Shehnaz Fatima and Subhash Mohan Agarwal* Pages 1 - 10 ( 10 )
Background: EGFR is a clinically approved drug target in cancer. The first generation tyrosine kinase inhibitors targeting L858R mutated EGFR are routinely used to treat non-small cell lung cancer (NSCLC). However, the presence of a secondary mutation (T790M) tenders these inhibitors ineffective and thus results in the relapse of the disease.
Objective: New reversible inhibitors are required, which act against T790M/L858R (TMLR) double mutants and overcome resistance. Method: In the present study, various Fragment based QSAR (G-QSAR) models along with interaction terms have been studied for amino-pyrimidine derivatives having biological activity against TMLR mutant enzyme.
Results: The G-QSAR models developed using partial least squares regression via stepwise forward-backward variable selection technique showed the best results. The model showed a high correlation coefficient (r² = 0.86), cross-validation coefficient (q² = 0.81) and predicted correlation (predicted r² = 0.62), which indicated that the model is robust and predictive. Based on the model, it was revealed that at R1 position increasing saturated carbon (number of –CH atom connected with 3 single bonds i.e. SsssCHcount) and retention index (chi3) is desired for enhancement of bioactivity. Additionally, at the R2 position, increasing lipophilic character (slogp) and at site R3, the polarizability of compound need to be increased for better inhibitory activity. We further studied the contribution of interactions among significant descriptors in enhancing the activity of the compounds. It revealed that the presence of Sum((R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC) are the most significantly influencing descriptors. We further compared the variation in the most and least active compounds which established that retention of the above properties is essential for imparting significant inhibitory activity to these molecules.
Conclusion: The study provides site specific information wherein chemical group variation influences the inhibitory potency of TMLR amino-pyrimidine inhibitors, which can be used for designing new molecules with the desired activity.
EGFR, Double mutant inhibitor, Fragment based QSAR, Descriptors, Interaction descriptors, reversible inhibitors, amino-pyrimidine
Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida-201301, Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida-201301