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dc.contributor.authorNarayanan, Dilip
dc.contributor.authorGani, Osman
dc.contributor.authorGruber, Franz
dc.contributor.authorEngh, Richard Alan
dc.date.accessioned2018-03-22T08:19:21Z
dc.date.available2018-03-22T08:19:21Z
dc.date.issued2017-07-04
dc.description.abstractDrug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.en_US
dc.description.sponsorshipNorwegian Cancer Societyen_US
dc.descriptionSource at <a href=https://doi.org/10.1186/s13321-017-0229-8> https://doi.org/10.1186/s13321-017-0229-8 </a>.en_US
dc.identifier.citationNarayanan, D., Gani, O., Gruber, F. & Engh, R.A. (2017). Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. Journal of Cheminformatics, 9(43).en_US
dc.identifier.cristinIDFRIDAID 1537066
dc.identifier.doi10.1186/s13321-017-0229-8
dc.identifier.issn1758-2946
dc.identifier.urihttps://hdl.handle.net/10037/12408
dc.language.isoengen_US
dc.publisherSpringerOpen / Chemistry Centralen_US
dc.relation.journalJournal of Cheminformatics
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FRINATEK/191303/Norway/Chemical determinants of binding to ATP dependent enzymes and chemical library design//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectLung canceren_US
dc.subjectStructure based drug designen_US
dc.subjectALKen_US
dc.subjectMETen_US
dc.subjectEGFRen_US
dc.subjectProtein kinase inhibitoren_US
dc.subjectProtein flexibilityen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Kjemi: 440::Legemiddelkjemi: 448en_US
dc.subjectVDP::Mathematics and natural science: 400::Chemistry: 440::Pharmaceutical chemistry: 448en_US
dc.titleData driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFRen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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