Tertiary alkylamines as nucleophiles in substitution reactions at heteroaromatic halide during the synthesis of the highly potent pirinixic acid derivative 2-(4-chloro-6-(2,3-dimethylphenylamino)pyrimidin-2-ylthio)octanoic acid (YS-121)

YS-121 [2-(4-chloro-6-(2,3-dimethylphenylamino)pyrimidin-2-ylthio)octanoic acid] is the result of target-oriented structural derivatization of pirinixic acid. It is a potent dual PPARα/γ-agonist, as well as a potent dual 5-LO/mPGES-1-inhibitor. Additionally, recent studies showed an anti-inflammatory efficacy in vivo. Because of its interference with many targets, YS-121 is a promising drug candidate for the treatment of inflammatory diseases. Ongoing preclinical studies will thus necessitate huge amounts of YS-121. To cope with those requirements, we have optimized the synthesis of YS-121. Surprisingly, we isolated and characterized byproducts during the resulting from nucleophilic aromatic substitution reactions by different tertiary alkylamines at a heteroaromatic halide. These amines should actually serve as assisting bases, because of their low nucleophilicity. This astonishing fact was not described in former publications concerning that type of reaction and, therefore, might be useful for further reaction improvement in general. Furthermore, we could develop a proposal for the mechanism of that byproduct formation

Johanniskraut - von Inhaltsstoffen und anderen Unwägbarkeiten

Die Depression gehört zu den häufigsten Volkskrankheiten. Derzeit sind rund vier Millionen Deutsche an einer behandlungsbedürftigen Depression erkrankt. Die Erkrankung verläuft typischerweise in Form von Episoden, die Wochen bis Monate, manchmal auch Jahre anhalten können. Wenn die Erkrankung unbehandelt bleibt, kann sie wiederkehren und einen chronischen Verlauf nehmen. Rund 75 Prozent der Betroffenen erleiden nach einer Ersterkrankung innerhalb von fünf Jahren mindestens eine neue depressive Phase. Zudem werden mit steigender Episodenzahl die episodenfreien Zwischenzeiten immer kürzer. Es gilt heute als unstrittig, dass mehr als die Hälfte aller Depressionen nicht diagnostiziert und allenfalls ein Fünftel adäquat behandelt werden. Das verursacht nicht nur enorme Kosten für die Volkswirtschaft, sondern ist für die Betroffenen auch mit erheblichem Leid und Lebensgefahr verbunden

Wie sekundäre Pflanzeninhaltsstoffe uns vor Krankheiten schützen : von molekularen Wirkmechanismen zu neuen Medikamenten

Wirkungen von Heilpflanzen, Gewürzen, Tees und Lebensmitteln werden in der Naturheilkunde seit der Antike genutzt. Pharmakologisch wirksam sind in der Regel nur die sekundären Pflanzeninhaltsstoffe. Diese in den oft aus vielen Bestandteilen zusammengesetzten Naturstoffen aufzuspüren und ihren molekularbiologischen Wirkungsmechanismus im Körper aufzuklären, ist das Ziel eines Forschungsnetzwerks am Frankfurter ZAFES (Zentrum für Arzneimittelforschung, -Entwicklung und -Sicherheit). So konnten Pharmazeuten und Kliniker gemeinsam herausfinden, wie ein Bestandteil des Rotweins, das Resveratrol, vor Darmkrebs schützt. Die Inhaltsstoffe von Salbei und Rosmarin bieten vielversprechende Ausgangspunkte für neue Medikamente gegen Altersdiabetes. Weihrauch, Myrte und Johanniskraut enthalten Wirkstoffe, die Schlüsselenzyme für Entzündungsreaktionen – etwa bei rheumatischen Beschwerden – hemmen

Survey on the Quality of the Top-Selling European and American Botanical Dietary Supplements Containing Boswellic Acids

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SQUIRRELnovo : de novo design of a PPARalpha agonist by bioisosteric replacement

Shape complementarity is a compulsory condition for molecular recognition. In our 3D ligand-based virtual screening approach called SQUIRREL, we combine shape-based rigid body alignment with fuzzy pharmacophore scoring. Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs). We demonstrate the real-life applicability of SQUIRREL by a prospective virtual screening study, where a potent PPARalpha agonist with an EC50 of 44 nM and 100-fold selectivity against PPARgamma has been identified..

Kernel learning for ligand-based virtual screening: discovery of a new PPARgamma agonist

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results

Zafirlukast Is a Dual Modulator of Human Soluble Epoxide Hydrolase and Peroxisome Proliferator-Activated Receptor γ

Cysteinyl leukotriene receptor 1 antagonists (CysLT1RA) are frequently used as add-on medication for the treatment of asthma. Recently, these compounds have shown protective effects in cardiovascular diseases. This prompted us to investigate their influence on soluble epoxide hydrolase (sEH) and peroxisome proliferator activated receptor (PPAR) activities, two targets known to play an important role in CVD and the metabolic syndrome. Montelukast, pranlukast and zafirlukast inhibited human sEH with IC50 values of 1.9, 14.1, and 0.8 μM, respectively. In contrast, only montelukast and zafirlukast activated PPARγ in the reporter gene assay with EC50 values of 1.17 μM (21.9% max. activation) and 2.49 μM (148% max. activation), respectively. PPARα and δ were not affected by any of the compounds. The activation of PPARγ was further investigated in 3T3-L1 adipocytes. Analysis of lipid accumulation, mRNA and protein expression of target genes as well as PPARγ phosphorylation revealed that montelukast was not able to induce adipocyte differentiation. In contrast, zafirlukast triggered moderate lipid accumulation compared to rosiglitazone and upregulated PPARγ target genes. In addition, we found that montelukast and zafirlukast display antagonistic activities concerning recruitment of the PPARγ cofactor CBP upon ligand binding suggesting that both compounds act as PPARγ modulators. In addition, zafirlukast impaired the TNFα triggered phosphorylation of PPARγ2 on serine 273. Thus, zafirlukast is a novel dual sEH/PPARγ modulator representing an excellent starting point for the further development of this compound class