Literature DB >> 30811850

QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors.

Shehnaz Fatima1, Divyani Pal1, Subhash Mohan Agarwal1.   

Abstract

EGFR is a well-established therapeutic target of clinical relevance in cancer. However, acquisition of secondary mutation (T790M) makes first-generation inhibitors ineffective. Therefore, to circumvent the problem of resistance, new T790M/L858R (TMLR) double mutant inhibitors are required. In this study, fragment-based QSAR models (GQSAR) were generated for pyridinylimidazole derivatives having biological activity against TMLR mutants. The GQSAR model developed using partial least squares regression via stepwise forward-backward variable selection technique showed best results as judged using statistical parameters (r2 , q2 , and pred_r2 ). Additionally, applicability domain of the model was verified using Williams plot, which indicated that the predicted data are reliable. The GQSAR provided site-specific clues wherein modifications related to decreasing lipophilic character and rotatable bonds and increasing SaaCHE-index are required for improving inhibitory activity. Overall, the study indicated that the presence of acrylamide at R5 is essential for covalent bond formation with Cys797 and occurrence of aromatic residue at R2 is required for occupying hydrophobic region next to Met790 gatekeeper residue. Based on this information, new derivatives were designed that show better inhibitory activity than the experimentally reported most active molecules. Thus, the model developed can be used to design new pyridinylimidazole derivatives with improved TMLR bioactivity.
© 2019 John Wiley & Sons A/S.

Entities:  

Keywords:  EGFR; QSAR; T790M; Williams plot; double mutant inhibitor; irreversible inhibitors

Year:  2019        PMID: 30811850     DOI: 10.1111/cbdd.13505

Source DB:  PubMed          Journal:  Chem Biol Drug Des        ISSN: 1747-0277            Impact factor:   2.817


  3 in total

1.  Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach.

Authors:  Subhash M Agarwal; Prajwal Nandekar; Ravi Saini
Journal:  RSC Adv       Date:  2022-06-07       Impact factor: 4.036

2.  EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2.

Authors:  Ravi Saini; Subhash Mohan Agarwal
Journal:  Mol Divers       Date:  2021-08-03       Impact factor: 2.943

3.  Osimertinib or EGFR-TKIs/chemotherapy in patients with EGFR-mutated advanced nonsmall cell lung cancer: A meta-analysis.

Authors:  Lei Huang; Hao Huang; Xiao-Ping Zhou; Jin-Feng Liu; Chun-Rong Li; Min Fang; Jun-Rong Wu
Journal:  Medicine (Baltimore)       Date:  2019-10       Impact factor: 1.817

  3 in total

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