Chen Hsiao-Rong, Sherr David H, Hu Zhenjun, DeLisi Charles
Bioinformatics Program, College of Engineering, Boston University, Boston, MA, USA.
Graduate Program in Translational Molecular Medicine, Boston University School of Medicine, Boston, MA, USA.
BMC Med Genomics. 2016 Jul 30;9(1):51. doi: 10.1186/s12920-016-0212-7.
The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs-to find new uses for which they weren't intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. We report on the development, testing and application of a promising new approach to repositioning.
Our approach is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes.
The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and 82/106; (ii) the ROC/AUC performance substantially exceeds that of comparable methods; (iii) preliminary in vitro studies indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. We briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate.
Our method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of other CMap-based methods, and in vitro experiments indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. The approach has the potential to provide a more efficient drug discovery pipeline.
将药物推向市场所需的高成本和长时间促使人们努力对FDA批准的药物进行重新定位——寻找它们原本未被设计的新用途，从而降低商业化的总体成本，并缩短药物发现与上市之间的时间间隔。我们报告了一种有前景的重新定位新方法的开发、测试和应用。
我们的方法基于挖掘人类功能联系网络，以寻找药物和疾病基因靶点的负相关模块。该方法考虑了多种信息来源，包括基因突变、基因表达以及模块内基因的功能连通性和邻近性。
该方法用于识别治疗乳腺癌和前列腺癌的候选药物。我们发现：（i）FDA批准用于乳腺癌（前列腺癌）的药物召回率为20/20（10/11），而临床试验中的药物召回率分别为131/154和82/106；（ii）ROC/AUC性能显著超过可比方法；（iii）初步体外研究表明，5/5的候选药物在MCF7和SUM149癌细胞系中的治疗指数优于阿霉素。我们在它们所介导的生物学过程的背景下，简要讨论了候选药物在分子水平上的生物学合理性。
我们的方法似乎有望识别出能够纠正异常细胞功能的多靶点候选药物。特别是计算性能超过了其他基于CMap的方法，体外实验表明，5/5的候选药物在MCF7和SUM149癌细胞系中的治疗指数优于阿霉素。该方法有可能提供更高效的药物发现流程。
