Machine Learning Identifies Candidates for Drug Repurposing in Alzheimer's Disease

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. In this study, we profile 80 FDA-approved and clinically tested drugs in neural cell cultures, with the goal of producing a ranked list of possible repurposing candidates. An in-vitro differentiated mixture of neuron and glial cells derived from the ReNcell VM neural progenitor cell line was treated with 80 different compounds and mRNA levels measured using RNA-seq.

阿尔茨海默病（Alzheimer's Disease, AD）新型治疗药物的临床试验已耗费大量时间与资源，却大多以阴性结果告终。将已获美国食品药品监督管理局（Food and Drug Administration, FDA）批准的药物拓展至新适应症的药物重定位策略，是一种更快捷、成本更低的备选方案。本研究对神经细胞培养体系中的80种经FDA批准且已完成临床测试的药物进行了表达谱分析，旨在生成一份潜在重定位候选药物的排序列表。本研究采用源自ReNcell VM神经祖细胞系的体外分化神经元-胶质细胞混合培养体系，使用80种不同化合物对该体系进行处理，并通过RNA测序（RNA-seq）检测其mRNA表达水平。