Literature DB >> 28359250

Insight into Discovery of Next Generation Reversible TMLR Inhibitors Targeting EGFR Activating and Drug Resistant T790M Mutants.

Subhash Mohan Agarwal1, Divyani Pal1, Mansi Gupta1, Ravi Saini1.   

Abstract

BACKGROUND: Epidermal growth factor receptor (EGFR) is a well-recognised drug target exploited for treating non-small cell lung cancer (NSCLC). Gefitinib and erlotinib are first generation clinically employed inhibitors used against EGFR activating mutants. However, during course of treatment these inhibitors become ineffective due to the emergence of an acquired secondary mutation. Subsequently, in order to overcome non-responsiveness second and third generation inhibitors were designed having covalent bond and irreversible mode of action. However, these inhibitors were shown to be toxic. This led to the discovery of lead candidates with completely different mode of action and therapeutic efficacy.
OBJECTIVE: We have reviewed the recent efforts undertaken by researchers in discovering newer noncovalent reversible next generation inhibitors for treating NSCLC.
METHODS: We first studied the optimization steps and pharmacokinetic variables of the synthesised molecules. We also analysed bonds and interactions using PDB X-ray crystal structures as well as scaffold and selectivity analysis was undertaken.
RESULTS: We identified that ligand lipophilic efficiency driven potency is a preferable optimisation parameter for maintaining drug likeliness of the molecule. Also, few h-bonds were recognised as major players in affecting the binding of compound. The scaffold analysis revealed that ligand molecules with pyrimidine core exhibit higher inhibitory activity against TMLR, as well as higher selectivity with respect to other kinases.
CONCLUSION: Next generation reversible inhibitors exhibited unique binding mode and were found to occupy three major pockets (ribose pocket, back pocket and hinge region), which is critical for increasing the selectivity of the compound against TMLR mutants. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Entities:  

Keywords:  EGFR; TMLR inhibitors; activating mutation; covalent inhibitors; reversible inhibitors; secondary mutant

Mesh:

Substances:

Year:  2017        PMID: 28359250     DOI: 10.2174/1568009617666170330112842

Source DB:  PubMed          Journal:  Curr Cancer Drug Targets        ISSN: 1568-0096            Impact factor:   3.428


  1 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

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.