Generous donors: The dithioperoxyanhydrides (CH3 COS)2 , (PhCOS)2 , CH3 COSSCO2 Me and PhCOSSCO2 Me act as thiol-activated hydrogen sulfide donors in aqueous buffer solution. The most efficient donor (CH3 COS)2 can induce a biological response in cells, and advantageously replace hydrogen sulfide in ex vivo vascular studies.

BackgroundSenescence is characterized by a gradual decline in cellular functions, including changes in energy homeostasis and decreased proliferation activity. As cellular power plants, contributors to signal transduction, sources of reactive oxygen species (ROS) and executors of programmed cell death, mitochondria are in a unique position to affect aging-associated processes of cellular decline. Notably, metabolic activation of mitochondria is tightly linked to Ca2+ due to the Ca2+ -dependency of several enzymes in the Krebs cycle, however, overload of mitochondria with Ca2+ triggers cell death pathways. Consequently, a machinery of proteins tightly controls mitochondrial Ca2+ homeostasis as well as the exchange of Ca2+ between the different cellular compartments, including Ca2+ flux between mitochondria and the endoplasmic reticulum (ER).MethodsIn this study, we investigated age-related changes in mitochondrial Ca2+ homeostasis, mitochondrial-ER linkage and the activity of the main ROS production site, the mitochondrial respiration chain, in an in vitro aging model based on porcine aortic endothelial cells (PAECs), using high-resolution live cell imaging, proteomics and various molecular biological methods.ResultsWe describe that in aged endothelial cells, increased ER-mitochondrial Ca2+ crosstalk occurs due to enhanced ER-mitochondrial tethering. The close functional inter-organelle linkage increases mitochondrial Ca2+ uptake and thereby the activity of the mitochondrial respiration, but also makes senescent cells more vulnerable to mitochondrial Ca2+-overload-induced cell death. Moreover, we identified the senolytic properties of the polyphenol resveratrol, triggering cell death via mitochondrial Ca2+ overload exclusively in senescent cells.ConclusionBy unveiling aging-related changes in the inter-organelle tethering and Ca2+ communications we have advanced the understanding of endothelial aging and highlighted a potential basis to develop drugs specifically targeting senescent cells.

The purpose of the present work was to elucidate the mechanisms underlying the endothelium-dependent and endothelium-independent components of the vascular relaxation induced by a water-soluble and ruthenium-based carbon monoxide (CO)-releasing agent, tricarbonylchloro(glycinato)ruthenium(II) (CORM-3). Changes in isometric tension and cyclic guanosine monophosphate (cGMP) production were measured in isolated aortic rings from normotensive Wistar-Kyoto rats. Nitric oxide (NO) generation was assessed in cultured human umbilical vein endothelial cells (HUVEC) by electron spin resonance. In rat aortic rings, CORM-3, but not the inactivated compound, iCORM, induced relaxations. In rings with but not in those without endothelium relaxations were partially inhibited by L-nitro-arginine (L-NA), 1H-(1,2,4)-oxadiazolo(4,2-a)quinoxalin-1-one (ODQ), or hydroxocobalamin, inhibitors of NO-synthase, soluble guanylyl cyclase, and scavenger of NO, respectively. In rings with and without endothelium, deoxyhemoglobin abolished the relaxations. A combination of potassium channel blockers (barium, glibenclamide, and iberiotoxin) blunted the relaxation in rings without endothelium. CORM-3 produced an endothelium-dependent generation of cGMP that was inhibited by L-NA. CORM-3, but not iCORM, inhibited the endothelium-dependent relaxation to acetylcholine without affecting the response to sodium nitroprusside. In HUVEC, CORM-3 produced a concentration-dependent release of NO. Therefore, CORM-3-induced relaxations involve the soluble guanylyl cyclase-independent activation of smooth muscle potassium channels. Additionally, CO can produce concomitantly activation and inhibition of NO synthase, the former being responsible for the endothelium- and cGMP-dependent effect of CORM-3, the latter for the inhibition of acetylcholine-induced endothelium-dependent relaxations.

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AIdesigned compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one.The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

No abstract

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

No abstract