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 非法（例如甲基苯丙胺和可卡因）和处方（例如， Adderall）在阿片类药物流行病的脚后跟飙升，与隔离有关 与19日大流行有关。与阿片类药物使用障碍不同，目前尚无FDA批准 治疗刺激使用障碍（Stud）的药物，仅保奴支持 和行为修改疗法。这些是昂贵的治疗方法，效力不佳，因为 高复发率（60-90％）证明了这一点。兴奋剂使用障碍显然是未满足的需求。 Verisim Life是一家创新的公司，正在开发和利用人工智能（AI）和 用于更快的药物发现和开发的机器学习（ML）技术。我们的专利 Bioisim平台可以预测小分子药代动力学和药效学 通过AI/ML参数化的全身基于生理的建模。它被设计为 准确预测人类试验前研究药物的临床价值。全堆Ai- 启用了生物模拟模型可显着减少动物测试的数量 通过管道来推进治疗疗法，加速药物开发，并明显 增加投资回报率。该SBIR应用的主要目标是开发，验证 并利用与Core Bioisim平台合并的可靠，准确的AI驱动工具 加速旨在缓解螺柱的药物的发现和开发。 当前的I阶段建议将开始通过两个补充来解决这一目标 方法。 AI驱动的药代动力学建模特定于中枢神经系统药物 通过以下筛选虚拟化合物库的分布将在AIM 1中执行。 这将通过概念验证验证的验证证明验证一部分具有体内的化合物 药代动力学。 AIM 2将重点放在药效预测建模的开发上 用于刺激性使用障碍药物发现和开发。重点将放在潜力上 通过对神经元可塑性调节神经元可塑性的治疗靶标，通过深入分析 临床前和临床文献。这将通过子集的概念验证验证来支持 使用可卡因和 甲基苯丙胺。拟议的AI/ML驱动方法预计将显着加速 通过将努力引导到 具有最佳ADME和药效特性的化合物。这样的方法会创造 快速发展负担得起且有效的治疗方法的途径 其他迹象。