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

Agarwal, Subhash Mohan; Pal, Divyani; Gupta, Mansi; Saini, Ramesh Kumar

doi:10.2174/1568009617666170330112842

Cited by 9 publications

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“…Drug effect reversibility is an important factor in determining drug administration mode for optimizing efficacy and persistence while avoiding adverse secondary effects (20). The same drug may produce different effects on different types of tumor cells, mainly due to the particular cell type's genetic background (14).…”

Section: Discussionmentioning

confidence: 99%

Antipsychotic agent pimozide promotes reversible proliferative suppression by inducing cellular quiescence in liver cancer

et al. 2019

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The antipsychotic drug pimozide has been found to exhibit anticancer effects. Previously, it was demonstrated that pimozide inhibits hepatocellular carcinoma (HCC) cell growth, but its pharmacodynamic characteristics remain unclear. The aim of the present study was to investigate the reversibility and mechanism of the ability of pimozide to inhibit cell proliferation in liver cancer. Cell viability was determined by Cell Counting Kit-8 and colony formation assay. The cell cycle distribution was analyzed by flow cytometry with Ki-67 and PI staining. ROS production of HCC cells was detected with DCFH-DA and inhibited with NAC treatment. Western blot assay was performed to detect the expression of related signaling molecules in HCC cells. Our results showed that pimozide promoted G0/G1 phase arrest in HCC cell lines without significant cell death. Its anti-proliferative effects on HCC cells were reversible, consistent with involvement of cell quiescence and reactive oxygen species (ROS) production. Pimozide enhanced inhibition of HCC cell proliferation by sorafenib. In conclusion, elucidation of pimozide's reversible proliferation inhibition in liver cancer and additive activity with a well-established anticancer drug warrants further exploration of the potential of pimozide as an adjuvant anticancer therapy.

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Section: Discussionmentioning

confidence: 99%

Antipsychotic agent pimozide promotes reversible proliferative suppression by inducing cellular quiescence in liver cancer

et al. 2019

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“…Also, it was shown that Gln791/Glu762 acts as a hydrogen bond acceptor while interacting with the ligand. 32 Therefore, used the above structural features for defining the pharmacophore definition. As Met793 interaction showed an absolute 100% conservation, it was termed as “essential” constraint while interactions with either Gln791 or Glu762 was defined as “optimal”.…”

Section: Resultsmentioning

confidence: 99%

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

2022

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“…To overcome the resistance to T790M mutation and enable effective treatment, second‐ and third‐generation inhibitors were developed (Yadav, Singh, et al, 2014). These inhibitors covalently bind to cysteine 797 and inhibit mutant EGFR T790M; however, this produces side‐effects (Agarwal, Pal, Gupta, & Saini, 2017). Thus, in the last 7 years researchers have been synthesizing reversible non‐covalent inhibitors which are responsive to lung cancer (Agarwal et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…These inhibitors covalently bind to cysteine 797 and inhibit mutant EGFR T790M; however, this produces side‐effects (Agarwal, Pal, Gupta, & Saini, 2017). Thus, in the last 7 years researchers have been synthesizing reversible non‐covalent inhibitors which are responsive to lung cancer (Agarwal et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors

2020

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The EGFR is a clinically important therapeutic drug target in lung cancer. The first‐generation tyrosine kinase inhibitors used in clinics are effective against L858R‐mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance‐based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross‐validation and independent validation datasets. It was observed that the random forest‐based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open‐source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti‐TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.

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

Abstract: 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.

Cited by 9 publications

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Antipsychotic agent pimozide promotes reversible proliferative suppression by inducing cellular quiescence in liver cancer

Antipsychotic agent pimozide promotes reversible proliferative suppression by inducing cellular quiescence in liver cancer

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

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors

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