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

基于神经元可塑性的人工智能驱动抗SUD药物开发模型的开发

• 批准号: 10467528
• 负责人: Courtney A Miller
• 金额: $ 31.23万
• 依托单位: VERISIM LIFE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10467528
• 关键词: Abstinence; Adderall; Address; Affect; Aftercare; Agonist; Algorithms; American; Anhedonia; Animal Model; Animal Testing; Animals; Area; Artificial Intelligence; Artificial Intelligence platform; Behavior Therapy; Blood - brain barrier anatomy; Brain; COVID-19 pandemic; Chemicals; Clinical; Cocaine; Computer Models; Custom; Descriptor; Development; Dose; Drug Compounding; Drug Kinetics; Drug Targeting; Drug Transport; Environment; Evaluation; FDA approved; Generations; Goals; Heel; Human; Hybrids; Individual; Infrastructure; Inpatients; Intravenous; Investigational Drugs; Investments; Legal patent; Libraries; Life; Ligands; Literature; Machine Learning; Medical; Methamphetamine; Modality; Modeling; Neuronal Plasticity; Neurotransmitters; Outcome; Permeability; Pharmaceutical Preparations; Pharmacodynamics; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Phase; Physiological; Process; Property; Protein Phosphatase 2A Regulatory Subunit PR53; Rehabilitation therapy; Relapse; Reporting; Scientist; Self Administration; Small Business Innovation Research Grant; Software Engineering; Stimulant; Surveys; System; Technology; Therapeutic; Toxic effect; Treatment Cost; Validation; abuse liability; aged; antagonist; artificial intelligence algorithm; base; blood-brain barrier penetration; clinical predictors; cocaine use; compliance behavior; cost; design; drug development; drug discovery; drug distribution; hypocretin; in silico; in vivo; innovation; lead optimization; machine learning model; model development; models and simulation; neuropharmacologic agent; opioid epidemic; opioid use disorder; overdose death; pharmacokinetic model; pharmacokinetics and pharmacodynamics; physiologically based pharmacokinetics; pre-clinical; predictive modeling; prescription stimulants; primary outcome; programs; screening; simulation; small molecule; stimulant use; stimulant use disorder; therapeutic target; tool; virtual; virtual screening

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.

项目摘要 兴奋剂使用率，包括非法（如甲基苯丙胺和可卡因）和处方（如 Adderall）在阿片类药物流行之后激增，由于与之相关的隔离而恶化 新冠肺炎大流行与阿片类药物使用障碍不同，目前还没有FDA批准的 用于治疗兴奋剂使用障碍（StUD）的药物，仅留下禁欲支持 和行为矫正疗法这些都是昂贵的治疗效果差，因为 复发率高（60-90%）。兴奋剂使用障碍显然是一个未满足的需求。 VeriSIM Life是一家开发和利用人工智能（AI）的创新型公司， 机器学习（ML）技术，以加快药物发现和开发。我们的专利 BIOiSIM平台可预测小分子药代动力学和药效学 通过AI/ML参数化的基于生理学的全身建模。它旨在 在人体试验前准确预测研究药物的临床价值。全栈AI- 启用的生物模拟模型大大减少了所需的动物试验数量， 通过管道推进治疗，加速药物开发， 提高投资回报率。此SBIR应用程序的主要目标是开发、验证 并利用与核心BIOiSIM平台相结合的可靠和准确的AI驱动工具， 加速发现和开发用于缓解StUD的药物。 目前的第一阶段提案将开始通过两个互补的 接近。中枢神经系统药物AI驱动的药代动力学模型的建立 将在目标1中进行以下虚拟化合物文库的筛选。 这将得到一组化合物的概念验证的支持， 药代动力学目标2将重点关注药效学预测模型的开发 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，确定了调节神经可塑性的治疗靶点。 临床前和临床文献。这将得到子集概念验证的支持 使用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驱动方法预计将显着加速 新的兴奋剂使用障碍药物的开发过程， 具有最佳ADME和药效学特性的化合物。这样的做法， 一个快速开发负担得起的和有效的治疗方法的途径， 其他迹象。