PREDICTIVE MODELS FOR SYSTEM XC - ANTIPORTER INHIBITION BASED ON STRUCTURALLY DIVERSE MOLECULES

The morbidity and mortality throughout the world is increasing day by day due to cancer. Several molecular targets have been identified and being targeted for treatment of cancer cells. System xc - , an amino acid antiporter, is one such potential target. With the uptake of one molecule of cystine and release of one molecule glutamate, over expressed system xc -manipulates the redox status within cancer cells and protects them. Simultaneously, released glutamate helps in growth and metastasis of cancer cells. Few researches have synthesized and screened structurally diverse molecules against system xc - antiporter. Amongst these, few molecules like erastin analogues, amino acid analogues, iso-oxazole analogues, hydantoin analogues and sulfasalazine analogues exhibited potent inhibitory activity. It is possible to identify desirable molecular properties required for system xc - inhibition using information from above mentioned molecules. In context, we developed different predictive models using above mentioned analogues using SAS software using regression and decision tree analysis mainly. The score ranking overlay plots showed moderate to good fit of data for the training and validation data sets. These predictive models may further be used for the design and development of potent system xc - inhibitors. Key words: System xc - antiporter, cystine, Glutamate, SAS software, Predictive modelling