Development of therapeutic candidates for stimulant use disorder using AI-driven platform for focused drug discovery and accelerated development

PROJECT SUMMARY Stimulant use disorder (StUD) is emerging as a serious global threat to health, with an exponential rise in prevalence and overdose deaths. In 2022, 60 million people reported past year use of stimulants, with 35 million people meeting the criteria for a StUD Currently, there are no FDA- approved medications for treating StUD, leaving only costly and often ineffective abstinence support and behavioral therapies, as evidenced by relapse rates as high as 80–90%. This underscores StUD as a critical unmet medical and social need. VeriSIM Life, a leader in artificial intelligence (AI)-driven drug discovery, uses its patented BIOiSIM® platform to predict the pharmacokinetics (PK), pharmacodynamics (PD), and toxicity profiles of potential drug compounds. This platform employs AI/ML-parameterized whole-body physiologically based PK modeling, designed to accurately predict a drug's clinical value early in development, thereby reducing the need for extensive preclinical testing and significantly enhancing return on investment. During Phase I, we developed, validated, and integrated a specialized PK-PD module for StUD drug development into our hybrid whole-body iPBPK-PD platform, BIOiSIM. The primary goal of the STTR Phase II application is to develop novel therapeutic agents against StUD using an AI-driven platform for new drug synthesis with the following predictions of the PK, PD, and toxicity profiles. The current Phase II proposal continues the work performed during Phase I when a comprehensive AI-based PK-PD module for StUD drug development was developed, validated, and integrated into the hybrid whole-body iPBPK-PD platform BIOiSIM. Aim 1 of the Phase II study is devoted to the expansion of BIOiSIM’s AI-driven model to enable the prediction of drug PD parameters characterized by targeting the Ghrelin-1 pathway with the following virtual synthesis of potent inhibitors with beneficial PK and safety profiles. Aim 2 is specifically focused on the AI/ML-driven lead optimization of predicted hit molecules to increase their selectivity, potency, and safety. Aim 3 is dedicated to the AI/ML-driven prediction of the synthesis methods for the lead compounds, the characterization of their physicochemical properties, and preclinical testing of the main PK and PD parameters predicted by BIOiSIM®. By harnessing AI technology, this project aims to significantly expedite the drug discovery and development process, delivering a drug candidate with a favorable profile ready to proceed to the pre-IND stage by the project's end.