HDAC6 score: to treat or not to treat?

Re-exploration of tetrahydro-β-carboline scaffold: Discovery of selective histone deacetylase 6 inhibitors with neurite outgrowth-promoting and neuroprotective activities

Histone deacetylase 6 (HDAC6) has drawn more and more attention for its potential application in Alzheimer's disease (AD) therapy. A series of tetrahydro-β-carboline (THβC) hydroxamic acids with aryl linker were synthesized. In enzymatic assay, all compounds exhibited nanomolar IC50 values. The most promising compound 11d preferentially inhibited HDAC6 (IC50, 8.64 nM) with approximately 149-fold selectivity over HDAC1. Molecular simulation revealed that the hydroxamic acid of 11d could bind to the zinc ion by a bidentate chelating manner. In vitro, 11d induced neurite outgrowth of PC12 cells without producing toxic effects and showed obvious neuroprotective activity in a model of H2O2-induced oxidative stress.

Technologies of targeting histone deacetylase in drug discovery: Current progress and emerging prospects

Histone deacetylases (HDACs) catalyze the hydrolysis of acetyl-l-lysine side chains in histones and non-histones, which are key to epigenetic regulation in humans. Targeting HDACs has emerged as a promising strategy for treating various types of cancer, including myeloma and hematologic malignancies. At present, numerous small molecule inhibitors targeting HDACs are actively being investigated in clinical trials. Despite their potential efficacy in cancer treatment, HDAC inhibitors suffer from multi-directional selectivity and preclinical resistance issues. Hence, developing novel inhibitors based on cutting-edge medicinal chemistry techniques is essential to overcome these limitations and improve clinical outcomes. This manuscript presents an extensive overview of the properties and biological functions of HDACs in cancer, provides an overview of the current state of development and limitations of clinical HDAC inhibitors, and analyzes a range of innovative medicinal chemistry techniques that are applied. These techniques include selective inhibitors, dual-target inhibitors, proteolysis targeting chimeras, and protein-protein interaction inhibitors.

NetBID2 provides comprehensive hidden driver analysis

Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .