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Publication Open Access Classification of P-glycoprotein-interacting compounds using machine-learning methods(2015-07) Watshara Shoombuatong; Apilak Worachartcheewan; Veda Prachayasittikul; Chanin Nantasenamat; Virapong Prachayasittikul; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical InformaticsP-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 non-inhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance.Publication Open Access P-Glycoprotein transporter in drug development(2016-02-12) Veda Prachayasittikul; Virapong Prachayasittikul; Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied TechnologyPublication Open Access Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification(2015-10-05) Veda Prachayasittikul; Ratchanok Pingaew; Nuttapat Anuwongcharoen; Apilak Worachartcheewan; Chanin Nantasenamat; Supaluk Prachayasittikul; Somsak Ruchirawat; Virapong Prachayasittikul; Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied Technology; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical InformaticsConsiderable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quantitative structure–activity relationships (QSAR) is well-known as a powerful computational tool to facilitate the discovery of potential compounds. In this study, a series of thirty-two 1,2,3-triazole derivatives (1–32) together with their experimentally measured cytotoxic activities against four cancer cell lines i.e., HuCCA-1, HepG2, A549 and MOLT-3 were used for QSAR analysis. Four QSAR models were successfully constructed with acceptable predictive performance affording RCV ranging from 0.5958 to 0.8957 and RMSE CV ranging from 0.2070 to 0.4526. An additional set of 64 structurally modified triazole compounds (1A–1R, 2A–2R, 7A–7R and 8A–8R) were constructed in silico and their predicted cytotoxic activities were obtained using the constructed QSAR models. The study suggested crucial moieties and certain properties essential for potent anticancer activity and highlighted a series of promising compounds (21, 28, 32, 1P, 8G, 8N and 8Q) for further development as novel triazole-based anticancer agents.