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Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
Structure-activity Relationship Study on Therapeutically Relevant EGFR Double Mutant Inhibitors
Shehnaz Fatima1, Subhash M Agarwal1
1Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida-201301, India.
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 the 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.
Insights
Researchers developed quantitative structure-activity relationship (QSAR) models to design new amino-pyrimidine inhibitors targeting the T790M/L858R (TMLR) double mutant EGFR. These models identify key chemical features for enhanced bioactivity against resistant non-small cell lung cancer.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Pharmacology
Background:
- Epidermal growth factor receptor (EGFR) is a key target in cancer therapy.
- First-generation EGFR inhibitors are effective against the L858R mutation in non-small cell lung cancer (NSCLC).
- Secondary T790M mutations confer resistance to existing EGFR inhibitors, leading to disease relapse.
Purpose of the Study:
- To develop novel reversible inhibitors targeting the T790M/L858R (TMLR) double mutant EGFR.
- To overcome acquired resistance to EGFR tyrosine kinase inhibitors in NSCLC.
Main Methods:
- Utilized Fragment-based Quantitative Structure-Activity Relationship (G-QSAR) modeling.
- Employed partial least squares regression with stepwise forward-backward variable selection.
- Analyzed amino-pyrimidine derivatives for biological activity against the TMLR mutant enzyme.
Main Results:
- Developed robust and predictive G-QSAR models (r² = 0.86, q² = 0.81, predicted r² = 0.62).
- Identified key molecular descriptors for enhancing bioactivity: increasing saturated carbon and retention index at R1, lipophilic character at R2, and polarizability at R3.
- Highlighted the importance of descriptor interactions (Sum(R1-SsssCHcount, R2-slogp) and Mult(R1-chi3, R3-polarizabilityAHC)) for inhibitory potency.
Conclusions:
- The study provides site-specific insights into chemical group variations influencing TMLR inhibitor potency.
- These findings can guide the rational design of new amino-pyrimidine derivatives with improved anti-cancer activity.
- The developed models offer a valuable tool for designing next-generation EGFR inhibitors.
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