Development of a NanoBRET assay to validate dual inhibitors of Sirt2-mediated lysine deacetylation and defatty-acylation that block prostate cancer cell migration

Sirtuin2 (Sirt2) with its NAD+-dependent deacetylase and defatty-acylase activities plays a central role in the regulation of specific cellular functions. Dysregulation of Sirt2 activity has been associated with the pathogenesis of many diseases, thus making Sirt2 a promising target for pharmaceutical intervention. Herein, we present new high affinity Sirt2 selective Sirtuin-Rearranging Ligands (SirReals) that inhibit both Sirt2-dependent deacetylation and defatty-acylation in vitro and in cells. We show that dual inhibition of Sirt2 results in strongly reduced levels of the oncogene c-Myc and an inhibition of cancer cell migration. Furthermore, we describe the development of a NanoBRET-based assay for Sirt2, thereby providing a method to study cellular target engagement for Sirt2 in a straightforward and accurately quantifiable manner. Applying this assay, we could confirm cellular Sirt2 binding of our new Sirt2 inhibitors and correlate their anticancer effects with their cellular target engagement.

In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha

Inflammation involves a complex biological response of the body tissues to damaging stimuli. When dysregulated, inflammation led by biomolecular mediators such as caspase-1 and tumor necrosis factor-alpha (TNF-alpha) can play a detrimental role in the progression of different medical conditions such as cancer, neurological disorders, autoimmune diseases, and cytokine storms caused by viral infections such as COVID-19. Computational approaches can accelerate the search for dual-target drugs able to simultaneously inhibit the aforementioned proteins, enabling the discovery of wide-spectrum anti-inflammatory agents. This work reports the first multicondition model based on quantitative structure–activity relationships and a multilayer perceptron neural network (mtc-QSAR-MLP) for the virtual screening of agency-regulated chemicals as versatile anti-inflammatory therapeutics. The mtc-QSAR-MLP model displayed accuracy higher than 88%, and was interpreted from a physicochemical and structural point of view. When using the mtc-QSAR-MLP model as a virtual screening tool, we could identify several agency-regulated chemicals as dual inhibitors of caspase-1 and TNF-alpha, and the experimental information later retrieved from the scientific literature converged with our computational results. This study supports the capabilities of our mtc-QSAR-MLP model in anti-inflammatory therapy with direct applications to current health issues such as the COVID-19 pandemic.