2018 Farm Bill, which legalized industrial hemp in the United States. Many of these products contain varying levels of Δ9-THC, which can lead to false-positive tests for cannabis use. Furthermore, hemp-derived CBD is used to synthesize Δ8-THC, which possesses psychoactive properties similar to Δ9-THC and is surrounded by legal controversy. For accuracy, analytical methods must be able to distinguish the various THC isomers, which have identical masses and exhibit immunological cross-reactivity. A new testing approach has been developed based on exhaled breath and blood sampling that incorporates kinetic changes and the presence of key cannabinoids to detect recent cannabis use within the impairment window without the false-positive results seen with other methods. The complexity of determining recent cannabis use that may lead to impairment demands such a comprehensive method so that irresponsible users can be accurately detected without falsely accusing responsible users who may unjustly suffer harsh, life-changing consequences.