Knowledge discovery and machine learning to elucidate the mechanisms of HIV activity and interaction with substance use disorder

知识发现和机器学习阐明艾滋病毒活动及其与药物滥用障碍相互作用的机制

• 批准号: 10348407
• 负责人: Ilya Safro
• 金额: $ 44.41万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348407
• 关键词: Accidents; Acquired Immunodeficiency Syndrome; Age; Algorithms; Amphetamines; Animals; Artificial Intelligence; Biological; Biomedical Research; Caring; Clinical; Clinical Trials; Cocaine; Cognitive; Cognitive deficits; Custom; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Drug Combinations; Drug Modelings; Drug Side Effects; Electronic Health Record; Gene Expression; Generations; Genes; Goals; HIV; HIV Infections; HIV Seropositivity; HIV-1; Health; Health Status; Highly Active Antiretroviral Therapy; Human; Individual; Infrastructure; Intelligence; Knowledge; Knowledge Discovery; Literature; Longevity; Machine Learning; Manuals; Medical; Memory Loss; Mining; Modeling; Neurocognitive Deficit; Neurologic Effect; Neurons; Opioid; Outcome; Overdose; Pathology; Patients; Pharmaceutical Preparations; Population; PubChem; Quality of life; Research; Research Personnel; Severities; Signal Transduction; Substance Use Disorder; Substance of Abuse; Suicide; System; Testing; Triage; United States Department of Veterans Affairs; Validation; Work; World Health Organization; antiretroviral therapy; base; clinical development; comorbidity; deep learning; drug candidate; drug of abuse; drug repurposing; experience; health record; improved; inquiry-based learning; machine learning algorithm; mortality; multiple data sources; nervous system disorder; network informatics; neurotoxic; neurotoxicity; next generation; novel; peer; prevent; programs; public health relevance; small molecule; text searching; tool

PROJECT SUMMARY More than 36 million people worldwide are estimated to be living with HIV infection and more than 1.2 million are in the USA. With the introduction of highly active anti-retroviral therapy, the life span of HIV-infected individuals has increased significantly. However, the quality of life of can be compromised owing to a range of cognitive deficits and memory loss, commonly referred to as HIV-associated neurological disorders (HAND). HIV-infected individuals are more likely to suffer from substance use disorder (SUD), and disproportionately suffer from high all-cause mortality. Drugs of abuse also increase severity of HAND by several potential biological mechanisms. HIV associated cognitive deficiencies in conjunction with SUD decrease engagement in HIV care, which fuels a worsening downward spiral of health status. Despite intensive research, there is no approved therapy for the treatment of HAND and particularly for the combined neurological effects of HIV and drugs of abuse. We have developed and employed MOLIERE and AGATHA, AI-based literature mining systems that discover novel interactions that potentially contribute to HAND. These systems also prioritize mining results to uncover small molecules that can be tested for anti-HAND therapy. Experimental validation of MOLIERE was achieved; four small molecules predicted by MOLIERE were shown to prevent HIV-Tat and cocaine induced neurotoxicity. AGATHA improved MOLIERE results on a massive retroactive validation and is ready to be deployed for wider searches that now include PubChem. In parallel, our previous efforts querying the Department of Veterans Affairs / Veterans Informatics Network Computing Infrastructure (VINCI) with specific hypotheses have successfully uncovered potential associations of unanticipated modifiers of HIV-associated pathologies. Collectively, these results led us to the central goal of this proposal to develop and apply an integrative AI-based approach to analyze biomedical datasets and Electronic Health Records to determine new mechanisms of HIV and substanses of abuse interactions, and to discover repurposed drug candidates to be tested for the treatment of HIV-infected SUD patients. This will be accomplished in three Aims. Aim 1 will develop a multidimensional AI-based text mining approach to explore new mechanistic connections between HAND and substanses of abuse. This will generate new knowledge of HAND and SUD interactions, and uncover small molecule and drug candidates that can be tested for activity against the neurotoxic insults caused by HIV and substanses of abuse. Aim 2 will develop and apply advanced machine learning and AI algorithms to explore health records of HIV and SUD patients. The outcome will be the development of the machine learning system to analyze VA data and generate of signals (hypotheses) for medications or medication targets that might have value to experimentally test for repurposing to manage HAND. Aim 3 will prioritize the selected candidates for experimental validation and further clinical development.

项目摘要 据估计，全球超过3600万人患有艾滋病毒感染，超过120万人 在美国。随着引入高度活跃的抗逆转录病毒疗法，艾滋病毒感染者的寿命 显着增加了。但是，由于一系列认知能力，其生活质量可能会受到损害 缺陷和记忆丧失，通常称为HIV相关的神经系统疾病（手）。感染HIV 个体更有可能患有吸毒障碍（SUD），并且遭受高度痛苦 全因死亡率。滥用药物还通过几种潜在的生物学机制增加了手的严重程度。 艾滋病毒相关的认知缺陷以及SUD与SUD的参与度降低了艾滋病毒护理的参与度，这助长了A 健康状况的下降趋势恶化。尽管进行了深入的研究，但仍未获得批准的治疗 治疗艾滋病毒和滥用药物的神经系统作用，尤其是治疗。 我们已经开发并采用了基于AI的文献挖掘系统Moliere和Agatha 发现可能有助于手的新型互动。这些系统还将采矿结果优先考虑 发现可以进行抗手术治疗的小分子。 Moliere的实验验证是 实现；显示了莫里埃预测的四个小分子可防止HIV-TAT和可卡因诱导 神经毒性。 Agatha改善了大规模追溯验证的Moliere结果，并准备好 部署用于更广泛的搜索，现在包括PubChem。同时，我们以前在询问部门的努力 具有特定假设的退伍军人事务 /退伍军人信息学网络计算基础架构（VINCI） 已经成功发现了与HIV相关病理的意外修饰剂的潜在关联。 总的来说，这些结果使我们达到了这一提案的核心目标，以开发和应用综合性 基于AI的方法来分析生物医学数据集和电子健康记录以确定新的 艾滋病毒和滥用互动的替代机制，并发现重新利用的毒品候选者 要测试以治疗HIV感染的SUD患者。这将在三个目标中实现。目标1 将开发一种基于AI的多维文本挖掘方法，以探索新的机械连接 手和滥用的替代品。这将产生有关手和SUD相互作用的新知识，并发现 可以测试的小分子和候选药物，以实现针对由HIV引起的神经毒性损伤的活性 和滥用的代替。 AIM 2将开发和应用高级机器学习和AI算法来探索 艾滋病毒和SUD患者的健康记录。结果将是机器学习系统的开发 分析VA数据并生成可能具有的药物或药物靶标的信号（假设） 实验测试的价值，以进行重新利用以管理手。 AIM 3将优先考虑选定的候选人 实验验证和进一步的临床发展。