EGFRIndb: epidermal growth factor receptor inhibitor database

Aberrant activity of epidermal growth factor receptor (EGFR) family proteins has been found to be associated with a number of human cancers including that of lung and breast. Consequently, the search for EGFR family inhibitors, a well established target of pharmacological and therapeutic value has been ongoing. Therefore, over the years several small molecules, which compete for ATP in the kinase domain have been synthesised and some of them have proved to be effective in attenuating EGFR mediated proliferation. Thus, there exists in literature a vast amount of experimental data on EGFR tyrosine kinase inhibitors. In this paper, we describe a comprehensive database EGFRIndb that contains details of the small molecular inhibitors of EGFR family.

EGFRIndb is a literature curated database of small synthetic molecular inhibitors of EGFR. It consists of 4581 compounds showing in vitro inhibitory activities (IC50, IC80, GI50, GI90, EC50, Ki, Kd and percentage inhibition) either against EGFR or its different isoforms i.e. Erbb2 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 2) and Erbb4 (v-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 4) or various mutants. For each compound, database provides information on structure, experimentally determined inhibitory activity of compound against kinase as well as various cell lines, properties (physical, elemental and topological) and drug likeness. Additionally, it provides information on irreversible as well as dual inhibitors that have gained importance in recent years due to the emergence of clinical resistance to known drugs. As compound activity against similar kinases is a measure of its selectivity and specificity, the database also provides this information. It also provides simple search, advanced search, browse facility as well as a tool for structure based searching.

EGFRIndb gathers biological and chemical information on EGFR inhibitors from the literature. It is hoped that it will serve as a useful resource in drug discovery and provide data for docking, virtual screening and Quantitative structure-activity relationship (QSAR) model development to the cancer researchers.