Keynote Presentation: Assessing the Impact of Medical Cannabis on Autism using Pharmacometabolomics and Machine Learning with Live Q&A

Autism spectrum disorder is a group of neurodevelopmental conditions that impact behavior, communication, social interaction, and learning abilities. There has been great interest in using medical cannabis to reduce the severity of certain behavioral aspects, however results remain difficult to assess as there are no laboratory tools available for this emerging treatment modality. Using Cannabis Responsive biomarkers obtained in saliva, we were able to measure physiologic levels of numerous metabolites before and after medical cannabis treatment. We discovered 23 potential Cannabis Response biomarkers that exhibited change towards the mean established by our typically developing participants. These biomarkers reflect chemical pathways involved in inflammation, neurotransmission, endocannabinoid signaling and other neurologic processes. We also used machine learning to determine which biomarkers were associated with specific phytocannabinoid compounds (THC, CBD and CBG), non-cannabinoid cannabis plant molecules with synergistic effects, and any compounds that might distinguish autism from typically developing participants.

• Siani-Rose, M., Cox, S., Goldstein, B., Abrams, D., Taylor, M., & Kurek, I. (2023). Cannabis-Responsive Biomarkers: A Pharmacometabolomics-Based Application to Evaluate the Impact of Medical Cannabis Treatment on Children with Autism Spectrum Disorder. Cannabis and Cannabinoid Research, 8(1), 126-137.
• Quillet, J. C., Siani-Rose, M., McKee, R., Goldstein, B., Taylor, M., & Kurek, I. (2023). A machine learning approach for understanding the metabolomics response of children with autism spectrum disorder to medical cannabis treatment. Scientific Reports, 13(1), 13022.
• Siani-Rose, M., McKee, R., Cox, S., Goldstein, B., Abrams, D., Taylor, M., & Kurek, I. (2023). The potential of salivary lipid-based Cannabis-responsive biomarkers to evaluate medical cannabis treatment in children with autism spectrum disorder. Cannabis and Cannabinoid Research, 8(4), 642-656.

Learning Objectives:

1. Define pharmacometabolomics and its potential to objectively measure impact of cannabis therapy.

2. Discuss pharmacometabolomics applications, including predictive, responsive, dosing guidance and monitoring and diagnostic.

3. Explain how identification of biomarkers by machine learning may lead to better understanding of the uses and impact of medical cannabis.