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Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and
Jagat Singh Chauhan1, Sandeep Kumar Dhanda1, Deepak Singla1
1Bioinformatics Centre, Institute of Microbial Technology (CSIR), Chandigarh, India.
Abstract:
Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.
Insights
Researchers developed quantitative structure-activity relationship (QSAR) models to design novel quinazoline derivatives targeting wild-type and mutant epidermal growth factor receptor (EGFR) for lung cancer treatment. These models facilitate the discovery of new anticancer drugs by predicting inhibitor efficacy.
Area of Science:
- Medicinal Chemistry
- Computational Drug Design
- Oncology
Background:
- Epidermal growth factor receptor (EGFR) overexpression drives various cancers, including lung cancer, through downstream signaling.
- Targeting EGFR's kinase domain, specifically ATP binding, is a validated strategy for cancer therapy.
- Existing quinazoline-based drugs inhibit wild-type EGFR, necessitating new approaches for mutant forms.
Purpose of the Study:
- To develop quantitative structure-activity relationship (QSAR) models for designing novel inhibitors against wild-type and mutant EGFR.
- To create predictive models for quinazoline derivatives targeting wild-type EGFR.
- To design imidazothiazoles/pyrazolopyrimidines derivatives targeting mutant EGFR (L858R).
Main Methods:
- Developed three prediction methods: one for wild-type EGFR, one for mutant EGFR (L858R), and a hybrid model for both.
- Trained and validated models using datasets of quinazoline-based inhibitors for wild-type EGFR (n=128) and mutant EGFR (n=unknown).
- Evaluated model performance based on the correlation between predicted and experimentally determined inhibition (IC50) values.
Main Results:
- Achieved a maximum correlation of 0.90 for wild-type EGFR inhibitors on the validation dataset.
- Obtained maximum correlations ranging from 0.834 to 0.850 for mutant EGFR inhibitors.
- Developed an integrated hybrid model with correlations between 0.761 to 0.850 on various datasets.
Conclusions:
- Successfully developed QSAR models for predicting inhibitors against both wild-type and mutant EGFR.
- Created a webserver and standalone software to promote open-source drug discovery for novel anticancer agents.
- The developed tools are expected to aid in designing new drugs targeting EGFR in cancer patients.
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