Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Research output: Contribution to journalJournal articlepeer-review

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Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. / Narayanan, Dilip; Gani, Osman ABSM; Gruber, Franz XE; Engh, Richard A.

In: Journal of Cheminformatics, Vol. 9, 43, 2017.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Narayanan, D, Gani, OABSM, Gruber, FXE & Engh, RA 2017, 'Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR', Journal of Cheminformatics, vol. 9, 43. https://doi.org/10.1186/s13321-017-0229-8

APA

Narayanan, D., Gani, O. ABSM., Gruber, F. XE., & Engh, R. A. (2017). Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. Journal of Cheminformatics, 9, [43]. https://doi.org/10.1186/s13321-017-0229-8

Vancouver

Narayanan D, Gani OABSM, Gruber FXE, Engh RA. Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. Journal of Cheminformatics. 2017;9. 43. https://doi.org/10.1186/s13321-017-0229-8

Author

Narayanan, Dilip ; Gani, Osman ABSM ; Gruber, Franz XE ; Engh, Richard A. / Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR. In: Journal of Cheminformatics. 2017 ; Vol. 9.

Bibtex

@article{11fc5cb063324a70b552ac5c000dcbeb,
title = "Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR",
abstract = "Drug 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.",
author = "Dilip Narayanan and Gani, {Osman ABSM} and Gruber, {Franz XE} and Engh, {Richard A}",
year = "2017",
doi = "10.1186/s13321-017-0229-8",
language = "English",
volume = "9",
journal = "Journal of Cheminformatics",
issn = "1758-2946",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

AU - Narayanan, Dilip

AU - Gani, Osman ABSM

AU - Gruber, Franz XE

AU - Engh, Richard A

PY - 2017

Y1 - 2017

N2 - Drug 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.

AB - Drug 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.

U2 - 10.1186/s13321-017-0229-8

DO - 10.1186/s13321-017-0229-8

M3 - Journal article

C2 - 29086093

VL - 9

JO - Journal of Cheminformatics

JF - Journal of Cheminformatics

SN - 1758-2946

M1 - 43

ER -

ID: 180734227