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

摘要 阿尔茨海默病(AD)和AD相关痴呆(AD/ADRD)是美国第六大死因 美国-是一种与衰老相关的神经退行性疾病，其致病机制复杂，影响 据估计，2021年有620万美国人。AD/ADRD的发病机制和病理生理 是复杂的，在寻找有效的新的治疗或预防策略方面造成了困难，尽管 过去十年的投资。另一方面，大型临床研究网络(CRN)的激增 使用真实世界数据(RWD)，例如电子健康记录(EHR)、索赔和计费数据等， 提供独特的机会来生成将对以下方面产生直接转换影响的真实证据(RWE) AD/ADRD。在过去，像EHR这样的RWD在AD/ADRD药物再利用方面的使用有限，主要用于 仅用于验证和评估AD/ADRD的分子水平预测产生的假说 重新调整制剂用途，部分原因是一些关键的方法差距：(1)缺乏与 现有丰富的AD/ADRD生物学和病理生理学知识用于假说生成，(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，用于确定严重性的MMSE分数)，(3)缺乏对疾病的异质性的考虑(即， AD/ADRD亚型)，以及(4)缺乏对RWD固有偏见的认识和应用的必要性 因果推论原则。这个项目的目标是开发一个全面的基于机器学习的 用于生成高通量和高质量的药物再用途假设的因果推理框架 AD/ADRD通过集成不同的信息源。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 来自RWD的药物再利用研究。目标2旨在开发一个基于学习的因果推理框架 为了从RWD生成药物再用途假设，一个用于生成药物再用途假设的深度知识嵌入框架 生物医学知识库(BKB)中的药物再利用假说；以及互信息增强 该框架结合了RWD和BKB的信息，以进一步提高生成的质量 假设。目标3旨在用不同的数据来源和方法来验证所生成的假设。 该项目将利用来自两个大型临床研究网络(CRN)的患者数据，为 国家以患者为中心的临床研究网络(PCORnet)-覆盖约1,500万佛罗里达州人和~11 百万纽约人。开发的算法和软件将是开源的，并将广泛传播 在CRN和AD/ADRD研究界内。

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.