@article{VermaRaiKaushiketal.2016,
  author    = {Verma, Nidhi and Rai, Amit Kumar and Kaushik, Vibha and Br{\"u}nnert, Daniela and Chahar, Kirti Raj and Pandey, Janmejay and Goyal, Pankaj},
  title     = {Identification of gefitinib off-targets using a structure-based systems biology approach; their validation with reverse docking and retrospective data mining},
  series = {Scientific Reports},
  volume    = {6},
  journal   = {Scientific Reports},
  number    = {33949},
  doi       = {10.1038/srep33949},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-167621},
  year      = {2016},
  abstract  = {Gefitinib, an EGFR tyrosine kinase inhibitor, is used as FDA approved drug in breast cancer and non-small cell lung cancer treatment. However, this drug has certain side effects and complications for which the underlying molecular mechanisms are not well understood. By systems biology based in silico analysis, we identified off-targets of gefitinib that might explain side effects of this drugs. The crystal structure of EGFR-gefitinib complex was used for binding pocket similarity searches on a druggable proteome database (Sc-PDB) by using IsoMIF Finder. The top 128 hits of putative off-targets were validated by reverse docking approach. The results showed that identified off-targets have efficient binding with gefitinib. The identified human specific off-targets were confirmed and further analyzed for their links with biological process and clinical disease pathways using retrospective studies and literature mining, respectively. Noticeably, many of the identified off-targets in this study were reported in previous high-throughput screenings. Interestingly, the present study reveals that gefitinib may have positive effects in reducing brain and bone metastasis, and may be useful in defining novel gefitinib based treatment regime. We propose that a system wide approach could be useful during new drug development and to minimize side effect of the prospective drug.},
  language  = {en}
}