Chen Feng-Chi, Liao Yu-Chieh, Huang Jie-Mao, Lin Chieh-Hua, Chen Yih-Yuan, Dou Horng-Yunn, Hsiung Chao Agnes
Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan; Department of Life Sciences, National Chiao-Tung University, Hsinchu, Taiwan; Department of Dentistry, China Medical University, Taichung, Taiwan.
Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan.
PLoS One. 2014 Jun 27;9(6):e100829. doi: 10.1371/journal.pone.0100829. eCollection 2014.
Drug-resistant Mycobacterium tuberculosis (MTB), the causative pathogen of tuberculosis (TB), has become a serious threat to global public health. Yet the development of novel drugs against MTB has been lagging. One potentially powerful approach to drug development is computation-aided repositioning of current drugs. However, the effectiveness of this approach has rarely been examined. Here we select the "TB drugome" approach--a protein structure-based method for drug repositioning for tuberculosis treatment--to (1) experimentally validate the efficacy of the identified drug candidates for inhibiting MTB growth, and (2) computationally examine how consistently drug candidates are prioritized, considering changes in input data. Twenty three drugs in the TB drugome were tested. Of them, only two drugs (tamoxifen and 4-hydroxytamoxifen) effectively suppressed MTB growth at relatively high concentrations. Both drugs significantly enhanced the inhibitory effects of three first-line anti-TB drugs (rifampin, isoniazid, and ethambutol). However, tamoxifen is not a top-listed drug in the TB drugome, and 4-hydroxytamoxifen is not approved for use in humans. Computational re-examination of the TB drugome indicated that the rankings were subject to technical and data-related biases. Thus, although our results support the effectiveness of the TB drugome approach for identifying drugs that can potentially be repositioned for stand-alone applications or for combination treatments for TB, the approach requires further refinements via incorporation of additional biological information. Our findings can also be extended to other structure-based drug repositioning methods.
耐药结核分枝杆菌（MTB）是结核病（TB）的致病病原体，已对全球公共卫生构成严重威胁。然而，针对MTB的新型药物开发一直滞后。一种潜在的强大药物开发方法是对现有药物进行计算机辅助重新定位。然而，这种方法的有效性很少得到检验。在这里，我们选择“结核病药物组”方法——一种基于蛋白质结构的结核病治疗药物重新定位方法——来（1）通过实验验证所鉴定的候选药物抑制MTB生长的疗效，以及（2）通过计算研究考虑输入数据变化时候选药物的优先排序一致性。对结核病药物组中的23种药物进行了测试。其中，只有两种药物（他莫昔芬和4-羟基他莫昔芬）在相对较高浓度下有效抑制MTB生长。这两种药物均显著增强了三种一线抗结核药物（利福平、异烟肼和乙胺丁醇）的抑制作用。然而，他莫昔芬在结核病药物组中并非排名靠前的药物，且4-羟基他莫昔芬未获批准用于人类。对结核病药物组的计算重新检验表明，排名受技术和数据相关偏差的影响。因此，尽管我们的结果支持“结核病药物组”方法在识别可潜在重新定位用于单独应用或结核病联合治疗的药物方面的有效性，但该方法需要通过纳入额外的生物学信息进行进一步完善。我们的研究结果也可扩展到其他基于结构的药物重新定位方法。
