Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
J Alzheimers Dis. 2024;101(s1):S299-S315. doi: 10.3233/JAD-240680.
Drug repurposing is a methodology used to identify new clinical indications for existing drugs developed for other indications and has been successfully applied in the treatment of numerous conditions. Alzheimer's disease (AD) may be particularly well-suited to the application of drug repurposing methods given the absence of effective therapies and abundance of multi-omic data that has been generated in AD patients recently that may facilitate discovery of candidate AD drugs. A recent focus of drug repurposing has been in the application of pharmacoepidemiologic approaches to drug evaluation. Here, real-world clinical datasets with large numbers of patients are leveraged to establish observational efficacy of candidate drugs for further evaluation in disease models and clinical trials. In this review, we provide a selected overview of methods for drug repurposing, including signature matching, network analysis, molecular docking, phenotypic screening, semantic network, and pharmacoepidemiological analyses. Numerous methods have also been applied specifically to AD with the aim of nominating novel drug candidates for evaluation. These approaches, however, are prone to numerous limitations and potential biases that we have sought to address in the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, a multi-step framework for selection and validation of potential drug candidates that has demonstrated the promise of STAT3 inhibitors and re-evaluated evidence for other drug candidates, such as phosphodiesterase inhibitors. Taken together, drug repurposing holds significant promise for development of novel AD therapeutics, particularly as the pace of data generation and development of analytical methods continue to accelerate.
药物重定位是一种用于为其他适应症开发的现有药物确定新的临床适应症的方法，已成功应用于许多疾病的治疗。鉴于缺乏有效的治疗方法和最近在 AD 患者中生成的大量多组学数据，阿尔茨海默病（AD）可能特别适合应用药物重定位方法，这些数据可能有助于发现候选 AD 药物。最近药物重定位的一个重点是应用药物评价的药物流行病学方法。在这里，利用具有大量患者的真实世界临床数据集来确定候选药物的观察疗效，以便在疾病模型和临床试验中进一步评估。在这篇综述中，我们提供了药物重定位方法的精选概述，包括特征匹配、网络分析、分子对接、表型筛选、语义网络和药物流行病学分析。许多方法也特别应用于 AD，旨在提名新的候选药物进行评估。然而，这些方法容易受到许多限制和潜在偏见的影响，我们在有效的阿尔茨海默病药物药物重定位研究（DREAM）中试图解决这些问题，这是一个多步骤的潜在药物候选物选择和验证框架，已经证明了 STAT3 抑制剂的前景，并重新评估了其他候选物，如磷酸二酯酶抑制剂的证据。总之，药物重定位为开发新的 AD 治疗方法提供了巨大的希望，特别是随着数据生成和分析方法的发展速度继续加快。
