Development of the AI-driven model for anti-SUD drug development based on neuronal plasticity

项目总结 兴奋剂使用率，包括非法(如甲基苯丙胺和可卡因)和处方(如： 在阿片类药物流行之后激增，由于相关的隔离而恶化 随着新冠肺炎的流行。与阿片类药物使用障碍不同，目前没有FDA批准的 治疗兴奋剂使用障碍(STUD)的药物，只留下禁欲支持 和行为矫正疗法。这些都是昂贵的治疗方法，疗效不佳，因为 高复发率(60%-90%)就是明证。兴奋剂使用障碍显然是一种未得到满足的需求。 VeriSIM Life是一家创新公司，开发和利用人工智能(AI)和 机器学习(ML)技术，实现更快的药物发现和开发。我们的专利 BIOiSIM平台实现小分子药代动力学和药效学预测 通过基于AI/ML的参数化全身生理学建模。它的设计目的是 在人体试验前准确预测研究药物的临床价值。全栈人工智能- 启用的生物模拟模型显著减少了所需的动物试验次数 通过流水线推进治疗，加快药物开发，显著 提高投资回报。此SBIR应用程序的主要目标是开发、验证 并利用与核心BIOiSIM平台相结合的可靠且准确的AI驱动工具来 加速发现和开发用于缓解STARD的药物。 目前的第一阶段提案将通过两个相辅相成的方案开始解决这一目标 接近了。人工智能驱动的中枢神经系统药物药代动力学模型研究进展 在目标1中，将进行具有以下虚拟化合物文库筛选的分配。 这将得到体内化合物子集的概念验证的支持 药物动力学。目标2将专注于药效学预测模型的发展 用于兴奋剂使用障碍药物的发现和开发。重点将放在潜力上 通过对神经可塑性的深入分析，确定了调节神经元可塑性的治疗靶点 临床前和临床文献。这将由子集的概念验证验证来支持 使用可卡因和可卡因进行临床前静脉自我给药研究的化合物 冰毒。拟议的AI/ML驱动的方法预计将显著加快 通过指导努力开发新的兴奋剂使用障碍药物的过程 具有最佳ADME和药效学特性的化合物。这样的方法将创造 快速开发负担得起的有效治疗方法的途径 其他迹象。

PROJECT SUMMARY Rates of stimulant use, both illicit (e.g. methamphetamine and cocaine) and prescription (e.g. Adderall) are surging on the heels of the opioid epidemic, worsened by the isolation associated with the COVID-19 pandemic. Unlike opioid use disorder, there are currently no FDA-approved medications for the treatment of stimulant use disorder (StUD), leaving only abstinence support and behavioral modification therapies. These are costly treatments with poor efficacy, as evidenced by the high rate of relapse (60-90%). Stimulant use disorder is clearly an unmet need. VeriSIM Life is an innovative company developing and utilizing artificial intelligence (AI) and machine learning (ML) technologies for faster drug discovery and development. Our patented BIOiSIM platform enables prediction of small molecule pharmacokinetics and pharmacodynamics via an AI/ML-parameterized whole-body physiologically based modelling. It is designed to accurately predict the clinical value of investigational drugs before human trials. The full-stack AI- enabled bio-simulation models significantly reduce the number of animal tests required for advancing therapeutics through the pipeline, accelerating drug development and markedly increasing return on investment. The primary goal of this SBIR application is to develop, validate and utilize a reliable and accurate AI-driven tool incorporated with the core BIOiSIM platform to accelerate discovery and development of pharmaceuticals intended for the mitigation of StUD. The current Phase I proposal will begin to address this goal through two complementary approaches. Development of the AI-driven pharmacokinetic modeling specific to CNS drug distribution with the following screening of virtual compound libraries will be performed in Aim 1. This will be supported by proof-of-concept validation of a subset of compounds with in vivo pharmacokinetics. Aim 2 will focus on development of pharmacodynamics prediction modeling for stimulant use disorder drug discovery and development. The focus will be on potential therapeutic targets that modulate neuronal plasticity, identified through an in-depth analysis of the preclinical and clinical literature. This will be supported by proof-of-concept validation of a subset of compounds with preclinical intravenous self-administration studies using cocaine and methamphetamine. The proposed AI/ML-driven approach is expected to markedly accelerate the development process for new stimulant use disorder medications by directing efforts to compounds with optimal ADME and pharmacodynamic properties. Such an approach will create an avenue for fast-track development of affordable and efficacious therapeutics for StUD among other indications.