Thienopyridinyl and Thiazolopyridinyl Compounds as IRAK4 Inhibitors

The recent advance of Interleukin-1 receptor associated kinase 4 inhibitors for the treatment of inflammation and related diseases

The interleukin-1 receptor associated kinase 4 (IRAK-4) is a member of serine-threonine kinase family, which plays an important role in the regulation of interleukin-1 receptors (IL-1R) and Toll-like receptors (TLRs) related signaling pathways. At present, the IRAK-4 mediated inflammation and related signaling pathways contribute to inflammation, which are also responsible for other autoimmune diseases and drug resistance in cancers. Therefore, targeting IRAK-4 to develop single-target, multi-target inhibitors and proteolysis-targeting chimera (PROTAC) degraders is an important direction for the treatment of inflammation and related diseases. Moreover, insight into the mechanism of action and structural optimization of the reported IRAK-4 inhibitors will provide the new direction to enrich the clinical therapies for inflammation and related diseases. In this comprehensive review, we introduced the recent advance of IRAK-4 inhibitors and degraders with regards to structural optimization, mechanism of action and clinical application that would be helpful for the development of more potent chemical entities against IRAK-4.

PROTAC'ing oncoproteins: targeted protein degradation for cancer therapy

Molecularly targeted cancer therapies substantially improve patient outcomes, although the durability of their effectiveness can be limited. Resistance to these therapies is often related to adaptive changes in the target oncoprotein which reduce binding affinity. The arsenal of targeted cancer therapies, moreover, lacks coverage of several notorious oncoproteins with challenging features for inhibitor development. Degraders are a relatively new therapeutic modality which deplete the target protein by hijacking the cellular protein destruction machinery. Degraders offer several advantages for cancer therapy including resiliency to acquired mutations in the target protein, enhanced selectivity, lower dosing requirements, and the potential to abrogate oncogenic transcription factors and scaffolding proteins. Herein, we review the development of proteolysis targeting chimeras (PROTACs) for selected cancer therapy targets and their reported biological activities. The medicinal chemistry of PROTAC design has been a challenging area of active research, but the recent advances in the field will usher in an era of rational degrader design.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.