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Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification.

Prachayasittikul V, Pingaew R, Anuwongcharoen N, Worachartcheewan A, Nantasenamat C, Prachayasittikul S, Ruchirawat S, Prachayasittikul V - Springerplus (2015)

Bottom Line: Four QSAR models were successfully constructed with acceptable predictive performance affording R CV ranging from 0.5958 to 0.8957 and RMSECV 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand ; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand.

ABSTRACT
Considerable 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 R CV ranging from 0.5958 to 0.8957 and RMSECV 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.

No MeSH data available.


Related in: MedlinePlus

Plots of experimental versus predicted pIC50 values of cytotoxic activities against four cell lines (a HuCCA-1, b HepG2, c A549, d MOLT-3) generated by QSAR models (training set: compounds are represented by closed circle and regression line is shown as a solid line, leave-one-out validated testing set: compounds are represented by opened hex and regression line is shown as a dotted line)
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Fig8: Plots of experimental versus predicted pIC50 values of cytotoxic activities against four cell lines (a HuCCA-1, b HepG2, c A549, d MOLT-3) generated by QSAR models (training set: compounds are represented by closed circle and regression line is shown as a solid line, leave-one-out validated testing set: compounds are represented by opened hex and regression line is shown as a dotted line)

Mentions: The multiple linear regression (MLR) is one of the most popularly used machine learning algorithms for understanding SAR and it has been successfully employed for predicting bioactivities of diverse classes of compounds (Prachayasittikul et al. 2014; Worachartcheewan et al. 2012, 2013, 2014b, c). Regarding cytotoxic activity against four cancer cell lines, the data were separated into four data sets for QSAR analysis. Four QSAR models were successfully constructed by MLR method using a set of selected informative descriptor values and experimental cytotoxic activities (pIC50). The QSAR models and their predictive performance parameters are summarized in Table 3. Acceptable predictive performances were obtained from all constructed QSAR models with Rcv and RMSEcv values ranging from 0.5958 to 0.8957 and 0.2070–0.4526, respectively. The highest performance was achieved from the HuCCA-1 model showing Rcv = 0.8957 and RMSEcv = 0.2562 whereas the lowest performance was observed for A549 model (Rcv = 0.5958, RMSEcv = 0.4211). The experimental and predicted cytotoxic activities against four cancer cell lines (pIC50) are shown in Table 4 and Fig. 8.Table 3


Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification.

Prachayasittikul V, Pingaew R, Anuwongcharoen N, Worachartcheewan A, Nantasenamat C, Prachayasittikul S, Ruchirawat S, Prachayasittikul V - Springerplus (2015)

Plots of experimental versus predicted pIC50 values of cytotoxic activities against four cell lines (a HuCCA-1, b HepG2, c A549, d MOLT-3) generated by QSAR models (training set: compounds are represented by closed circle and regression line is shown as a solid line, leave-one-out validated testing set: compounds are represented by opened hex and regression line is shown as a dotted line)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4628044&req=5

Fig8: Plots of experimental versus predicted pIC50 values of cytotoxic activities against four cell lines (a HuCCA-1, b HepG2, c A549, d MOLT-3) generated by QSAR models (training set: compounds are represented by closed circle and regression line is shown as a solid line, leave-one-out validated testing set: compounds are represented by opened hex and regression line is shown as a dotted line)
Mentions: The multiple linear regression (MLR) is one of the most popularly used machine learning algorithms for understanding SAR and it has been successfully employed for predicting bioactivities of diverse classes of compounds (Prachayasittikul et al. 2014; Worachartcheewan et al. 2012, 2013, 2014b, c). Regarding cytotoxic activity against four cancer cell lines, the data were separated into four data sets for QSAR analysis. Four QSAR models were successfully constructed by MLR method using a set of selected informative descriptor values and experimental cytotoxic activities (pIC50). The QSAR models and their predictive performance parameters are summarized in Table 3. Acceptable predictive performances were obtained from all constructed QSAR models with Rcv and RMSEcv values ranging from 0.5958 to 0.8957 and 0.2070–0.4526, respectively. The highest performance was achieved from the HuCCA-1 model showing Rcv = 0.8957 and RMSEcv = 0.2562 whereas the lowest performance was observed for A549 model (Rcv = 0.5958, RMSEcv = 0.4211). The experimental and predicted cytotoxic activities against four cancer cell lines (pIC50) are shown in Table 4 and Fig. 8.Table 3

Bottom Line: Four QSAR models were successfully constructed with acceptable predictive performance affording R CV ranging from 0.5958 to 0.8957 and RMSECV 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand ; Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand.

ABSTRACT
Considerable 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 R CV ranging from 0.5958 to 0.8957 and RMSECV 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.

No MeSH data available.


Related in: MedlinePlus