Abstract :
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
Keyword :
System xc - antiporter, cystine, Glutamate, SAS software, Predictive modelling