Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4

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

Lysine methyltransferases and their inhibitors

Since 2000, the histone methyltransferases that catalyze the methylation of a number of histone and nonhistone substrates have been discovered. A growing body of literature is indicating that lysine methyltransferases (KMTs) play a crucial role for transcriptional regulation and are involved in cancer and. various other human diseases, thus being of high interest as potential therapeutic targets. In this book chapter, we highlight the discovery, characterization, and application of selective KMT inhibitors, useful for dissecting their physiological functions as well as their disease implications. Over the past decade, there has been an impressive progress regarding the KMT inhibitor discovery, especially conjugating the research interest with the available and novel techniques including new assay methods, high-throughput screening structural biology, and medicinal chemistry approaches. Our goal is to point out herein key advances, challenges, possible future opportunities, and directions, regarding KMT modulation in a preclinical and clinical setting