In silico drug repurposing in non-small cell lung cancer by docking and molecular dynamic simulations for epidermal growth factor receptor allosteric site inhibition - Swastika Maity, Megh Pravin Vithalkar, Krishnaprasad Baby, Usha Yogendra Nayak, Karkala Sreedhara Ranganath Pai, Yogendra Nayak (Journal of Applied Pharmaceutical Science, 2025)
Non-small cell lung cancer (NSCLC) has extreme mortality and morbidity rates compared to colon, prostate, and breast cancer. Presently, available medications for NSCLC have failed to provide the desired therapeutic outcome. Therefore, there is a need to speed up the drug development process, cost-effectively. New chemical entities such as EAI045 and EAI001 and a few small molecules allosterically inhibit epidermal growth factor receptor (EGFR) by binding to the allosteric domain to form a stable protein-ligand complex. This discovery is a milestone in inhibiting the EGFR-mutations such as L858R/T790M/C797S and L858R/T790M point alteration in lung cancer cells. Hence, our study aimed to identify and repurpose available drugs for treating NSCLC by targeting the EGFR allosteric region. A homology model was developed as the available protein structure had missing loops and amino acid sequences. Food and Drug Administration (FDA)-approved drugs (~2800) were docked with the validated homology protein using Schrodinger® Maestro software. Two docking methods were used, consensual docking and normal docking, for the comparative study of the hit compounds to increase the probability of identifying the most potent compound for the EGFR allosteric site. Molecular dynamic simulations were performed on the shortlisted compounds to check the potency. The best hits, such as polydatin, ezetimibe, methotrexate, and arbutamine, were identified based on the root mean square deviation of protein−ligand interaction, ligand stability, and bond interactions between the ligand and protein. The study highlights the potential FDA-approved drugs that can be repurposed for NSCLC treatment.
Keywords: In silico drug discovery, computational simulation, homology model, methotrexate, arbutamine, polydatin, ezetimibe