Topological, hydrophobicity, and other descriptors on α-glucosidase inhibition: a QSAR study on xanthone derivatives

References

• Silva CH, Taft CA. Computer-aided molecular design of novel glucosidase inhibitors for AIDS treatment. J Biomol Struct Dyn 2004;22:59–63.
   PubMed Web of Science ®Google Scholar
• Fischer PB, Collin M, Karlsson GB, James W, Butters TD, Davis SJ et al. The alpha-glucosidase inhibitor N-butyldeoxynojirimycin inhibits human immunodeficiency virus entry at the level of post-CD4 binding. J Virol 1995;69:5791–5797.
   PubMed Web of Science ®Google Scholar
• Taylor DL, Kang MS, Brennan TM, Bridges CG, Sunkara PS, Tyms AS. Inhibition of alpha-glucosidase I of the glycoprotein-processing enzymes by 6-O-butanoyl castanospermine (MDL 28,574) and its consequences in human immunodeficiency virus-infected T cells. Antimicrob Agents Chemother 1994;38:1780–1787.
   PubMed Web of Science ®Google Scholar
• Jung-Hum P, Sungmin K, Hwangseo P. Toward the virtual screening of α-glucosidase inhibitors with the homology-modelled protein structure. Bull Korean Chem Soc 2008;29:921–927.
   Google Scholar
• Wang S, Yan J, Wang X, Yang Z, Lin F, Zhang T. Synthesis and evaluation of the alpha-glucosidase inhibitory activity of 3-[4-(phenylsulfonamido)benzoyl]-2H-1-benzopyran-2-one derivatives. Eur J Med Chem 2010;45:1250–1255.
   Google Scholar
• Hwangseo P, Kyo YH, Kyung HO, Young HK, Jae YL, Keun K. Discovery of novel α-glucosidase inhibitors based on the virtual screening with the homology-modelled protein structure. Bioorg Med Chem 2008;16:284–292.
   Google Scholar
• Humphries MJ, Matsumoto K, White SL, Olden K. Inhibition of experimental metastasis by castanospermine in mice: blockage of two distinct stages of tumor colonization by oligosaccharide processing inhibitors. Cancer Res 1986;46:5215–5222.
   PubMed Web of Science ®Google Scholar
• Rudd PM, Storr, SJ, Royle L, Chapman CJ, Hamid UM, Robertson JF et al. The O-linked glycosylation of secretory/shed MUC1 from an advanced breast cancer patient’s serum. Glycobiol 2008;18:456–462.
   Google Scholar
• Karpas A, Fleet GW, Dwek RA, Petursson S, Namgoong SK, Ramsden NG et al. Aminosugar derivatives as potential anti-human immunodeficiency virus agents. Proc Natl Acad Sci USA 1988;85:9229–9233.
   PubMed Web of Science ®Google Scholar
• Zitzmann N, Mehta AS, Carrouée S, Butters TD, Platt FM, McCauley J et al. Imino sugars inhibit the formation and secretion of bovine viral diarrhea virus, a pestivirus model of hepatitis C virus: implications for the development of broad spectrum anti-hepatitis virus agents. Proc Natl Acad Sci USA 1999;96:11878–11882.
   PubMed Web of Science ®Google Scholar
• Kavitha B, Nagakumar B, Ki HP, Keun WL. Binding mode analyses and pharmacophore model development for sulphonamide chalcone derivatives, a new class of α-glucosidase inhibitors. J Mol Graph Model 2008;26:1202–1212.
   PubMedGoogle Scholar
• Rawling AJ, Lomas H, Adam WP, Marvin JRL, Dominic SA, Shane JSR et al. Synthesis and biological characterization of novel N-alkyl deoxynojirimycin α-glucosidase inhibitors. ChemBioChem 2009;10:1101–1105.
   PubMedGoogle Scholar
• Joseph R, Florent B, Catia T, Froncois M. 2D and 3D QSAR studies of diarylpyrimidine HIV-1 reverse transcriptase inhibitors. J Comput Aided Mol Des 2008;22:831–841.
   Google Scholar
• Karthikeyan C, Moorthy NSHN, Piyush T. QSAR study of substituted 2-pyridinyl quanidine as selective urokinase-type plasminogen activator (uPA) inhibitors. J Enz Inhibit Med Chem 2009;24:6–13.
   PubMed Web of Science ®Google Scholar
• Cho DH, Lee SK, Kim BT, No KT. Quantitative structure activity relationship (QSAR) study of new fluorovinyloxyacetamides. Bull Korean Chem Soc 2001;22:388–394.
   Web of Science ®Google Scholar
• Yan L, Zhuofeng K, Jianfang C, Wen-Hua C, Lin M, Bo W. Synthesis, inhibitory activities and QSAR study of xanthone derivatives as α-glucosidase inhibitors. Bioorg Med Chem 2008;16:7185–7192.
   Google Scholar
• Eun JS, Maecus JCL, Byong WL, Hoi YK, Young BR, Tae SJ, Woo SL, Ki HP. Xanthones from Cudrania tricuspidata displaying potent α-glucosidase inhibition. Bioorg Med Chem Lett 2007;17:6421–6424.
   Google Scholar
• Park H, Hwang KY, Kim YH, Oh KH, Lee JY, Kim K. Discovery and biological evaluation of novel alpha-glucosidase inhibitors with in vivo antidiabetic effect. Bioorg Med Chem Lett 2008;18:3711–3715.
   PubMed Web of Science ®Google Scholar
• Raymond AD, Terry DB, Frances MP, Nicole Z. Targeting glycosylation as a therapeutic approach. Nat Rev Drug Dis 2002;1:65–75.
