title: In silico identification of EGFR-T790M inhibitors with novel scaffolds: start with extraction of common features.
abstract: BACKGROUND
  Epidermal growth factor receptor (EGFR) is an attractive therapeutic target for a number of human tumors including non-small cell lung cancer (NSCLC). Most patients with NSCLC and somatic mutations have shown a dramatic initial clinical response to reversible EGFR inhibitors. The clinical efficacy of reversible inhibitors is, however, ultimately limited due to the emergence of drug resistance, which is usually conferred by the EGFR T790M mutation. Importantly, irreversible, synthetic small molecule inhibitors are currently evaluated and some of them have been shown to overcome the acquired resistance that is oftentimes observed in these patients. Thus far, irreversible EGFR inhibitors as a drug class have not received regulatory approval due in part to their poor effectiveness at clinically achievable concentrations. Therefore, there is an urgent need to discover and develop novel, potent irreversible inhibitors against the EGFR T790M mutation.
  
  
  METHODS
  In the following study, we report a novel "hybrid strategy" to identify irreversible EGFR inhibitors with active scaffolds starting with the identification and extraction of a common chemical reactive feature and a pharmacophore feature. The chemical reactive feature was elucidated by investigating 138 currently known irreversible inhibitors at B3LYP/6-31G(d) level using the density function theory method. The pharmacophore feature was extracted from the same inhibitors using pharmacophore modeling. Based on these unique features, two constraints were set while calibrating the protocols of in silico screening. Compounds bearing these specific features were obtained from the National Cancer Institute diversity database to form our subsequent library. Finally, a structure based virtual screening against the library was conducted using standard protocols validated in our lab.
  
  
  RESULTS
  Twenty-eight candidate compounds that demonstrated antitumor activity and that had novel scaffolds different from commonly known quinazoline/quinoline analogs were obtained. The interaction modes between three representative candidates and our model system are similar to that between the model system and the reference compound T-001, which has previously been reported to be one of the most potent of the 138 irreversible inhibitors.
  
  
  CONCLUSIONS
  The hybrid strategy starting with the extraction of common features is an effective approach to design potential irreversible inhibitors with novel scaffolds and therefore to obtain lead molecules in the selection process. These candidates possessing unique scaffolds have a strong likelihood to act as further starting points in the preclinical development of potent irreversible T790M EGFR inhibitors.
authors.affiliation: State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, #1 Keyuan Road 4, Chengdu, People's Republic of China. xiang_mingli@scu.edu.cn
authors.forename: Mingli
authors.initials: M
authors.lastname: Xiang
authors.forename: Kai
authors.initials: K
authors.lastname: Lei
authors.forename: Wenjie
authors.initials: W
authors.lastname: Fan
authors.forename: Yuchun
authors.initials: Y
authors.lastname: Lin
authors.forename: Gu
authors.initials: G
authors.lastname: He
authors.forename: Mingli
authors.initials: M
authors.lastname: Yang
authors.forename: Lijuan
authors.initials: L
authors.lastname: Chen
authors.forename: Yirong
authors.initials: Y
authors.lastname: Mo
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D003198:Computer Simulation; D015195:Drug Design; D019008:Drug Resistance, Neoplasm; D066246:ErbB Receptors / Q000037:antagonists & inhibitors* / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D058990:Molecular Targeted Therapy; D009154:Mutation; D011799:Quinazolines / Q000737:chemistry / Q000494:pharmacology; D011804:Quinolines / Q000737:chemistry / Q000494:pharmacology; D054852:Small Molecule Libraries
chemical_list: D000970:Antineoplastic Agents; D011799:Quinazolines; D011804:Quinolines; D054852:Small Molecule Libraries; D066246:ErbB Receptors
keywords: NCI database; drug resistant; mutant EGFR; pharmacophore modeling; quantum chemical calculation; virtual screening