Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical Knowledge

通过对真实世界数据和生物医学知识的综合分析，计算药物再利用用于 AD/ADRD

• 批准号: 10576853
• 负责人: Jiang Bian
• 金额: $ 77.52万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576853
• 关键词: Acceleration; Affect; Aging; Algorithmic Software; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; American; Bioinformatics; Biological; Biology; Cause of Death; Characteristics; Clinical Medicine; Clinical Research; Cohort Studies; Collection; Communities; Complex; Computer software; Data; Data Science; Data Sources; Development; Dose; Electronic Health Record; Evaluation; Failure; Formulation; Functional disorder; Future; Generations; Goals; Investments; Knowledge; Learning; Machine Learning; Malignant Neoplasms; Medical Genetics; Medicine; Methodology; Methods; Molecular; Multiomic Data; Natural Language Processing; Neurodegenerative Disorders; Outcome; Pathogenicity; Patients; Pharmaceutical Preparations; Prevention strategy; Program Development; Proliferating; Prospective cohort; Reporting; Research; Safety; Sampling; Severities; Signal Transduction; Software Tools; Source; Standardization; Techniques; United States; Update; Validation; billing data; cohort; computable phenotypes; computerized tools; cost; cost effective; deep learning; disease heterogeneity; diverse data; drug candidate; drug development; drug quality; drug repurposing; heterogenous data; high risk; improved; in silico; knowledge base; knowledge graph; learning strategy; machine learning framework; manufacture; novel; novel therapeutic intervention; open source; patient oriented; phenotyping algorithm; pragmatic trial; prospective; study population; success; tool; translational impact

ABSTRACT Alzheimer’s disease (AD) and AD-related dementia (AD/ADRD) is the 6th leading cause of death in the United States (US) – is an aging-related neurodegenerative disease with complex pathogenic mechanism affecting an estimated 6.2 million Americans in 2021. Both the pathogenic mechanism and pathophysiology of AD/ADRD are complex, creating difficulties in finding effective new treatment or prevention strategies, despite significant investments in the last decade. On the other hand, the proliferation of large clinical research networks (CRNs) with real-world data (RWD), such as electronic health records (EHRs), claims, and billing data among others, offer unique opportunities to generate real-world evidence (RWE) that will have direct translational impacts on AD/ADRD. In the past, RWD such as EHRs have limited use for AD/ADRD drug repurposing and primarily used only for validating and evaluating the hypotheses generated by molecular level predictions of AD/ADRD repurposing agents, partially due to a number of key methodological gaps: (1) the lack of integration with existing rich biological and pathophysiological knowledge of AD/ADRD for hypothesis generation, (2) the lack of validated computable phenotyping (CP) and natural language processing (NLP) algorithms and tools that can accurately define the study populations, extract key relevant patient characteristics and meaningful outcomes (e.g., MMSE scores to determine severity), (3) the lack of consideration on the heterogeneity of the disease (i.e., AD/ADRD subtypes), and (4) the lack of recognition of the inherent biases in RWD and the need of applying causal inference principles. The goal of this project is to develop a comprehensive machine learning based causal inference framework for generating high-throughput and high-quality drug repurposing hypotheses for AD/ADRD by integrating heterogeneous information sources. There are three aims in this project. Aim 1 aims at developing computable phenotypes to extract key patient characteristics and outcomes relevant to AD/ADRD drug repurposing studies from RWD. Aim 2 aims at developing a learning-based causal inference framework for generating drug repurposing hypotheses from RWD, a deep knowledge embedding framework for generating drug repurposing hypotheses from biomedical knowledge bases (BKB); and a mutual information enhancement framework that combines the information from both RWD and BKB to further improve the quality of the generated hypotheses. Aim 3 aims at validating the generated hypotheses with diverse data sources and approaches. The project will leverage the patient data from two large clinical research networks (CRNs) contributing to the national Patient-Centered Clinical Research Network (PCORnet) – covering ~15 million Floridians and ~11 million New Yorkers. The developed algorithms and software will be open sourced and widely disseminated within the CRNs and the AD/ADRD research communities.

抽象的 阿尔茨海默氏病（AD）和与AD相关的痴呆症（AD/ADRD）是统一死亡的第六大原因 国家（美国） - 一种与老龄化的神经退行性疾病，具有复杂的致病机制，影响了 估计2021年有620万美国人。 很复杂，在寻找有效的新待遇或预防策略方面遇到困难，目的地很重要 过去十年的投资。另一方面，大型临床研究网络（CRN）的扩散 使用现实世界数据（RWD），例如电子健康记录（EHR），索赔和计费数据等 提供独特的机会来产生真实的证据（RWE），这些证据将直接翻译对 广告/adrd。过去，诸如EHR之类的RWD用于AD/ADRD药物重新利用和主要使用的使用有限 仅用于验证和评估AD/ADRD分子水平预测产生的假设 重新利用代理，部分是由于许多关键方法论差距：（1）与 关于假设产生的AD/ADRD的现有丰富的生物学和病理生理学知识，（2）缺乏 经过验证的可计算表型（CP）和自然语言处理（NLP）算法和工具可以 准确定义研究人群，提取关键的相关患者特征和有意义的结果 （例如，MMSE得分以确定严重程度），（3）缺乏对疾病异质性的考虑（即 AD/ADRD子类型）和（4）缺乏对RWD中继承偏见的认识和应用的需求 因果推论原则。该项目的目的是开发基于机器学习的全面 因果推理框架，用于产生高通量和高质量的药物重新利用假设 通过整合异质信息源来广告/ADRD。这个项目有三个目标。目标1目标 开发可计算的表型以提取与AD/ADRD相关的关键患者特征和结果 RWD的药物重新利用研究。 AIM 2旨在开发基于学习的因果推理框架 用于从RWD产生药物重新利用的假设，这是一个深入的知识嵌入框架 来自生物医学知识库（BKB）的药物重新利用假设；和共同的信息增强 结合了RWD和BKB信息以进一步提高生成的质量的框架 假设。 AIM 3的目的是通过不同的数据源和方法来验证生成的假设。 该项目将利用来自两个大型临床研究网络（CRN）的患者数据，从而有助于 国家以患者为中心的临床研究网络（PCORNET） - 覆盖约1500万佛罗里达人和〜11 百万纽约人。开发的算法和软件将被开源并广泛传播 在CRN和AD/ADRD研究社区中。