14 research outputs found

    Preserved regulation of renal perfusion pressure by small and intermediate conductance KCa channels in hypertensive mice with or without renal failure

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    The purpose of this study was to assess, in the murine kidney, the mechanisms underlying the endothelium-dependent control of vascular tone and whether or not, in a severe model of hypertension and renal failure, KCa channels contribute to its regulation. Wild-type (BL) and double-transgenic female mice expressing human angiotensinogen and renin (AR) genes received either control or a high-salt diet associated to a nitric oxide (NO) synthase inhibitor treatment (BLSL and ARSL). Changes in renal perfusion pressure (RPP) were measured in isolated perfused kidneys. BLSL and AR were moderately hypertensive without kidney disease while ARSL developed severe hypertension and renal failure. In the four groups, methacholine induced biphasic endothelium-dependent responses, a transient decrease in RPP followed by a cyclooxygenase-dependent increase in RPP. In the presence or not of indomethacin, the vasodilatations were poorly sensitive to NO synthase inhibition. However, in the presence of cyclooxygenase and NO synthase inhibitors, apamin, and/or TRAM-34, blockers of KCa2.3 and KCa3.1, respectively, abolished the decrease in RPP in response to either methacholine or the two activators of KCa2.3/KCa3.1, NS309, and SKA-31. Thus, KCa2/3 channels play a major role in the regulation of murine kidney perfusion and this mechanism is maintained in hypertension, even when severe and associated with kidney damage

    Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

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    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. </div
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