In this project, we will leverage two artificial intelligence (AI) tools and our company's unique Historeceptomicsapproach to rapidly discover an impactful new drug candidate to treat opioid use disorder (OUD) relapse viainhibition of withdrawal-associated negative affect (hyperkatifeia). The premise is that, unlike traditional drugdiscovery, which is conceptualized starting from drug targets (e.g., mu opioid receptor) and proceeds tophenotypes (e.g., pain relief), we can select specific cells or tissues that control a specific in vivo phenotype, inthis case hyperkatifeia, and work backward to identify a drug target specific for those cells, followed by thediscovery of a drug-like compound that modulates that drug target. This would be a revolutionary newparadigm for OUD drug discovery, translating extensive neuroscience knowledge about neural circuits thatelicit specific analgesic, addiction or withdrawal phenotypes directly into drugs, which has not been direct orefficient before. The critical technical advance that makes this paradigm possible is the capability to screenvirtually for drug candidates targeted at any human gene product. In other words, this drug discovery processcan start agnostic to the eventual target, with confidence that ligands for that target can rapidly be discoveredonce it is identified. This advance is made possible by our Historeceptomics technologies, which allow us toquery a specific cell or tissue and identify, with statistical significance, gene products exclusive or maximallyspecific to that tissue. The advance is also made temporally feasible by two historic breakthroughs in AI: 1)AlphaFold2 from DeepMind/Google, which has predicted accurate 3D structures, suitable for virtual chemicallibrary screening, for every human gene product for the first time in history; and 2) breakthrough AI bindingaffinity prediction for drug candidates to 3D structures developed by our partner Molsoft LLC, which uses aconvolutional neural network. We thus aim to 1) adapt our current Historeceptomics Tissue Search softwarefeature to single cell RNA seq data profiling of the human amygdala and identify gene products exclusive toRspo2+ neurons, which control hyperkatifeia. 2) perform an ultra-large virtual library screen using our AIbinding affinity prediction of the 1.5 billion Enamine Real database of drug-like chemical compounds for drugcandidates that bind to the AlphaFold2-generated, manually optimized, 3D structure of the thus-identified geneproduct, 3) test validated hit compounds for their ability to reduce the negative affect of protracted opioidabstinence and relapse in vivo. A drug targeting reinstatement in people suffering from OUD is arguably moreimpactful than other anti-addiction approaches, because it could synergize with medication-assisted treatment(MAT), psychosocial therapies and community-based recovery supports by neurochemically reducing theimpact of triggers conditioned by avoidance of withdrawal symptoms. We will leverage AI to discover such adrug within three years with modest investment, which would both advertise the paradigm of tissue/cell focuseddrug discovery and the power of AI to accelerate drug discovery for OUD/SUD.
Public Health Relevance Statement: Project Narrative
Discovering drugs that treat relapse in substance use disorder is a major challenge. The challenge might be
met if novel drugs could be focused in their activity to specific neuronal circuits in the brain, which mediate
addiction behavior conditioned negatively by withdrawal. Breakthrough AI tools for computational drug
discovery now make such focusing possible, and we will seek proof of principle for this approach in this project.
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