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Updated: Oct 8, 2025

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
Studies on ligand-based pharmacophore modeling approach in identifying potent future EGFR inhibitors
Gulam Moin Shaikh1, Manikanta Murahari2, Shikha Thakur1
1Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM'S NMIMS, V.L. Mehta Road, Vile Parle West, Mumbai, 400056, India.
Abstract:
Epidermal growth factor receptor (EGFR) is a validated drug target for cancer chemotherapy. Mutations in EGFR are directly linked with the development of drug resistance and this has led for the development of newer drugs in quest for more efficacious inhibitors. The current research is focused on identifying potential and safe molecules as EGFR inhibitors by using both structure and ligand based computational approaches. In quest for finding newer moieties, we have developed a pharmacophore model utilizing drugs like lazertinib, osimertinib, nazartinib, avitinib, afatininb, and talazoparib that are known to inhibit EGFR along with their downstream signaling. Ligand-based pharmacophore model have been developed to screen the ZINC database through ZINCPharmer webserver. The server has identified 9482 best possible ligands with high pharmacophoric similarity i.e., RMSD value less than 0.2 Å. The top 10 ligands with the criteria of dock score(s) and interactions were further subjected to in silico ADMET studies giving two plausible ligands that were further subjected to Molecular Dynamics and MM/PBSA free energy calculations to ensure stability to the target site. Results deduced by in silico work in the current study may be corroborated biologically in the future. The current work, therefore, provides ample opportunity for computational and medicinal chemists to work in allied areas to facilitate the design and development of novel and more efficacious EGFR inhibitors for future experimental studies.
Insights
Researchers identified novel drug candidates for cancer by computationally screening molecules for inhibiting the epidermal growth factor receptor (EGFR). This study offers new avenues for developing more effective EGFR inhibitors for future experimental validation.
Area of Science:
- Computational chemistry and drug discovery.
- Oncology and molecular biology.
- Pharmacology and medicinal chemistry.
Background:
- Epidermal growth factor receptor (EGFR) is a key target in cancer chemotherapy.
- EGFR mutations drive drug resistance, necessitating novel inhibitor development.
- Current research focuses on identifying safe and effective EGFR inhibitors using computational methods.
Purpose of the Study:
- To develop a pharmacophore model for identifying novel EGFR inhibitors.
- To screen chemical databases for potential drug candidates targeting EGFR.
- To evaluate the stability and drug-likeness of identified compounds.
Main Methods:
- Developed a ligand-based pharmacophore model using known EGFR inhibitors.
- Screened the ZINC database using the ZINCPharmer webserver.
- Conducted in silico ADMET studies, molecular dynamics, and MM/PBSA calculations on top-ranked ligands.
Main Results:
- Identified 9482 potential ligands with high pharmacophoric similarity (RMSD < 0.2 Å).
- Selected top ligands based on dock scores and interactions.
- Two plausible drug candidates were identified and validated through molecular dynamics and free energy calculations.
Conclusions:
- The study successfully identified potential novel molecules for EGFR inhibition using computational approaches.
- These findings provide a foundation for future biological validation and drug development.
- The research facilitates the design of more efficacious EGFR inhibitors for cancer therapy.
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