Gisbert Schneider

ETH Zurich, Zürich, Zurich, Switzerland

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Publications (229)944.89 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The concept of dual PPARα/γ activation was originally proposed as a new approach for the treatment of the metabolic syndrome. However, recent results indicated that PPARα as well as PPARγ activation might also be beneficial in the treatment of inflammatory diseases and cancer. We have recently identified aminothiazole-featured pirinixic acids as dual 5-lipoxygenase (5-LO) and microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors. Here we present the structure-activity relationship of these aminothiazole-featured pirinixic acids as dual PPARα/γ agonists and discuss their advantages with their potential as dual 5-LO/mPGES-1 inhibitors in inflammatory and cancer diseases. Various pirinixic acid derivatives had already been identified as dual PPARα/γ agonists. However, within this series of aminothiazole-featured pirinixic acids we were able to identify the most potent selective PPARγ agonistic pirinixic acid derivative (compound 13, (2-[(4-chloro-6-{[4-(naphthalen-2-yl)-1,3-thiazol-2-yl]amino}pyrimidin-2-yl)sulfanyl]octanoic acid)). Therefore, docking of 13 on PPARγ was performed to determine the potential binding mode.
    Bioorganic & medicinal chemistry letters. 07/2014;
  • Angewandte Chemie 07/2014; 126(27).
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    ABSTRACT: Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative “homology” model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.
    Chemical Science 06/2014; · 8.31 Impact Factor
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    ABSTRACT: The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant-garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non-homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non-mevalonate pathway of isoprenoid biosynthesis, and multi-kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand- and structure-based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.
    Angewandte Chemie International Edition in English 06/2014; · 13.45 Impact Factor
  • Gisbert Schneider
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    ABSTRACT: The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced “polypharmacology” prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.
    Molecular Informatics. 05/2014;
  • Tiago Rodrigues, Petra Schneider, Gisbert Schneider
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    ABSTRACT: Mikrofluidiksysteme werden für viele Anwendungen im Bereich der chemischen Forschung und Entwicklung eingesetzt, einschließlich der Miniaturisierung von (bio)organischen Synthese- und (Bio)analysemethoden. Gegenwärtig beobachten wir den stetig wachsenden Einsatz von Mikrofluidikverfahren bei der Erforschung neuer chemischer Substanzen. Diese neuen Techniken haben bereits einen spürbaren Einfluss auf die chemische Biologie und molekulare Medizin. In diesem Kurzaufsatz beschreiben wir den aktuellen Stand der Forschung und stellen die jüngsten Fortschritte für die Anwendung von Mikrochip-Reaktoren sowie kleinen und mittelgroßen Coil-Reaktoren in der Synthese bioaktiver Substanzen vor und geben einen Ausblick auf mögliche künftige Anwendungen dieser vielversprechenden Technologie.
    Angewandte Chemie 05/2014;
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    Tiago Rodrigues, Petra Schneider, Gisbert Schneider
    Angewandte Chemie International Edition 05/2014; · 11.34 Impact Factor
  • Tiago Rodrigues, Petra Schneider, Gisbert Schneider
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    ABSTRACT: Flow systems have been successfully utilized for a wide variety of applications in chemical research and development, including the miniaturization of (bio)analytical methods and synthetic (bio)organic chemistry. Currently, we are witnessing the growing use of microfluidic technologies for the discovery of new chemical entities. As a consequence, chemical biology and molecular medicine research are being reshaped by this technique. In this Minireview we portray the state-of-the-art, including the most recent advances in the application of microchip reactors as well as the micro- and mesoscale coil reactor-assisted synthesis of bioactive small molecules, and forecast the potential future use of this promising technology.
    Angewandte Chemie International Edition 05/2014; · 11.34 Impact Factor
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    ABSTRACT: The causative agents of the parasitic disease human African trypanosomiasis belong to the family of trypanosomatids. These parasitic protozoa exhibit a unique thiol redox metabolism that is based on the flavoenzyme trypanothione reductase (TR). TR was identified as a potential drug target and features a large active site that allows a multitude of possible ligand orientations, which renders rational structure-based inhibitor design highly challenging. Herein we describe the synthesis, binding properties, and kinetic analysis of a new series of small-molecule inhibitors of TR. The conjunction of biological activities, mutation studies, and virtual ligand docking simulations led to the prediction of a binding mode that was confirmed by crystal structure analysis. The crystal structures revealed that the ligands bind to the hydrophobic wall of the so-called “mepacrine binding site”. The binding conformation and potency of the inhibitors varied for TR from Trypanosoma brucei and T. cruzi.
    ChemMedChem 04/2014; · 2.84 Impact Factor
  • Tiago Rodrigues, Gisbert Schneider
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    ABSTRACT: Review: 52 refs.+subrefs.
