Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA; Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Cell Rep Med. 2023 Oct 17;4(10):101227. doi: 10.1016/j.xcrm.2023.101227.
Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.
药物重定位旨在利用现有的临床知识，为已批准的药物确定新的临床应用。然而，重定位工作未能在后期临床试验中证明成功率有所提高。本研究聚焦于已在 139 种重定位假说中评估的 11 种已批准的激酶抑制剂，我们使用数据挖掘来描述临床再利用的状态。然后，我们使用在体外药效学测定中用人血清蛋白进行的简单实验校正，开发了一种衡量药物有效暴露的方法。我们发现，对于白血病中的 23 个药物变异靶点的五个激酶抑制剂的组合，该指标对临床活性具有显著的预测性。然后，我们在 NSCLC 和 GIST 两种实体瘤的六种其他激酶抑制剂中验证了我们模型的性能。我们的方法提供了一种简单的策略，可利用现有临床信息和实验系统，降低药物重定位研究中的临床失败率。
