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Bajaj, Shalini
- Quantitative Structure–Activity Relationship and Combinatorial Design of 1,3,4-Oxadiazole-Based Thymidine Phosphorylase Inhibitors as Potential Anti-Cancer Agents
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Authors
Affiliations
1 SLT Institute of Pharmaceutical Sciences, Guru Ghasidas Vishwavidyalaya, Bilaspur 495 009, IN
1 SLT Institute of Pharmaceutical Sciences, Guru Ghasidas Vishwavidyalaya, Bilaspur 495 009, IN
Source
Current Science, Vol 114, No 10 (2018), Pagination: 2063-2071Abstract
The 3D quantitative structure–activity relationship model representing r2 = 0.8605, q2 = 0.8193 and pred_r2 = 0.6847 respectively, was generated for thymidine phosphorylase (TP) inhibitory activity of some 1,3,4-oxadiazole derivatives. Electronegative substituents at R1 and less steric bulk with electropositive substituents at R2 were found to be favourable for TP inhibition. The activity prediction of a combinatorial library of 1629 compounds resulted in 50 molecules whose predicted activity was comparable to the most active compound in the dataset and within the model’s applicability domain. Among them six molecules showed favourable interactions with the active site of TP proposing potential anticancer activity of the title compounds.Keywords
Anti-Cancer Therapy, Docking, Combinatorial Library, 1,3,4-Oxadiazole, 3D-QSAR.Full Text
References
- Pauly, J. L., Schuller, M. G., Zelcer, A. A., Kirss, T. A., Gore, S. S. and Germain, M. J., Identification and comparative analysis of thymidine phosphorylase in the plasma of healthy subjects and cancer patients. J. Natl. Cancer Inst., 1977, 58, 1587–1590.
- Matsushita, S. et al., The effect of thymidine phosphorylase inhibitor on angiogenesis and apoptosis in tumors. Cancer Res., 1999, 59, 1911–1916.
- Focher, F. and Spadari, S., Thymidine phophorylase: a two-face janus in anticancer chemotherapy. Curr. Cancer Drug Targets, 2001, 1, 141–153.
- Sivridis, E., Giatromanolaki, A., Papadopoulos, I., Gatter, K. C., Harris, A. L. and Koukourakis, M. I., Thymidine phosphorylase expression in normal, hyperplastic and neoplastic prostates: correlation with tumour associated macrophages, infiltrating lymphocytes, and angiogenesis. Brit. J. Cancer, 2002, 86, 1465–1467.
- Ferreira, M. M. C., Multivariate QSAR. J. Braz. Chem. Soc., 2002, 13, 742–753.
- Abdullahi, A. D. et al., Novel insight into the structural requirements of P70S6K inhibition using group-based quantitative structure activity relationship (GQSAR). J. Appl. Pharm. Sci., 2014, 4(06), 16–24.
- Jain, S. V., Bhadoriya, K. S., Bari, S. B., Sahu, N. K. and Ghate, M., Discovery of potent anticonvulsant ligands as dual NMDA and AMPA receptors antagonists by molecular modelling studies. Med. Chem. Res., 2012, 21, 3465–3484.
- Shahzad, S. A. et al., Synthesis and biological evaluation of novel oxadiazole derivatives: A new class of thymidine phosphorylase inhibitors as potential anti-tumor agents. Bioorganic Med. Chem., 2014, 22, 1008–1015.
- VLifeMDS 3.5, Molecular Design Suite, VLife Sciences Technology Pvt Ltd, Pune, India, 2009; www.vlifesciences.com
- Trott, O. and Olson, A. J., AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comp. Chem., 2010, 31, 455–461.
- Dallakyan, S. and Olson, A. J., Small-molecule library screening by docking with PyRx. Meth. Mol. Biol., 2015, 1263, 243–250.
- Halgren, T. A., Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94. J. Comp. Chem., 1996, 17, 553–586.
- Ajmani, S., Jhadav, K. and Kulkarni, S. A., Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J. Chem. Inf. Model., 2006, 46, 24–31.
- Ghosh, P. and Bagchi, M. C., Comparative QSAR studies of nitrofuranyl amide derivatives using theoretical structural properties. Mol. Simul., 2009, 35(14), 1185–1200.
- Clark, M., Cramer, R. D. and Van, O. N., Validation of the general purpose Tripos 5.2 force field. J. Comp. Chem., 1989, 10, 982–1012.
- Golbraikh, A. and Tropsha, A., Predictive QSAR modeling based on diversity sampling of experimental datasets for training and test set selection. J. Comp. Aided Mol. Des., 2002, 16, 357–369.
- Sahu, N. K., Sharma, M. C., Mourya, V. and Kohli, D. V., QSAR studies of some side chain modified 7-chloro-4-aminoquinolines as antimalarial agents. Arabian J. Chem., 2014, 7, 701–707.
- Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P., Optimization by simulated annealing. Science, 1983, 220, 671–680.
- Scior, T., Medina-Franco, J., Do, Q. T., Martínez-Mayorga, K., Yunes, R. J. and Bernard, P., How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr. Med. Chem., 2009, 16, 4297–4313.
- Armstrong, N. A., Pharmaceutical Experimental Design and Interpretation, CRC Press, Taylor & Francis, 2006.
- Hoskuldsson, A., PLS regression methods. J. Chemom., 1988, 2, 211–228.
- Martens, H. and Naes, T., Multivariate Calibration, Chichester, Wiley, 1989.
- Palyulin, V. A., Radchenko, E. V. and Zefirov, N. S., Molecular field topology analysis method in QSAR studies of organic compounds. J. Chem. Inf. Comp. Sci., 2000, 40, 659–667.
- Norman, R. A. et al., Crystal structure of human thymidine phosphorylase in complex with a small molecule inhibitor. Structure, 2004, 12, 75–84.
- Seeliger, D. and Gischolar_main, B. L., Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comp. Aided Mol. Des., 2010, 24, 417–422.
- Osterberg, F., Morris, G. M., Sanner, M. F., Olson, A. J. and Goodsell, D. S., Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in autodock. Proteins: Struct. Funct. Genet., 2002, 46, 34–40.
- Lipinski, C. A., Lombardo, F., Dominy, B. W. and Feeney, P. J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 1997, 23, 3–25.
- Golbraikh, A. and Tropsha, A., Beware of q2. J. Mol. Graph Model., 2002, 20, 269–276.
- Zheng, W. and Tropsha, A., Novel variable selection quantitative structure–property relationship approach based on the k-nearest neighbor principle. J. Chem. Inf. Comp. Sci., 2000, 40, 185–194.
- Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T., McDowell, R. M. and Gramatica, P., Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect., 2003, 111, 1361–1375.
- Snedecor, G. W. and Cochran, W. G., Statistical Methods, Oxford and IBH, New Delhi, 1967.
- Roy, K., Supratik, K. and Pravin, A., On a simple approach for determining applicability domain of QSAR models. Chemometr. Intell. Lab. Syst., 2015, 145, 22–29.