AI Platform to Discover Therapeutics for Substance Use Disorders

ABSTRACT Substance use disorders (SUDs) are a global burden in healthcare, with over 35 million people afflicted worldwide. Both those who suffer from SUDs, their family members and caretakers suffer from higher rates of suicide and mental health disorders. Unfortunately, medicine and science has not developed new treatment options for these populations, mostly due to the highly complex physiology and pharmacology of central nervous system (CNS)-related diseases. In this proposal, we will use a hypothesis-based system to identify, validate and screen potential known and unknown targets to treat the behavioral phenotype of addiction. Recently, medications which target the glucagon-like peptide 1 (GLP-1) endogenous system and pathways have demonstrated a reduction in the phenotype of addiction. This system interacts with the dopaminergic mesolimbic system, which is strongly implicated in a reduction in ‘craving’ for addictive substances, including opiates, nicotine, alcohol and hedonistic over consumption. We propose using artificial intelligence (AI)-based systems to interrogate these different pathways which all work within the reward-based addiction system. The overall premise and goals of this project is to leverage the combined expertise of the research team to develop: 1) a set of curated training datasets and machine learning (ML) models that accelerate key drug discovery tasks, 2) mechanistic targets for GLP-1 receptor agonist pathways which can be explored experimentally, and 3) preliminary hits for compounds that can modulate the group(s) of AI-recommended targets. This will enhance our understanding of reward system physiology and pharmacology, as well as lay the foundation to develop an AI-based platform for drug repurposing using similar phenotype-based treatment options for CNS-related disease. Graph Neural Network (GNN) models will be trained to identify: 1) possible targets which singly show a reduction in addictive behaviors, and 2) grouping of two or more targets that work together to reduce addictive phenotypes. Top pathways and targets (differentially expressed genes or signaling pathways) will be validated using known pharmacological modulators of those targets. Ligand-based drug screening will also be used for pathway/target validation. A set of promising compounds will be identified that interact with possible targets of interest with high potency and selectivity. Successful completion of this Phase I project will demonstrate feasibility of the ML models, validation and drug target screening using the compound libraries available in the Assay Development and Drug Repurposing Core at Michigan State University. In addition, this phase includes planning and development for Phase II, where we continue to explore, refine and validate targets as well as use pre-clinical model behavioral testing, confirming discovery targets. The future goals of this project include development of these ML models into a platform that pharmaceutical companies can use to repurpose currently FDA-approved medication by symptom and/or behavioral phenotype.