CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRD

CICADA：AD/ADRD 药物重新定位的临床信息学和计算方法

基本信息

批准号：
10476677
负责人：
Yong Chen
金额：
$ 79.4万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10476677
关键词：
Accounting Address Age Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Area Artificial Intelligence Behavioral Brain Brain Pathology Cessation of life Characteristics Clinical Clinical Informatics Cognitive Computer software Computing Methodologies Data Databases Dementia Detection Diabetes Mellitus Dimensions Drug Design Drug Exposure Drug Modelings Elderly Electronic Health Record Environmental Exposure Event Exposure to FDA approved Functional disorder Genetic Genetic Diseases Goals Health Health Sciences Heterogeneity Hybrids Hyperlipidemia Hypertension Impaired cognition Infusion procedures Intervention Investments Knowledge Link Literature Machine Learning Medicine Meta-Analysis Methods Modeling Natural Language Processing Neurodegenerative Disorders Ontology Outcome Pathway interactions Patients Pharmaceutical Preparations Pharmacy facility Phase Phase II/III Clinical Trial Prevention Procedures Regression Analysis Reproducibility Research Risk Risk Factors Safety Scoring Method Semantics Signal Transduction Statistical Methods Technology Testing Texas Universities base biobank cognitive development detection method drug candidate effective therapy efficacy evaluation follow-up genetic risk factor high dimensionality individual patient knowledge graph mild cognitive impairment multidimensional data multimodal data multimodality multiple datasets novel novel therapeutics physical inactivity resilience response social social health determinants success

项目摘要

Project Summary This proposal seeks support for developing advanced clinical informatics and computational approaches for drug-repositioning for Alzheimer's disease (AD) and related dementias (ADRD). The proposed project directly addresses the areas of emphasis in PAR-20-156 to “develop computational methods such as artificial intelligence/machine learning to investigate new uses of FDA-approved drugs or candidate drugs from failed Phase II/Phase III clinical trials through analysis of multimodal data.” The overarching goals of this proposal are to develop novel clinical informatics and computational approaches for drug repositioning of AD/ADRD. Specifically, we will develop statistical methods and ontology technology to extract drug-repositioning signals from multidimensional data (e.g., pharmacy-linked genetic data and biobank data, historical trials, and EHR data). The proposed framework is novel because it integrates advanced statistical inference procedures with semantic technology for data-driven and reproducible drug repositioning for AD/ADRD. We have three aims: We have three specific aims: Aim 1: Develop signal detection methods using multi-modal data (pharmacy-linked genetic data, genetic and electronic health record (EHR) data, and BioBank data). Aim 2: Evaluate the efficacy and safety of candidate drugs via historical trials and EHR data. Aim 3: Develop novel semantic and natural language processing (NLP) methods for Knowledge Graph (KG) construction. The success of this project will lead to novel computational methods, KG, and software for facilitating drug repositioning for AD/ADRD based on multimodal data. If successful, the proposed method could identify novel drug repositioning signals and generate novel hypotheses for prevention and treatment intervention of treat AD/ADRD. Our project holds the promise of identifying novel drug repositioning signals. This project is novel for integrating evidence synthesis methods with signal detection methods using advanced multimodal modeling, and it is potentially transformative for advancing prevention and treatment for AD/ADRD.

项目摘要该建议寻求支持发展先进的临床信息和计算阿尔茨海默氏病（AD）和相关痴呆症（ADRD）的药物替代方法的方法。拟议的项目直接解决了Par-20-156的重点领域，以“发展计算方法，例如人工智能/机器学习来研究新用途通过失败的II/II期临床试验失败的FDA批准药物或候选药物多模式数据的分析。” 该提案的总体目标是开发新颖的临床信息和 AD/ADRD药物重新定位的计算方法。具体来说，我们将发展统计方法和本体技术从中提取药物替代信号多维数据（例如，与药房相关的遗传数据和生物库数据，历史试验，和EHR数据）。提出的框架是新颖的，因为它整合了高级统计数据使用语义技术进行数据驱动和可重复的药物的推理程序重新定位AD/ADRD。我们有三个目标：我们有三个具体的目标： AIM 1：使用多模式数据（药房连接）开发信号检测方法遗传数据，遗传和电子健康记录（EHR）数据和生物库数据）。目标2：通过历史试验和EHR评估候选药物的效率和安全性数据。目标3：开发新颖的语义和自然语言处理（NLP）方法知识图（kg）结构。该项目的成功将导致新颖的计算方法，kg和软件基于多模式数据促进对AD/ADRD的药物重新定位。如果成功，提出的方法可以识别新的药物重新定位信号并产生新的假设用于预防和治疗干预AD/ADRD。我们的项目承诺识别新型药物重新定位信号。这个项目是整合证据的新颖的使用高级多峰建模，具有信号检测方法的合成方法，它具有推进AD/ADRD的预防和治疗的潜在变革性。