In silico receptor-based drug design of X,Y-benzenesulfonamide derivatives as selective COX-2 inhibitors

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• 作者：David J. Pé ; rez^a ; Orlando Sarabia^a ; Manuel Villanueva-Garc&iacute ; a^b ; Kayim Pineda-Urbina^a ; Á ; ngel Ramos-Organillo^a ; Jorge Gonzalez-Gonzalez^a ; Zeferino Gó ; mez-Sandoval^a ; ^{zgomez@ucol.com} ; Rodrigo Said Razo-Herná ; ndez^a ; ^c ; ^{rrazo@ucol.mx}
• 关键词：QSAR ; Docking ; Cyclooxygenase ; Benzenesulfonamides ; Drug design ; Artificial neural networks
• 刊名：Comptes Rendus Chimie
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：20
• 期：2
• 页码：169-180
• 全文大小：1927 K
• 卷排序：20

文摘

COX-2 is a widely studied biological target, since its activity is directly related to the inflammation response. The design of COX-2 selective inhibitors is an ongoing topic in drug design. We performed a quantitative structure–activity relationship and docking studies over a series of benzenesulfonamide derivatives on their inhibition towards COX-1 and COX-2, in order to rationalize their selectivity towards COX-2. Constitutional, topological and molecular property descriptors for the QSAR models and molecular docking calculations were employed. The mathematical model highlighted that lipophilic character and size are the most important features for COX-2 inhibition by benzenesulfonamides. A second QSAR model revealed that the dipole moment, the number of hydrogen bond donors and lipophilicity descriptors of benzenesulfonamides are crucial for their binding to COX-1. Moreover, artificial neural networks were employed to improve the prediction power of the COX-1 inhibition QSAR model. In this sense, we proposed new selective potential inhibitors by introducing different halogens into the benzenesulfonamide scaffold, improving their interactions with key residues of COX-2.