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New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters
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1  Biomedical Sciences Department, Health Science Division, University of Quintana Roo


According to Global Health in 2013, it was estimated that there were 508 000 women deaths in the world in the year 2011 caused by breast cancer. Even though cancer can be treated with different treatments for example: immunotherapy, radiotherapy and chemotherapy surgical operation, this disease continues being a severe medical problem. For that reason it has to be found another methods for cancer treatment. The discovery of new drugs with better activity and less toxicity for the treatment of Breast Cancer is a goal of the major importance. In this sense, theoretical models as QSAR can be useful to discover new anti-breast cancer drugs. For this reason, we developed a new multi-parameter-QSAR (mp-QSAR) model to discover new drugs. However, almost all the computational models known focus in only one target or receptor. In this work, Breast cancer type 1 susceptibility protein, ATP-binding cassette sub-family G member 2, Human breast cancer cell lines, Peroxisome proliferator-activated receptor gamma/Nuclear receptor coactivator 3, Nuclear receptor coactivator 3 and STE20-related kinase adapter protein alpha were used as receptor inputs in the model. A linear technique like Linear Discriminant Analysis (LDA) is our statistical analysis, and we compared with others models to seek alternative multi-target models for inhibitors of some of these receptors. In so doing, we used as input Topological Indices, in specific Wiener, Barabasi and Harary indices calculated by Dragon software. These operators quantify the deviations of the structure of one drug from the expected values for all drugs assayed in different boundary conditions or parameters (type of receptor, type of assay, type of target, target mapping). The best model correctly classifies as active compounds 84.00 % and non-active compounds (99.06 %) in the training series. Overall training performance was 95.91%. Validation of the model was carried out by means of external predicting series. Overall predictability performance was 95.52%. The present work reports the attempts to calculate within unified framework probabilities of new anti-Breast cancer drugs.

Keywords: Breast cancer; Multi-target receptors; Barabasi Topological Indices, Wierner Topological Indices, Harary Topological Indices, Breast cancer receptors.