Background: Cancer is becoming an increasingly important risk factor in the global
burden of diseases. Cancer chemotherapy has been one of the major medical advances in the last
few decades. However, the drugs used for this therapy have a narrow therapeutic index, and often
the responses produced are only just palliative as well as unpredictable. In contrast, targeted therapy
that has been introduced in recent years is directed against cancer-specific molecules and signaling
pathways and thus has more limited nonspecific toxicities. This paper reviews about 2D, 3D QSAR
and G-QSAR on a set of thiazolyl-pyrazole derivatives to identify novel and potent EGFR-TK inhibitors
and elucidate structural properties required for anti-EGFR-TK activity. The 2D QSAR studies
were carried out by multiple regression method and the r2 and q2 values were found to be 0.81 and
0.72 respectively. The 3D QSAR was performed using method k-nearest neighbour–molecular field
analysis (kNN-MFA) with simulated annealing variable selection approach; a leave-one-out crossvalidated
correlation coefficient q2 = 0.87 and non-cross-validated correlation coefficient r2 = 0.93.
G-QSAR was performed by generation of multiple models of the same training and test sets as used
in 2D & 3D QSAR by multiple linear regressions. G-QSAR was carried out using template based
fragmentation scheme and forward variable selection method. Docking analysis was performed
further and is suggestive of binding affinity with standard compounds.
Methods: Molecular modeling, structure based drug design and docking analysis studies were performed.
Initially molecular modeling studies (2D, 3D and G-QSAR) were performed on a set of
thiazolyl-pyrazole derivatives. A lead nucleus was predicted and with the help of CombiLib; VLife
MDS and Lipinski's screen 12 novel chemical entities (NCE’s) were designed. They were then
subjected to docking and were further filtered. Five compounds having potent EGFR activity having
drug like pharmacokinetic profiles were predicted.
Results: The lead nucleus required for anti-EGFR activity could be predicted. The NCE's could then
be designed, docked. Erlotinib was considered as the docking standard and it was found that these
compounds mimic Erlotinib and bind to the same amino acids pocket region of active binding site
of Erlotinib in PTK receptors with much higher affinities involving more number of H bonds with
shorter measure, higher number of good vdW bonds and lesser number of/number bad/ugly vdW
interactions. Thus they show enzyme specificity and are developed as selective PTK inhibitors having
potent anticancer activity. These compounds with potent anticancer (anti-EGFR activity) along with
their Absorption, Distribution, Metabolism, and Excretion (ADME) properties by means of Qikprop
2.2 Tool of, Schrodinger, could be successfully predicted having drug like pharmacokinetic profile.
Conclusion: Molecular modeling is one of the most successful and rapidly growing techniques. It
not only helps in predicting target specific compounds but also helps in reducing cost of valuable
chemicals. In this study successful use of molecular modeling was done and caution was taken to
avoid any chance co-relation. Optimization was done to obtain lead nucleus and after designing
NCE's and docking five compounds with potent anti-EGFR activity and drug like pharmacokinetic
profile could be successfully predicted.