In Silico Screening of Medications for Slowing Alzheimer's Disease Progression.

减缓阿尔茨海默病进展药物的计算机筛选。

• 批准号: 9884696
• 负责人: RICHARD E KENNEDY
• 金额: $ 64.6万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9884696
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease therapy; Bioinformatics; Clinical Data; Clinical Trials; Cognitive; Combination Medication; Complement; Data; Data Set; Databases; Dementia; Development; Diagnosis; Disease; Disease Progression; Drug Screening; Electronic Health Record; Failure; Future; Gene Expression; Gene Targeting; Genes; Goals; Individual; Investments; Literature; Malignant Neoplasms; Measures; Medical; Medicare; Medicare claim; Methods; Modeling; Modification; Natural experiment; Outcome; Participant; Patients; Pattern; Pattern Recognition; Pharmaceutical Preparations; Phase; Process; Randomized Controlled Trials; Research; Resources; Rewards; Sampling; Series; Source; Testing; Therapeutic; Therapeutic Agents; Therapeutic Effect; Time; United States National Institutes of Health; Validation; base; beneficiary; clinical Diagnosis; clinical care; clinical encounter; cognitive performance; cognitive testing; cohort; cooperative study; cost; data mining; drug development; epidemiology study; follow-up; improved; in silico; in vivo; interest; large scale data; mild cognitive impairment; multidimensional data; neuroimaging; novel; novel strategies; novel therapeutics; screening; symposium; text searching; therapy development

Drug development in Alzheimer's disease (AD) requires a considerable investment of time and re- sources, often with little reward as the vast majority of medications ultimately prove unsuccessful. Drug repur- posing, in which medications that already have been approved for treatment are evaluated for therapeutic effects in other disorders, has the potential to markedly increase the number of agents in the drug development pipeline but requires methods for effective screening of candidate medications for activity. In silico or computational ap- proaches to medication screening are rapidly growing, and have been successful in illnesses such as cancer, but their application to AD remains understudied. There is also intense interest in drug repurposing approaches that will utilize the vast amounts of clinical data that are being collected from epidemiological studies and clinical encounters documented through electronic health records (EHRs). In this proposal, we present a novel approach to drug repurposing that uses large-scale data mining (i.e., pattern recognition) algorithms applied to concurrent medication taken by participants in AD clinical trials and in Medicare administrative data to determine which of these medications show potential therapeutic benefits. With over 30 years of AD clinical trial data available to us through a recently developed meta-database and 10 years of prescription data available through Medicare Part D, the administration of concurrent medications to patients as part of their routine clinical care constitutes a large- scale natural experiment. This information can be harnessed for AD treatment discovery if appropriate methods can be developed to detect effects on disease progression within this high-dimensional data. Data mining al- gorithms that discover patterns of associations in data, rather than testing predetermined hypotheses, are well suited to application in large-scale screening for drug repurposing. Using our meta-database and Medicare data, we will be able to evaluate most of the more than 6,000 currently available prescription medications for efficacy in AD using well-accepted endpoints for measuring disease progression. The discovery phase will be followed by a validation phase of promising candidate medications in independent data sets, as well as identification of plausible gene targets for each medication from the biomedical literature. This study will set the groundwork for a series of follow-up in vivo studies to conclusively demonstrate effects of selected medications for AD, expanding the current armamentarium for treating this common and debilitating disorder.

阿尔茨海默氏病（AD）的药物开发需要考虑时间和重新投资 资料来源通常几乎没有回报，因为绝大多数药物最终被证明没有成功。毒品rec- 摆姿势，其中已经批准用于治疗的药物进行治疗作用。 在其他疾病中，有可能显着增加药物开发管道中的药物数量 但是需要有效筛查候选药物进行活动的方法。在计算机或计算中 对药物筛查的精神迅速增长，并且在癌症等疾病中已经成功， 但是他们在广告中的应用仍然被理解。对药物重新利用的方法也很感兴趣 这将利用从流行病学研究和临床中收集的大量临床数据 通过电子健康记录（EHRS）记录的遇到。在此提案中，我们提出了一种新颖的方法 使用大规模数据挖掘（即模式识别）算法的药物重新利用 参与者在AD临床试验和Medicare行政数据中服用的药物，以确定哪些 这些药物显示出潜在的治疗益处。有30多年的广告临床试验数据可供我们使用 通过最近开发的元数据库和通过Medicare Part提供的10年的处方数据 D，作为其常规临床护理的一部分，对患者的并发药物构成很大的 缩放自然实验。如果适当的方法，可以利用此信息用于AD治疗发现 可以开发以检测此高维数据中对疾病进展的影响。数据挖掘 发现数据中关联的模式而不是检验预定的假设，这是很好的 适用于大规模筛查以重新利用药物。使用我们的元数据库和Medicare数据， 我们将能够评估目前有6,000多种可用处方药的效率 在AD中，使用经过良好接受的终点来测量疾病进展。发现阶段将遵循 通过在独立数据集中验证候选药物的验证阶​​段，并识别 生物医学文献中每种药物的合理基因靶标。这项研究将为 一系列随访在体内研究，以最终证明选定药物对AD的影响，并扩大 当前治疗这种常见和使人衰弱障碍的武术。