QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR

PLoS One. 2014 Jul 3;9(7):e101079. doi: 10.1371/journal.pone.0101079. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Drug Design*
  • ErbB Receptors / antagonists & inhibitors*
  • ErbB Receptors / genetics
  • ErbB Receptors / metabolism
  • Humans
  • Models, Biological
  • Molecular Docking Simulation
  • Mutation
  • Protein Kinase Inhibitors / chemistry
  • Protein Kinase Inhibitors / pharmacology*
  • Pyrimidines / chemistry
  • Pyrimidines / pharmacology*
  • Quantitative Structure-Activity Relationship
  • Quinazolines / chemistry
  • Quinazolines / pharmacology*
  • Thiazoles / chemistry
  • Thiazoles / pharmacology*

Substances

  • Protein Kinase Inhibitors
  • Pyrimidines
  • Quinazolines
  • Thiazoles
  • EGFR protein, human
  • ErbB Receptors

Grant support

The authors are thankful to the Council of Scientific and Industrial Research (CSIR) (project open source drug discovery (OSDD) and GENESIS BSC0121), Department of Biotechnology (DBT) (project BTISNET) and Indian Council of Medical Research (ICMR) Government of India for financial. SMA is thankful to Department of Biotechnology-IYBA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.