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  • Several highly potent CysLT receptor


    Several highly potent CysLT1 receptor antagonists with large structural AZD6738 have been developed. Beyond antagonists with structures analogous to cysteinyl leukotrienes, other subclasses comprise: (a) hydroxyacetophenones, (b) indoles and indazoles, (c) quinolines, benzothiazoles, and thiazoles. On the basis of structure–activity analyses several authors developed pharmacophore models for CysLT1 receptor antagonists.[16], [17], [18] The aim of the present study is to develop valid and predictive chemometric QSAR models for a set of CysLT1 receptor antagonists, to demonstrate the applicability of GRID independent descriptors (=GRIND) for this purpose and to compare our GRIND/ALMOND approach with previous pharmacophore studies.
    Datasets and methodology
    Results and discussion
    Conclusion The present study describes the development of valid and predictive QSAR models for a set of 54 CysLT1 receptor antagonists of the quinolinyl(bridged)aryl type using the chemometric GRIND/ALMOND approach, which is alignment-independent, has simple computational demands and is correspondingly fast. Via the GRIND/ALMOND approach the entire training set could be modeled without any outlier behavior. Considering that conformational sampling can affect the quality of the model, we generated different conformational ensembles, but did not succeed in obtaining better models than presented here. PLS analysis resulted in a two-component model explaining 67% of the variance for CysLT1 receptor binding. GRIND variables 11-50 and 22-55 are responsible for high CysLT1 receptor affinity; variable 11-62 is detrimental. External predictivity of the GRIND/ALMOND model was tested for a set of 69 CysLT1 receptor antagonists with varying chemical similarity to the training set. Via inclusion of test set compounds with varying chemical similarity we intended to prove the general limits of external predictivity. The quality of prediction mainly coincides with chemical subclassification: phenylene bridged compounds are well predicted; for structures with bridging heterocycles predictions are rather poor. Thus, predictive power significantly corresponds to the extent of training and test set similarity.
    Introduction Chronic inflammation is a highly prevalent co-morbid condition that predicts poor clinical outcome in end-stage renal disease (ESRD) patients [1], [2], [3], enhancing mainly cardiovascular risk and mortality [4]. Evidence for oxidative stress in chronic renal failure (CRF) was based on the elevation of toxic lipid peroxidation products, which cause destruction and damage to cell membranes [5], [6], [7], [8]. Several earlier studies have revealed that these toxic products cause an inflammatory burden in CRF through the generation of an imbalance between increased production of reactive oxygen species (ROS) and limited or decreased antioxidant capacity [9]. A wide array of inflammatory biomarkers, such as C-reactive protein, interleukin (IL)-6 and white blood cell count, has been proven to be robust predictors of poor outcome in the ESRD patients [9], [10]. It is well documented that the inflammatory response in CRF is characterized by activation of neutrophils and collective action of chemotactic mediators [11]. The cellular sources of inflammatory cytokines enhanced in ESRD patients are mainly neutrophils and monocytes. Once neutrophils migrate into the tissue, they release reactive oxygen species, proteases, elastase, myeloperoxidase (MPO), cytokines and various other mediators [12]. Along with the various pro-inflammatory chemokines, cysteinyl leukotrienes (CysLTs), the 5-lipoxygenase metabolites of arachidonic acid, are also proven to be potent inflammatory mediators that cause tissue injury. Antileukotriene drugs, i.e. leukotriene receptor antagonists and synthesis inhibitors, are a new class of anti-inflammatory drugs that have shown efficacy in the treatment of several inflammatory models. A selective reversible CysLT1 receptor antagonist, montelukast (MK-0476), was used in the treatment of asthma and is reported to reduce eosinophilic inflammation in the airways [13], [14], while CysLT1 receptor antagonists or biosynthesis inhibitors have been reported to ameliorate ethanol-induced gastric mucosal damage [15], [16] and experimental colitis [17]. Recently, we have shown that the CysLT1 receptor antagonist, montelukast, ameliorates burn- and sepsis-induced multiple organ damage [18], [19]. Furthermore, we have reported that montelukast improved microscopic damage and renal function and protected against renal ischemia/reperfusion injury in rats [20]. These data suggest that limiting the inflammatory cascade by blocking the CysLT1 receptors may also limit the oxidative damage induced by CRF. Therefore, using a rat model of chronic renal failure, the present study was designed to elucidate the putative protective effect of montelukast on renal functions and oxidant/antioxidant status of the kidney and the affected organs, lung, heart and brain.