Application of GA-MLR for QSAR Modeling of the Arylthioindole Class of Tubulin Polymerization Inhibitors as Anticancer Agents

Background: Microtubules are dynamic filamentous cytoskeletal proteins which have used widely in cancer chemotherapy. Generally, the action of these compounds depends on binding to the tubulin proteins which are alpha, beta-heterodimers that form the core of the microtubules. Arylthioindoles (ATIs) inhibit tubulin polymerization by binding to the colchicine site, inhibiting the binding of colchicine to tubulin. QSAR modeling play an important role in modern medicinal chemistry, presenting a unique potential for transforming the early phases of drug research, in terms of time and money savings. In this study we are intending to achieve some useful information about the structural properties of mentioned ATIs using the QSAR modeling method. In this research, the QSAR modeling has been employed to evaluate the efficacy of ATIs derivatives in the inhibition of tubulin polymerization. The best multivariate linear model for each training and test data subset calculated using both genetic algorithm-multiple linear regressions (GA-MLR) and stepwise-multiple linear regressions (S-MLR) methods. The best RS trial set has been selected by comparing the two different QSAR modeling results in all train and test subsets. Finally, the S-MLR and GA-MLR results were compared for finding the best random selection (RS) subset. Methods: The molecular structures and experimental activity values for inhibition of tubulin polymerization obtained from the literature. Here, using Dragon package over 1185 molecular descriptors such as RDF, GETAWAY, WHIM, 3D-MoRSE descriptors, and functional group descriptors derived for proper characterizing the arylthioindole derivatives structures. These molecular descriptors reduced to 437 one by eliminating the constant variables, collinear descriptors and the variables having low correlation with response. In fact in order to obtain the best RS subsets in the mentioned method, the random samplings of the training subset (80% of data) were carried out 20 times and the remaining molecules (20% of data) considered as external validation set. The QSAR models were constructed using stepwise-MLR and GA-MLR. Results: In order to select important variables the GA-MLR and S-MLR methods applied on the training subsets after data splitting. The best MLR models (models with high fitness function values) with four, five and six variables built to obtain the best QSAR model. The best MLR model had four parameters in both GA-MLR and S-MLR methods. The best significant relationships, using comparison of Q(2) of models, for the IC50 values of tubulin assembly in the models obtained in S-MLR and GA-MLR methods are presented for all of the random sets and SOM set. Conclusion: The result of external validation indicates that in the study of inhibition of tubulin polymerization, the Q(test)(2) values are 0.7242 and 0.3215 for GA-MLR and S-MLR methods respectively. Considering the results which obtained in both methods it can be concluded that in variable selecting the GA-MLR method is more powerful than the S-MLR method.