Chen Lei, Lu Jing, Huang Tao, Cai Yu-Dong
College of Life Science, Shanghai University, Shanghai, People's Republic of China.
College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
PLoS One. 2017 Aug 18;12(8):e0183411. doi: 10.1371/journal.pone.0183411. eCollection 2017.
Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.
肺癌每年导致大量死亡。到目前为止，尚未找到或研发出针对这种疾病的治愈方法。通过传统实验方法找到一种有效的药物总是要花费数百万美元，并且需要数年时间。必须开发计算方法来整合多种现有信息，以识别用于进一步研究的候选药物，这可以降低研发成本和时间。在本研究中，我们试图通过提出一种计算方法来推进这项工作，以识别非小细胞肺癌（NSCLC，肺癌的主要类型）的候选药物。该方法使用了三个步骤：（1）初步筛选，（2）通过关联测试和置换测试筛选化合物，（3）使用EM聚类算法筛选化合物。在第一步中，基于STITCH（一个报告化学物质与蛋白质之间相互作用的著名数据库）中报告的化学-化学相互作用信息以及已批准的NSCLC药物，挑选出能够与至少一种已批准的NSCLC药物相互作用的化合物。在第二步中，关联测试选择能够与至少一种NSCLC相关化学物质和至少一种NSCLC相关基因相互作用的化合物，随后，使用置换测试从剩余化合物中剔除非特异性化合物。在最后一步中，使用强大的聚类算法EM算法选择核心化合物。通过该方法鉴定出六种推定化合物，即原卟啉IX、血卟啉、卡奈替尼、拉帕替尼、培利替尼和达可替尼。先前发表的数据表明，所有选定的化合物均已被报道具有抗NSCLC活性，这表明这些化合物作为NSCLC新型候选药物的可能性很高。
