Laboratory of Information Biology, Information Science and Technology, Hokkaido University, Sapporo, Japan.
OMICS. 2022 Jun;26(6):339-347. doi: 10.1089/omi.2022.0026. Epub 2022 Jun 3.
Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by -means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.
药物重利用在行星健康学中具有广泛的重要性，可用于传染病以及常见或罕见的慢性人类疾病的治疗创新。药物重利用对于开发针对 COVID-19 大流行的干预措施也很重要。我们提出了一种涉及两阶段预测和机器学习的药物重利用新方法。首先，在相似的基因表达模式暗示不同疾病状态下存在共同关键途径的前提下，通过基因表达对疾病进行聚类。接下来，通过评估异常基因表达的可逆转性来评估药物疗效，并对结果进行聚类以确定重利用的靶点。为了聚类相似的疾病，我们分析了来自 262 例 31 种疾病和 268 例对照的基因表达数据，使用 Uniform Manifold Approximation and Projection for Dimension Reduction （UMAP）进行降维，然后使用 -means 优化聚类数量。为了评估，我们检查了肌炎、多发性肌炎和皮肌炎（DM）的疾病特异性基因表达数据，并使用 LINCS L1000 特征方向签名搜索引擎（L1000CDS）获得了可逆转这些特定改变基因表达模式的小分子化合物列表，作为药物重利用的候选物。最后，通过基因集富集分析（Gene Set Enrichment Analysis）分析受影响基因的功能，以检查与预期药物疗效的一致性。结果，我们发现了疾病特异性基因表达，并且重要的是，确定了 20 种药物，如 BMS-387032、佛波醇-12-肉豆蔻酸-13-乙酸酯、米托蒽醌、alvocidib 和 vorinostat 等，作为重利用的候选物。这些药物以前被认为对三种疾病中的两种有效，并且对另一种疾病也有很高的有效可能性。即包涵体肌炎和 DM。本文提出的药物重利用两阶段预测方法为未来各种人类疾病的药物发现和临床试验提供了创新思路。
