Sengupta Avik, Singh Saurabh Kumar, Kumar Rahul
Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana 502284, India.
Department of Chemistry, Indian Institute of Technology Hyderabad, Kandi, Telangana 502284, India.
ACS Omega. 2024 Apr 9;9(16):18584-18592. doi: 10.1021/acsomega.4c01195. eCollection 2024 Apr 23.
Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination () after 10-fold cross-validation. For 12 CRC cell lines, ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.
结直肠癌（CRC）的发病率呈令人担忧的上升趋势，且由于其预后较差，构成了重大的治疗挑战。迫切需要确定新的药物疗法来对抗CRC。在本研究中，我们通过利用癌症药物敏感性基因组学（GDSC）数据库中抗癌药物的药理学特征来满足这一需求，并使用支持向量机（SVM）算法开发了定量构效关系（QSAR）模型，以预测用于CRC治疗的替代和多靶点抗癌化合物。我们的QSAR模型在10倍交叉验证后实现了高决定系数（）相关性，证明了其稳健性。对于12种CRC细胞系，范围为0.609至0.827。SW1417和GP5d细胞系表现最佳，值分别为0.827和0.786。此外，我们列出了CRC细胞系药物谱中最常见的化学描述符，并进一步报告了这些描述符与药物活性的相关性。KRFP314指纹是主要出现的描述符，KRFPC314指纹在CRC细胞系药物谱中的流行程度紧随其后。除了预测建模，我们还通过进行描述符-药物分析和概括药物-癌基因关系，通过方法确认了我们开发的QSAR模型的适用性。我们还使用QSAR模型确定了两种潜在的抗CRC FDA批准药物，紫霉素和二醋吗啡。为确保我们研究结果的易于获取和实用性，我们已将这些模型整合到一个名为“ColoRecPred”的用户友好型预测网络服务器中，可在https://project.iith.ac.in/cgntlab/colorecpred上获取。我们预计这个网络服务器可用于筛选化学文库以识别潜在的抗CRC药物。
