1,368
Views
24
CrossRef citations to date
0
Altmetric
Articles

The failings of per se limits to detect cannabis-induced driving impairment: Results from a simulated driving study

ORCID Icon, , , &
Pages 102-107 | Received 13 May 2020, Accepted 10 Nov 2020, Published online: 05 Feb 2021
 

Abstract

Objective

Many jurisdictions use per se limits to define cannabis-impaired driving. Previous studies, however, suggest that THC concentrations in biological matrices do not reliably reflect cannabis dose and are poorly correlated with magnitude of driving impairment. Here, we first review a range of concerns associated with per se limits for THC. We then use data from a recent clinical trial to test the validity of a range of extant blood and oral fluid THC per se limits in predicting driving impairment during a simulated driving task.

Methods

Simulated driving performance was assessed in 14 infrequent cannabis users at two timepoints (30 min and 3.5 h) under three different conditions, namely controlled vaporization of 125 mg (i) THC-dominant (11% THC; <1% CBD), (ii) THC/CBD equivalent (11% THC; 11% CBD), and (iii) placebo (<1% THC & CBD) cannabis. Plasma and oral fluid samples were collected before each driving assessment. We examined whether per se limits of 1.4 and 7 ng/mL THC in plasma (meant to approximate 1 and 5 ng/mL whole blood) and 2 and 5 ng/mL THC in oral fluid reliably predicted impairment (defined as an increase in standard deviation of lateral position (SDLP) of >2 cm relative to placebo).

Results

For all participants, plasma and oral fluid THC concentrations were over the per se limits used 30 min after vaporizing THC-dominant or THC/CBD equivalent cannabis. However, 46% of participants failed to meet SDLP criteria for driving impairment. At 3.5 h post-vaporization, 57% of participants showed impairment, despite having low concentrations of THC in both blood (median = 1.0 ng/mL) and oral fluid (median = 1.0 ng/mL). We highlight two individual cases illustrating how (i) impairment can be minimal in the presence of a positive THC result, and (ii) impairment can be profound in the presence of a negative THC result.

Conclusions

There appears to be a poor and inconsistent relationship between magnitude of impairment and THC concentrations in biological samples, meaning that per se limits cannot reliably discriminate between impaired from unimpaired drivers. There is a pressing need to develop improved methods of detecting cannabis intoxication and impairment.

Acknowledgments

We thank Tilray for supplying the cannabis to conduct this study, and Storz and Bickel for supplying the Mighty Medic vaporizers. We also sincerely thank Prof. Nicholas Lintzeris and Dr. Jordyn Stuart for their contributions.

Disclosure statement

Ryan Vandrey has received consulting fees from Zynerba Pharmaceuticals, Battelle Memorial Institute, and Canopy Health Innovations Inc and has received compensation for being on the advisory boards for Insys Therapeutics, Brain Solutions Inc., and The Realm of Caring Foundation. Iain McGregor acts as a consultant to Kinoxis Therapeutics, has received compensation for sitting on the advisory board of BOD Australia and has received speaker fees from Janssen. In addition, Iain McGregor holds patent AU2017904438 pending, and patents WO2019/227167 and WO2019071302 that are relevant to cannabinoid therapeutics.

Additional information

Funding

This study was supported by the Lambert Initiative for Cannabinoid Therapeutics, a philanthropically funded independent center for medicinal cannabis research at the University of Sydney.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 331.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.