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

项目摘要 该提案寻求支持开发先进的临床信息学和计算 阿尔茨海默病（AD）和相关痴呆（ADRD）的药物重新定位方法。 拟议项目直接涉及PAR-20 - 156中的重点领域， 人工智能/机器学习等计算方法来研究新用途 FDA批准的药物或失败的II期/III期临床试验的候选药物， 多模式数据分析"。 该提案的总体目标是开发新的临床信息学， AD/ADRD的药物重新定位的计算方法。具体来说，我们将开发 统计学方法和本体论技术来提取药物重新定位信号， 多维数据（例如，与药物相关的基因数据和生物库数据，历史试验， EHR数据）。所提出的框架是新颖的，因为它集成了先进的统计 数据驱动可重复药物语义推理程序 AD/ADRD的重新定位。我们有三个目标： 我们有三个具体目标： 目标1：开发使用多模态数据的信号检测方法（与药物相关 遗传数据、遗传和电子健康记录（EHR）数据以及生物银行数据）。 目的2：通过历史试验和EHR评估候选药物的有效性和安全性 数据 目标3：开发新的语义和自然语言处理（NLP）方法， 知识图谱（KG）构建。 该项目的成功将导致新的计算方法，KG和软件， 促进基于多模态数据的AD/ADRD的药物重新定位。如果成功， 所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AD/ADRD的防治干预。我们的项目承诺 识别新的药物重新定位信号。这个项目是新颖的整合证据 使用先进的多模态建模的信号检测方法的合成方法， 并且它对于推进AD/ADRD的预防和治疗具有潜在的变革性。

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.