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This study presents Quantitative Structure Activity Relationships (QSAR) study on a pool of 18 bio-active sulfonamide compounds which includes five acetazolamide derivatives, eight sulfanilamide derivatives and five clinically used sulfonamides molecules as drugs namely acetazolamide, methazolamide, dichlorophenamide, ethoxolamide and dorzolamide. For all the compounds, initial geometry optimizations were carried out with a molecular mechanics (MM) method using the MM+ force fields. The lowest energy conformations of the compounds obtained by the MM method were further optimized by the Density Functional Theory (DFT) method by employing Becke's three-parameter hybrid functional (B3LYP) and 6-31G (d) basis set. Molecular descriptors, dipole moment, electronegativity, total energy at 0 K, entropy at 298 K, HOMO and LUMO energies obtained from DFT calculations provide valuable information and have a significant role in the assessment of carbonic anhydrase (CA-II) inhibitory activity of the compounds. By using the multiple linear regression technique several QSAR models have been drown up with the help these calculated descriptors and carbonic anhydrase (CA-II) inhibitory data of the molecules. Among the obtained QSAR models presented in the study, statistically the most significant one is a five parameters linear equation with the squared correlation coefficient R 2 values of ca. 0.94 and the squared cross-validated correlation coefficient R 2 CV values of ca. 0.85. The results were discussed in the light of the main factors that influence the inhibitory activity of the carbonic anhydrase (CA-II) isozyme.

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Abstract:In the present study, quantitative structure-activity-relationship (QSAR) study on a group of sulfonamide Schiff-base inhibitors of Carbonic Anhydrase (CA) enzyme has been carried out using Codessa Pro methodology and software. Linear regression QSAR models of the biological activity (Ki) of 38 inhibitors of carbonic anhydrase CA-II isozyme were established with 12 different molecular descriptors which were selected from more than hundreds of geometrical, topological, quantum-mechanical, and electronic types of descriptors and calculated using Codessa Pro software. Among the models presented in this study, statistically the most significant one is a five-parameter equation with correlation coefficient, R 2 values of ca. 0.840, and the cross-validated correlation coefficient, R 2 values of ca. 0.777. The obtained models allowed us to reveal some physicochemical and structural factors, which are strongly correlated with the biological activity of the compounds.

Several quantum-mechanics-based descriptors were derived for a diverse set of 48 organic compounds using AM1, PM3, HF/6-31+G, and DFT-B3LYP/6-31+G (d) level of the theory. LC50 values of acute toxicity of the compounds were correlated to the fathead minnow and predicted using calculated descriptors by employing Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA) program. The heuristic method, implemented in the CODESSA program for selecting the ‘best’ regression model, was applied to a pre-selection of the most-representative descriptors by sequentially eliminating descriptors that did not satisfy a certain level of statistical criterion. First model, statistically, the most significant one has been drawn up with the help of DFT calculations in which the squared correlation coefficient R2 is 0.85, and the squared cross-validation correlation coefficient RCV2 is 0.79. Second model, which has been drawn up with the help of HF calculations, has its statistical quality very close to the DFT-based one and in this model value of R2 is 0.84 and that of RCV2 is 0.78. Third and fourth models have been drawn up with the help of AM1 and PM3 calculations, respectively. The values of R2 and RCV2 in the third case are correspondingly 0.79 and 0.66, whereas in the fourth case they are 0.78 and 0.65 respectively. Results of this study clearly demonstrate that for the calculations of descriptors in modeling of acute toxicity of organic compounds to the fathead minnow, first principal methods are much more useful than semi-empirical methods.