AI-assisted behavioral profiling of an invertebrate model to identify new psychedelic therapeutics for stimulant use disorders

人工智能辅助无脊椎动物模型的行为分析，以确定兴奋剂使用障碍的新迷幻疗法

• 批准号: 10467072
• 负责人: Dhaval Subhas Patel
• 金额: $ 31.84万
• 依托单位: NEMALIFE 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/10467072
• 关键词: Accounting; Addictive Behavior; Adverse effects; Agar; Algorithms; Animal Behavior; Animals; Automatic Data Processing; Behavior; Behavior Disorders; Behavioral; Behavioral Assay; Biological Assay; Brain; COVID-19 pandemic; Caenorhabditis elegans; Cessation of life; Clinical Trials; Cocaine; Cocaine Abuse; Cocaine Dependence; Cocaine use disorder; Complex; Cues; Data; Development; Environment; Exhibits; FDA approved; Future; Hallucinogens; Ibogaine; Image; Incidence; Individual; Intervention; Invertebrates; Knowledge; Libraries; Life; Mammals; Microfluidic Microchips; Microfluidics; Modeling; Molecular; Nematoda; Neurotransmitters; Opioid; Output; Overdose; Pathway interactions; Persons; Pharmaceutical Preparations; Phase; Phenotype; Prevention; Process; Property; Risk; Rodent; Rodent Model; Self Administration; Source; Substance Use Disorder; Sum; System; Therapeutic; Training; United States; Validation; Video Recording; Work; addiction; analog; analysis pipeline; base; chemical synthesis; clinical risk; cocaine exposure; cocaine self-administration; cocaine use; conditioned place preference; data analysis pipeline; data curation; data pipeline; design; drug candidate; drug discovery; economic cost; high throughput screening; in vivo; innovation; insight; large datasets; lead candidate; machine learning algorithm; neurobehavioral disorder; novel; novel therapeutics; opioid mortality; opioid use; opioid use disorder; overdose death; preclinical study; screening; side effect; societal costs; stimulant use disorder; tool; transcriptome sequencing; transcriptomics; validation studies

Project Abstract Substance use disorders are neurobehavioral disorders in which afflicted individuals continue to use a substance despite its adverse effects on their lives. Opioids and cocaine use were responsible for 70% and 22%, respectively, of drug overdose deaths in the US alone in 2019. While there are FDA-approved treatments for opioid use disorders, no such therapeutics are available for cocaine addiction. Opioids have a well-characterized mechanism of action in the brain. However, cocaine targets multiple neurotransmitter pathways, and the molecular basis of addiction has not been elucidated. A significant challenge in discovering new therapeutics for cocaine use disorders (CUD) is the need to perform behavioral assays on animals to determine if a candidate drug reduces or abolishes addictive behavior. The need for behavioral assays in rodents is not compatible with high-throughput drug discovery screens. NemaLife, Inc. has developed a microfluidic platform that enables high-throughput in vivo screening using the nematode Caenorhabditis elegans. Video data recorded by our platform is automatically processed by our AI analysis pipeline NemaStudio.ai for metrics such as live/dead and animal activity. This Phase 1 project will implement additional machine learning algorithms into NemaStudio.ai to classify worm behavior. This new functionality will enable us to perform high-throughput behavioral profiling of animals. This form of behavioral profiling using worm models of CUD is expected to rapidly identify new treatments for cocaine addiction. Aim 1 - Develop high-throughput AI-assisted behavioral profiling assays for cocaine use disorders. In this aim, we will take existing worm CUD paradigms and optimize them for our microfluidic platform. Specifically, we will create microfluidic chip-based versions of cue-conditioned place preference and self-administration CUD paradigms. Once these assays are developed, we will collect large datasets of animal behavior either on or off cocaine. This data will then be used to train NemaStudio.ai to classify and quantify addictive behaviors in animals exposed to cocaine and form our behavioral profiling pipeline. Aim 2 - Identify potential CUD therapeutics from a targeted library of psychedelic compounds. With our screening pipeline in place, we will examine whether psychedelic compounds, which are known to have anti- addiction properties, might represent a new source of treatments for CUD. We will screen a variety of psychedelic compounds, select the top candidates, and perform RNA-seq on psychedelic-treated and untreated CUD animals. This data will help identify possible mechanisms of action by which these candidates reduce/abolish the effects of cocaine addiction in the worm. Our data-driven insights will enable targeted rodent studies for pre- clinical validation of the hit compounds. In sum, this work leverages AI tools to build a high throughput in vivo drug discovery platform to accelerate the development of new therapeutics for CUD.

项目摘要 物质使用障碍是一种神经行为障碍，其中受影响的个体继续使用某种物质 尽管这会对他们的生活产生不利影响。阿片类药物和可卡因的使用分别占70%和22%， 分别是2019年仅在美国的药物过量死亡人数。虽然有FDA批准的治疗方法， 阿片类药物使用障碍，没有这样的治疗可用于可卡因成瘾。阿片类药物具有良好的特征， 大脑中的作用机制。然而，可卡因靶向多种神经递质通路， 成瘾的分子基础尚未阐明。发现新疗法的重大挑战 可卡因使用障碍（CUD）是需要对动物进行行为测定，以确定候选人是否 药物减少或消除成瘾行为。啮齿类动物行为测定的需要与 高通量药物发现筛选 NemaLife公司已经开发了一种微流体平台，该平台能够使用 线虫线虫我们平台记录的视频数据由我们的AI自动处理 分析管道NemaStudio.ai，用于诸如活/死和动物活动等指标。第一阶段项目将 在NemaStudio.ai中实施其他机器学习算法，以分类蠕虫行为。这个新 功能将使我们能够对动物进行高通量的行为分析。这种形式的行为 使用CUD的蠕虫模型的分析预计将快速鉴定可卡因成瘾的新治疗方法。 目标1 -开发用于可卡因使用障碍的高通量AI辅助行为分析测定。在 为了实现这一目标，我们将采用现有的蠕虫CUD范例，并为我们的微流体平台优化它们。具体地说， 我们将创建基于微流控芯片的线索条件位置偏好和自我管理CUD版本， 范例一旦这些检测方法被开发出来，我们将收集动物行为的大型数据集， 可卡因然后，这些数据将用于训练NemaStudio.ai对动物的成瘾行为进行分类和量化 暴露在可卡因中形成了我们的行为侧写流程 目标2 -从迷幻化合物的靶向库中鉴定潜在的CUD治疗剂。与 我们的筛选管道到位，我们将检查是否迷幻化合物，这是已知的具有抗- 成瘾特性，可能代表了CUD治疗的新来源。我们将放映各种迷幻的 化合物，选择最佳候选物，并对迷幻剂处理和未处理的CUD进行RNA-seq 动物这些数据将有助于确定这些候选人减少/消除的可能作用机制 可卡因成瘾对蠕虫的影响我们的数据驱动的见解将使有针对性的啮齿动物研究， 临床验证的命中化合物。总之，这项工作利用人工智能工具在体内建立高通量 药物发现平台，以加速CUD新疗法的开发。