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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.
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.
Auf 10 Jahre kann das Tochterunternehmen der Goethe-Universität nunmehr zurückblicken: INNOVECTIS ist zuständig für den erfolgreichen Transfer von akademischem Know-how in die wirtschaftliche Praxis. Wir haben anlässlich der Festveranstaltung auf dem Campus Westend dem Aufsichtsratsvorsitzenden Prof. Manfred Schubert-Zsilavecz, dem Vorsitzenden des Bewertergremiums, Prof. Jürgen Bereiter-Hahn, und dem Geschäftsführer Dr. Otmar Schöller einige Fragen gestellt.
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.
Background: The oral administration of the gum resin extracts of Indian frankincense (Boswellia serrata Roxb. ex Colebr) results in very low plasma concentrations of boswellic acids (BAs), being far below the pharmacologically active concentrations required in vitro for anti-inflammatory activity. For that reason the use of Indian frankincense in clinical practice and pharmaceutical development has substantially lagged behind. Recently the application of new formulation technologies resulted in a formulation of frankincense extract with lecithin, which revealed improved absorption and tissue penetration of BAs in a rodent study, leading for the first time to plasma concentrations of BAs in the range of their anti-inflammatory activity.
Purpose: In order to verify these encouraging results in humans, the absorption of a standardized Boswellia serrata extract (BE) and its lecithin formulation (CSP) was comparatively investigated in healthy volunteers.
Study design: According to a randomized cross-over design with two treatments, two sequences and two periods, 12 volunteers alternatively received the lecithin-formulated Boswellia extract (CSP) or the non-formulated Boswellia extract (BE) at a dosage of 2 × 250 mg capsules.
Methods: The plasma concentrations of the six major BAs (KBA, AKBA, βBA, αBA, AβBA, AαBA) were determined using LC/MS.
Results: With the exception of KBA, a significantly higher (both in terms of weight-to-weight and molar comparison) and quicker absorption of BAs from the lecithin formulation was observed, leading to Cmax in the range required for the interaction with their molecular targets.
Conclusion: These findings pave the way to further studies evaluating the clinical potential of BAs, and verify the beneficial effect of lecithin formulation to improve the absorption of poorly soluble phytochemicals.
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...
Background: Current approved drugs for Alzheimer’s disease (AD) only attenuate symptoms, but do not cure the disease. The pirinixic acid derivate MH84 has been characterized as a dual gamma-secretase/proliferator activated receptor gamma (PPARγ) modulator in vitro. Pharmacokinetic studies in mice showed that MH84 is bioavailable after oral administration and reaches the brain. We recently demonstrated that MH84 improved mitochondrial dysfunction in a cellular model of AD. In the present study, we extended the pharmacological characterization of MH84 to 3-month-old Thy-1 AβPPSL mice (harboring the Swedish and London mutation in human amyloid precursor protein (APP)) which are characterized by enhanced AβPP processing and cerebral mitochondrial dysfunction, representing a mouse model of early AD.
Methods: Three-month-old Thy-1 AβPPSL mice received 12 mg/kg b.w. MH84 by oral gavage once a day for 21 days. Mitochondrial respiration was analyzed in isolated brain mitochondria, and mitochondrial membrane potential and ATP levels were determined in dissociated brain cells. Citrate synthase (CS) activity was determined in brain tissues and MitoTracker Green fluorescence was measured in HEK293-AβPPwt and HEK293-AβPPsw cells. Soluble Aβ1–40 and Aβ1–42 levels were determined using ELISA. Western blot analysis and qRT-PCR were used to measure protein and mRNA levels, respectively.
Results: MH84 reduced cerebral levels of the β-secretase-related C99 peptide and of Aβ40 levels. Mitochondrial dysfunction was ameliorated by restoring complex IV (cytochrome-c oxidase) respiration, mitochondrial membrane potential, and levels of ATP. Induction of PPARγ coactivator-1α (PGC-1α) mRNA and protein expression was identified as a possible mode of action that leads to increased mitochondrial mass as indicated by enhanced CS activity, OXPHOS levels, and MitoTracker Green fluorescence.
Conclusions: MH84 modulates β-secretase processing of APP and improves mitochondrial dysfunction by a PGC-1α-dependent mechanism. Thus, MH84 seems to be a new promising therapeutic agent with approved in-vivo activity for the treatment of AD.