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: 10671033
• 负责人: Ilya Safro
• 金额: $ 41.79万
• 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/10671033
• 关键词: Accidents; Acquired Immunodeficiency Syndrome; Age; Algorithms; Amphetamines; Animals; Artificial Intelligence; Biological; Biomedical Research; Caring; Clinical; Clinical Trials; Cocaine; Cognitive; Cognitive deficits; Data; Data Analyses; Data Set; Development; Dimensions; Disease; Drug Combinations; Drug Modelings; Drug Side Effects; Electronic Health Record; Explosion; 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; Persons; 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; Veterans; Work; World Health Organization; antiretroviral therapy; artificial intelligence algorithm; candidate selection; clinical development; comorbidity; deep learning model; drug candidate; drug of abuse; drug repurposing; experience; gene interaction; health record; improved; inquiry-based learning; machine learning algorithm; mortality; multiple data sources; network informatics; neuroAIDS; 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，全世界有3600多万人感染艾滋病毒， 在美国。随着高效抗逆转录病毒疗法的采用，艾滋病毒感染者的寿命 已显著增加。然而，由于一系列认知障碍， 缺陷和记忆丧失，通常称为HIV相关神经系统疾病（HAND）。hiv感染 个人更有可能患有物质使用障碍（SUD），并且不成比例地患有高 全因死亡率药物滥用也通过几种潜在的生物学机制增加HAND的严重程度。 与艾滋病毒相关的认知缺陷与SUD一起减少了对艾滋病毒护理的参与，这助长了艾滋病毒感染。 健康状况不断恶化。尽管进行了深入的研究，但目前还没有批准的治疗方法。 HAND的治疗，特别是用于HIV和药物滥用的联合神经效应。 我们已经开发并采用了MOLIERE和AGATHA，基于AI的文献挖掘系统， 发现可能有助于HAND的新的相互作用。这些系统还优先考虑挖掘结果， 发现可以用于抗HAND治疗的小分子。MOLIERE的实验验证是 实现; MOLIERE预测的四种小分子被证明可以预防HIV-Tat和可卡因诱导的 神经毒性AGATHA通过大规模追溯验证改进了MOLIERE结果，并准备 部署了更广泛的搜索，现在包括PubChem。与此同时，我们先前询问该部的努力 退伍军人事务部/退伍军人信息学网络计算基础设施（芬奇）的具体假设 已经成功地发现了HIV相关病理的非预期修饰剂的潜在关联。 总的来说，这些结果使我们达到了这项建议的中心目标，即开发和应用一种综合的 基于人工智能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 艾滋病毒和滥用物质相互作用的机制，并发现重新利用的候选药物 用于治疗HIV感染的SUD患者。这将在三个目标中实现。要求1 将开发一种基于多维人工智能的文本挖掘方法，以探索 滥用的手和物质。这将产生关于HAND和SUD相互作用的新知识， 小分子和候选药物，可以测试其对HIV引起的神经毒性损伤的活性 和滥用的物质。Aim 2将开发和应用先进的机器学习和人工智能算法， 艾滋病毒和SUD患者的健康记录。其成果将是机器学习系统的开发 分析VA数据并生成可能具有以下特征的药物或药物靶点的信号（假设）： 值进行实验测试，以重新利用来管理HAND。目标3将优先考虑选定的候选人， 实验验证和进一步的临床开发。

项目成果

Association between Haloperidol use and Risk of Rheumatoid Arthritis.

氟哌啶醇使用与类风湿关节炎风险之间的关联。

• DOI: 10.1101/2023.09.11.23295367
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Ambati,VidyaL;Cummings,TammyH;Yerramothu,Praveen;Nguyen,Joseph;Sutton,SScott;Werner,BrianC;Magagnoli,Joseph
• 通讯作者: Magagnoli,Joseph

Odds of Achieving Target Serum Uric Acid Levels Among Gout Patients: The Role of Rurality in Outcomes and Treatment Adherence.

• DOI: 10.1177/21501319231167379
• 发表时间: 2023-01
• 期刊: JOURNAL OF PRIMARY CARE AND COMMUNITY HEALTH
• 影响因子: 3.6
• 作者: Sutton, S. Scott;Magagnoli, Joseph;Cummings, Tammy H.;Hardin, James W.
• 通讯作者: Hardin, James W.

Leukotriene receptor antagonism with montelukast as a possible therapeutic for venous thromboembolism prophylaxis: An observational study.

孟鲁司特的白三烯受体拮抗作用作为静脉血栓栓塞预防的可能治疗方法：一项观察性研究。

• DOI: 10.1016/j.prostaglandins.2022.106649
• 发表时间: 2022
• 期刊: Prostaglandins & other lipid mediators
• 影响因子: 2.9
• 作者: Sutton,SScott;Magagnoli,Joseph;Cummings,TammyH;Hardin,JamesW
• 通讯作者: Hardin,JamesW