@article{e6864aad4ba8452cb55427ee180e2cf8,
title = "Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4",
abstract = "Inhibition of cancer-promoting kinases is an established therapeutic strategy for the treatment of many cancers, although resistance to kinase inhibitors is common. One way to overcome resistance is to target orthogonal cancer-promoting pathways. Bromo and Extra-Terminal (BET) domain proteins, which belong to the family of epigenetic readers, have recently emerged as promising therapeutic targets in multiple cancers. The development of multitarget drugs that inhibit kinase and BET proteins therefore may be a promising strategy to overcome tumor resistance and prolong therapeutic efficacy in the clinic. We developed a general computational screening approach to identify novel dual kinase/bromodomain inhibitors from millions of commercially available small molecules. Our method integrated machine learning using big datasets of kinase inhibitors and structure-based drug design. Here we describe the computational methodology, including validation and characterization of our models and their application and integration into a scalable virtual screening pipeline. We screened over 6 million commercially available compounds and selected 24 for testing in BRD4 and EGFR biochemical assays. We identified several novel BRD4 inhibitors, among them a first in class dual EGFR-BRD4 inhibitor. Our studies suggest that this computational screening approach may be broadly applicable for identifying dual kinase/BET inhibitors with potential for treating various cancers.",
author = "Allen, {Bryce K.} and Saurabh Mehta and Ember, {Stewart W.J.} and Ernst Schonbrunn and Nagi Ayad and Sch{\"u}rer, {Stephan C.}",
note = "Funding Information: This research was supported by grant U54HL127624 awarded by the National Heart, Lung, and Blood Institute through funds provided by the trans-NIH Library of Integrated Network-based Cellular Signatures (LINCS) Program (http://www.lincsproject.org/) and the trans-NIH Big Data to Knowledge (BD2K) initiative (http://www.bd2k.nih.gov). The authors thank ChemAxon for providing the academic research license for their Cheminformatics software tools including JChem for Excel, Instant JChem, and the Marvin tools. We thank D.E. Shaw Research (DESRES) and Schrodinger for providing an academic research licence for the Molecular Dynamics (MD) simulation package Desmond and the Maestro Molecular Modeling Environment. SM acknowledges the fellowship from the Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India, through a CREST award (BT/IN/DBT-CREST Awards/29/SM/2012-13). BKA acknowledges Dr. Chiara Pastori, Dr. Andrea Johnstone and Dr. Claude-Henry Volmar for their help with experimental preparation. We would like to thank all members of the Center for Therapeutic Innovation and members of the Lemmon-Bixby laboratory for helpful discussions. This work was partly funded by R01NS067289 to NGA. The authors acknowledge resources of the Center for Computational Science at the University of Miami.",
year = "2015",
month = nov,
day = "24",
doi = "10.1038/srep16924",
language = "English (US)",
volume = "5",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
}