Liu Chaoqun, Zhao Shuai, Qiao Liansheng, Ren Yue, Liu Kaiyang, Bi Shijie, Li Beiyan, Yuan Anlei, Zheng Lulu, Wang Zewen, Xu Zhenzhen, Zhang Yanling
Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Key Laboratory of TCM-information Engineer of State Administration of TCM, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China.
Int Immunopharmacol. 2025 Mar 6;149:114182. doi: 10.1016/j.intimp.2025.114182. Epub 2025 Feb 3.
Identifying effective druggable targets with disease-specific for diseases is a tremendous challenge in new drug development. However, current studies of druggable targets identification are most based on either druggability or disease-specific, lacking a combination of two factors. To further improve the accuracy of druggable targets discovery, a druggable target discovery strategy for diseases (DTDS) was proposed, which combined druggable targets prediction by machine learning and key targets identification by tissue-level and cellular-level transcriptomics analysis. Rheumatoid arthritis (RA), an autoimmune disease that cannot be treated entirely, was taken as a case. First, the protein-protein interaction network was constructed as the disease background network, and the classification models were established based on the topological parameters of known RA-druggable targets with druggability and non-RA targets without therapeutic effects on RA. 168 potential druggable targets were predicted by the classification models from 264 RA-related targets. Subsequently, 40 RA-specific targets were identified by tissue-level and cellular-level transcriptomics analysis from 168 potential druggable targets. Most of them were RA-druggable targets except PSMB9 and PTPRC. Finally, PSMB9 and PTPRC were further verified by in vitro experiments. The results showed that the inhibitor of PSMB9 or PTPRC could effectively inhibit inflammation and abnormal proliferation of synovial cells, proving that PSMB9 and PTPRC were potential RA-druggable targets, and further indicating that DTDS had high accuracy. In conclusion, the DTDS strategy established in this study is reliable and has been proven in identification of potential RA-druggable targets, which is expected to provide ideas and methods for systematic discovery of potential druggable targets for diseases.
识别针对特定疾病的有效可成药靶点是新药研发中的一项巨大挑战。然而，目前关于可成药靶点识别的研究大多仅基于可成药性或疾病特异性，缺乏对这两个因素的综合考量。为进一步提高可成药靶点发现的准确性，提出了一种针对疾病的可成药靶点发现策略（DTDS），该策略将机器学习预测可成药靶点与组织水平和细胞水平转录组学分析识别关键靶点相结合。以类风湿性关节炎（RA）这种无法完全治愈的自身免疫性疾病为例。首先，构建蛋白质-蛋白质相互作用网络作为疾病背景网络，并基于已知对RA有可成药性的RA可成药靶点和对RA无治疗作用的非RA靶点的拓扑参数建立分类模型。通过分类模型从264个与RA相关的靶点中预测出168个潜在的可成药靶点。随后，通过组织水平和细胞水平转录组学分析从168个潜在可成药靶点中识别出40个RA特异性靶点。除PSMB9和PTPRC外，它们大多是RA可成药靶点。最后，通过体外实验对PSMB9和PTPRC进行了进一步验证。结果表明，PSMB9或PTPRC的抑制剂可有效抑制滑膜细胞的炎症和异常增殖，证明PSMB9和PTPRC是潜在的RA可成药靶点，进一步表明DTDS具有较高的准确性。总之，本研究建立的DTDS策略可靠，已在识别潜在的RA可成药靶点中得到验证，有望为系统性发现疾病潜在可成药靶点提供思路和方法。