   Google Scholar
• Moorthy NSHN, Ramos MJ, Fernandes PA. Prediction of relationship between structural features of andrographolide derivatives and α-glucosidase inhibitory activity: a QSAR study. J Enz Inhibit Med Chem 2011;8:14–25..
   Google Scholar
• Moorthy NSHN, Ramos MJ, Fernandes PA. QSAR analysis of isosteviol derivatives as α-glucosidase inhibitors with element count and other descriptors. Lett Drug Des Dis (In Press).
   Google Scholar
• Bonchev D. Novel indices for the topological complexity of molecules. SAR/QSAR Environ Res 1997;7:23–43.
   Google Scholar
• Gálvez J, García-Domenech R, Julian-Ortiz J V de, Soler R. Topological approach to drug design. J Chem Inf Comput Sci 1995;35:272–284
   PubMedGoogle Scholar
• Schultz HP, Schultz EB, Schultz TP. Topological organic chemistry. 9. Graph theory and molecular topological indices of stereoisomeric organic compounds. J Chem Inf Comput Sci 1995;35:864–870.
   Google Scholar
• Biye R. Atomic level based AI topological descriptors for structure property correlations. J Chem Inf Comput Sci 2003;43:161–169.
   Google Scholar
• Jarmo H. QSAR modeling with the electrotopological state: TIBO derivatives. J Chem Inf Comput Sci 2001;41:425–429.
   Google Scholar
• Yan L, Lan Z, Lin M, Wen HC, Bo W, Zun LX. Synthesis and pharmacological activities of xanthone derivatives as α-glucosidase inhibitors. Bioorg Med Chem 2006;14:5683–5690.
   Google Scholar
• Chem BioOffice 2008, CambridgeSoft Corp, Cambridge, UK.
   Google Scholar
• V-Life Technologies, molecular design suite ™ 3.5, 2008, Pune, India.
   Google Scholar
• Statistica 8.0 statistical software, StatSoft, Inc. 2006, OK, USA
   Google Scholar
• Balaji S, Karthikeyan C, Moorthy NSHN, Trivedi P. QSAR modelling of HIV-1 reverse transcriptase inhibition by benzoxazinones using a combination of P_VSA and pharmacophore feature descriptors. Bioorg Med Chem Lett 2004;14:6089–6094.
   PubMed Web of Science ®Google Scholar
• Gramatica P. Principle of QSAR model validation: internal and external. QSAR Comb Sci 2007;26:694–701.
   Google Scholar
• Tropsha A, Gramatica P, Gombar VK. The importance of earnest, validation is the absolute essential application and interpretation of QSPR models. QSAR Comb Sci 2003;22:69–77.
   Google Scholar
• Erikkson L, Jaworsha J, Worth AP, Mark TDC, Robert MM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluation of classification and regression based QSARs. Envirn Health Perspect 2003;111:1361–1375.
   PubMedGoogle Scholar
• Alexander G, Alexander T. Beware of Q². J Mol Graphs Model 2002;20:269–276.
   PubMed Web of Science ®Google Scholar
• Tuppurainen K, Viisas M, Laatikainen R, Peräkylä M. Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set. J Chem Inf Comput Sci 2002;42:607–613.
   PubMedGoogle Scholar
• Durbin J, Watson GS. Testing for serial correlation in least squares regression. II. Biometrika 1951;38:159–178.
   PubMed Web of Science ®Google Scholar
• Prasanna S, Manivannan E, Chaturvedi SC. QSAR analysis of some fused pyrazoles as selective cyclooxygenase-2 inhibitors: a Hansch approach. Arch Pharm (Weinheim) 2004;337:440–444.
   PubMed Web of Science ®Google Scholar
• Gnanadesikan R, Kettenring JR. Robust estimate, residuals and outlier detection with multipurpose data. Biometrics 1972;28:81–124.
   Web of Science ®Google Scholar
• Santiago V, Humberto GD, Lourdes S, Eugenio U. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008;29:2613–2622.
   Google Scholar
• Cook RD. Influential observation in linear regression. J Am Stat Assoc 1979;74:169–174.
   Web of Science ®Google Scholar
• Rotriquez G. Chapter 2, Linear models for continuous data, 1993–2000:49–58. http://data.princeton.edu/wws509/notes
   Google Scholar
• Dogra Shailay, K. Script for computing linear regression diagnostics from QSAR world-free online resources for QSAR modelling. http://www.qsarworld.com/virtualworkshop.php
   Google Scholar
• Gough JD, Hall LH. Modeling antileukemic activity of carboquinones with electrotopological state and chi indices. J Chem Inf Comput Sci 1999;39:356–361.
   PubMedGoogle Scholar
• Andersson PM, Michael SM, Svante W, Torbjo Rn, L. Comparison between physicochemical and calculated molecular descriptors. J Chemometrics 2000;14:629–642.
   Google Scholar
• Hliang HM, Hall LA. E-state modeling of corticosteroids binding affinity validation of model for small data set. J Chem Inf Comput Sci 2001;41:1248–1254.
   Google Scholar
• Hall LM, Hall LH, Kier LB. Modeling drug albumin binding affinity with e-state topological structure representation. J Chem Inf Comput Sci 2003;43:2120–2128.
   PubMedGoogle Scholar
• Maw HH, Hall LH. E-state modeling of HIV-1 protease inhibitor binding independent of 3D information. J Chem Inf Comput Sci 2002;42:290–298.
   PubMedGoogle Scholar