Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer.
Ayed A DeraSumera Zaibnull AreebaNadia HussainNehal RanaHira JavedImtiaz KhanPublished in: Molecules (Basel, Switzerland) (2023)
Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a target protein along with EGFR because of its limited tyrosine kinase activity and ability to activate PI3/AKT pathway responsible for therapy failure. We herein used a BioSolveIT suite to identify potent inhibitors of EGFR and HER3. The schematic process involves screening of databases for constructing compound library comprising of 903 synthetic compounds (602 for EGFR and 301 for HER3) followed by pharmacophore modeling. The best docked poses of compounds with the druggable binding site of respective proteins were selected according to pharmacophore designed by SeeSAR version 12.1.0. Subsequently, preclinical analysis was performed via an online server SwissADME and potent inhibitors were selected. Compound 4k and 4m were the most potent inhibitors of EGFR while 7x effectively inhibited the binding site of HER3. The binding energies of 4k, 4m, and 7x were -7.7, -6.3 and -5.7 kcal/mol, respectively. Collectively, 4k , 4m and 7x showed favorable interactions with the most druggable binding sites of their respective proteins. Finally, in silico pre-clinical testing by SwissADME validated the non-toxic nature of compounds 4k , 4m and 7x providing a promising treatment option for chemoresistant NSCLC.
Keyphrases
- tyrosine kinase
- small cell lung cancer
- epidermal growth factor receptor
- advanced non small cell lung cancer
- brain metastases
- healthcare
- anti inflammatory
- public health
- molecular dynamics
- signaling pathway
- cell proliferation
- machine learning
- replacement therapy
- big data
- cancer therapy
- deep learning
- transcription factor