Aravind Badiger

Spleen Tyrosine Kinase (SYK) is known to play vital role in many signal transduction pathways and hence is considered as a potent target for various disorders like inflammatory, cancer and many auto immune disorders. QSAR study of Napthyridines as SYK Kinase inhibitors was performed using accelrrys discovery studio client (DSV - Version 3.0) as the modelling tool. A total of 53 selected molecules were considered for the development of QSAR model. The study was performed using the most stable confirmer fitting best to SYK Kinase enzyme binding site. The study resulted in development of cross validated QSAR models using different set of descriptors.  Partial least square model of the data generated exhibited a very good linear relation between the training set of compounds with that of the reported activity as well as the test set of compounds with the predicted activity. The 4 statistical analysis performed revealed following observations; Training data set r2= 0.848, q2 (Cross validated r2) = 0.581 validated by internal validation with correlation of coefficient (r2) of 0.941 and cross validated r2 (q2) of 0.617 and external set of compounds with a predictive correlation of coefficient of 0.918.  A total of 11 descriptors pruned on the study explained the structural correlation of Napthyridines with SYK Kinase enzyme. The mode developed can be used to predict bio-efficacy of unknown molecules 7-methoxy-6-[3-morpholinopropoxy]-quinazoline as SYK Kinase inhibitors. The study calls for the development of the molecules predicted as bio efficacious in this model and a quantitative inhibitory analysis of SYK Kinase. Key words: Napthyridines, QSAR, SYK.

Epidermal Growth Factor Receptor (EGFR) is known to play vital role in many cellular signalling pathways and hence is considered as a potent target for cancer. Inhibition of this enzyme has been reported to be beneficial by various workers. QSAR study of Quinazolines as EGFR was performed using accelrys discovery studio client (DSV-Version 3.0) as the modelling tool. A total of 67 selected molecules were considered for the development of QSAR model. Partial least square model of the data generated exhibited a very good linear relation between the training set of compounds with that of the reported activity as well as the test set of compounds with the predicted activity. The 4 statistical analysis performed revealed following observations; Training data set r2= 0.701, q2 (Cross validated r2) = 0.616 validated by internal validation with correlation of coefficient (r2) of 0.848 and cross validated r2 (q2) of 0.573 and external set of compounds with a predictive correlation of coefficient of 0.900.  A total of 9 descripters pruned on the study explained the structural correlation of quinazolines with EGFR. The model developed can be used to predict bioefficacy of unknown molecules 4-[1,3-benzothiazol-2-yl]-N-[(1E)-(4-nitrophenyl)methylene]aniline as EGFR inhibitors. Further a hypothetical model to describe the interaction between the predicted molecules with EGFR is proposed and this hypothetical model explains the possibility of Met769 and Gln767 as the possible binding sites. The activity is observed in the preliminary cytotoxic activity (MTT assay). The study calls for the development of the molecules predicted as bio efficacious in this model and a quantitative inhibitory analysis of EGFR. Key word: EGFR, QSAR, r2, q2