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.

项目总结 据估计，全球有超过3600万人感染艾滋病毒，超过120万人感染艾滋病毒 在美国。随着高效抗逆转录病毒疗法的引入，艾滋病毒感染者的寿命 显著增加了。然而，由于一系列的认知因素，生活质量可能会受到影响 缺陷和记忆力丧失，通常被称为艾滋病毒相关的神经疾病(手)。艾滋病病毒感染者 个人更有可能遭受物质使用障碍(SUD)的痛苦，并不成比例地遭受高 全因死亡。滥用药物还通过几种潜在的生物机制增加手部的严重程度。 与艾滋病毒相关的认知缺陷与SUD一起降低了对艾滋病毒护理的参与度，从而助长了 健康状况不断恶化的螺旋式下降。尽管进行了大量的研究，但目前还没有批准的治疗方法 手部治疗，特别是艾滋病毒和滥用药物对神经系统的综合影响。 我们已经开发并使用了基于人工智能的文献挖掘系统Moliere和Agatha 发现可能对手有贡献的新的互动。这些系统还对挖掘结果进行优先级排序，以 发现可用于抗手疗法测试的小分子。莫里哀的实验验证 实现了；莫里哀预测的四个小分子被证明可以防止艾滋病毒-TAT和可卡因诱导 神经毒性。阿加莎改进了莫里哀的大规模追溯验证结果，并准备好 部署用于更广泛的搜索，现在包括PubChem。同时，我们以前的努力质疑该部 退伍军人事务部/退伍军人信息学网络计算基础设施(Vinci)与特定假设 已经成功地发现了艾滋病毒相关病理的意外修饰因素的潜在关联。 总而言之，这些结果将我们引向这项提案的中心目标，即开发和应用 基于人工智能的生物医学数据集和电子健康记录分析方法 艾滋病毒和滥用物质相互作用的机制，并发现重新调整用途的候选药物 用于治疗感染艾滋病毒的sud患者的检测。这将通过三个目标来实现。目标1 将开发一种基于人工智能的多维文本挖掘方法，以探索 手和虐待的代名词。这将产生关于手和SUD交互的新知识，并发现 小分子和候选药物，可以测试其对艾滋病毒引起的神经毒性侮辱的活性 以及虐待的实质内容。目标2将开发和应用先进的机器学习和人工智能算法来探索 HIV和SUD患者的健康记录。其结果将是机器学习系统的发展 分析VA数据并生成药物或药物靶标的信号(假设) 对重新调整用途以管理手的实验测试价值。目标3将对选定的候选人进行优先排序 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