Objective
To utilize high-throughput screening and regenerative AI analysis to identify naturally derived and synthetically modified cannabinoids, terpenoids, and flavonoids with potential efficacy and safety profiles for treating cancer, Alzheimer's disease, seizures, and other conditions.

Methods
In collaboration with Texas A&M University and sponsored by Rare Earth Genomics (REG), we focused on utilizing hemp-derived molecules for our research. Advanced automation and screening platforms at our core facilities were employed, including Beckman Echo acoustic dispensers integrated into Access workstations with Cytomat cell culture incubators for 24/7 compound addition. The Tecan Evo 200 workstation with multimode readers and high-capacity Liconic incubators facilitated rapid dispensing and environmental control. A dual-arm Beckman-Coulter Biomek i7 liquid handling platform enabled precise, high-throughput liquid transfers using span-8, 96-multichannel, and single-channel dispensing heads. High-content imaging was conducted using the GE Healthcare IN Cell Analyzer 6000 and Molecular Devices ImageXpress Micro Confocal Microscope, providing detailed analysis of 2D, 3D, and spheroid cultures. We integrated regenerative AI algorithms with Texas A&M’s large drug databases to perform comparative analyses and pathway mapping, assisting in candidate selection for testing. Additionally, we are collaborating to obtain a Texas Compassionate Use Program license to study THC and psychoactive compounds, aiming to expand our research into these areas.

Learning Objectives:

• Learn how high-throughput screening and AI accelerate therapeutic cannabinoid discovery
• Understand Texas A&M and Rare Earth Genomics' collaboration on hemp-derived molecules and THC studies
• Explore advanced automation and imaging technologies in cannabis-derived drug development