    ChemInform 04/2014; 45(15).
  • ChemInform 03/2014; 45(12).
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    ABSTRACT: Nuclear farnesoid X receptor (FXR) has important physiological roles in various metabolic pathways including bile acid, cholesterol and glucose homeostasis. The clinical use of known synthetic non-steroidal FXR ligands is restricted due to toxicity or poor bioavailability. Here we report the development, synthesis, in vitro activity and structure-activity relationship (SAR) of anthranilic acid derivatives as novel FXR ligands. Starting from a virtual screening hit we optimized the scaffold to a series of potent partial FXR agonists with appealing drug-like properties. The most potent derivative exhibited an EC50 value of 1.5±0.2μM and 37±2% maximum relative FXR activation. We investigated its SAR regarding polar interactions with the receptor by generating derivatives and computational docking.
    Bioorganic & medicinal chemistry 03/2014; · 2.82 Impact Factor
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    ABSTRACT: Wir präsentieren die Entwicklung und Anwendung eines computergestützten Verfahrens für das De-novo-Design von bioaktiven niedermolekularen Substanzen mit gewünschten Bindungsprofilen. Der Ansatz überträgt das aus der Natur inspirierte Konzept der Ameisenkolonie-Optimierung auf die Auswahl von kombinatorischen Synthesebausteinen. Basierend auf öffentlich zugänglichen Struktur-Aktivitäts-Daten haben wir ein quantitatives Polypharmakologie-Vorhersagemodell für 640 humane Wirkstofftargets entwickelt. Am Beispiel der reduktiven Aminierung zeigen wir den Entwurf von sowohl selektiven als auch Multi-Target-modulierenden Dopamin-D4-Antagonisten sowie Sigma-1-Rezeptor-selektiven Liganden mit genau vorausgesagten Affinitäten. Die nanomolaren Potenzen der erzielten Treffer, ihre hohe Ligandeneffizienz und eine Erfolgsquote von 90 % zeigen, dass die computerbasierte Moleküldesignmethode für die schnelle Entwicklung fokussierter kombinatorische Substanzbibliotheken geeignet ist.
    Angewandte Chemie 03/2014;
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    ABSTRACT: We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure–activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.
    Angewandte Chemie International Edition 03/2014; · 11.34 Impact Factor
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    ABSTRACT: De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibrate-related compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.
    Proceedings of the National Academy of Sciences 03/2014; · 9.74 Impact Factor
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    ABSTRACT: Background: Prioritizing building blocks for combinatorial medicinal chemistry represents an optimization task. We present the application of an artificial ant colony algorithm to combinatorial molecular design (Molecular Ant Algorithm [MAntA]). Results: In a retrospective evaluation, the ant algorithm performed favorably compared with other stochastic optimization methods. Application of MAntA to peptide design resulted in new octapeptides exhibiting substantial binding to mouse MHC-I (H-2K(b)). In a second study, MAntA generated a new functional factor Xa inhibitor by Ugi-type three-component reaction. Conclusion: This proof-of-concept study validates artificial ant systems as innovative computational tools for efficient building block prioritization in combinatorial chemistry. Focused activity-enriched compound collections are obtained without the need for exhaustive product enumeration.
    Future medicinal chemistry 03/2014; 6(3):267-80. · 3.31 Impact Factor
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    ABSTRACT: We present the discovery of low molecular weight inhibitors of human immunodeficiency virus 1 (HIV-1) protease subtype B that were identified by structure-based virtual screening as ligands of an allosteric surface cavity. For pocket identification and prioritization, we performed a molecular dynamics simulation and observed several flexible, partially transient surface cavities. For one of these presumable ligand-binding pockets that are located in the so-called "hinge region" of the identical protease chains, we computed a receptor-derived pharmacophore model, with which we retrieved fragment-like inhibitors from a screening compound pool. The most potent hit inhibited protease activity in vitro in a noncompetitive mode of action. Although attempts failed to crystallize this ligand bound to the enzyme, the study provides proof-of-concept for identifying innovative tool compounds for chemical biology by addressing flexible protein models with receptor pocket-derived pharmacophore screening.
    Journal of Chemical Information and Modeling 02/2014; · 4.30 Impact Factor
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    ABSTRACT: Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures.
    PLoS Computational Biology 01/2014; 10(1):e1003400. · 4.87 Impact Factor
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    ABSTRACT: Am Beispiel der Ugi‐Dreikomponentenreaktion stellen wir ein schnelles und effizientes Verfahren für die Kopplung von On‐Chip‐Mikroflusssynthesen mit einer neuen Methode zur Vorhersage biologischer Targets vor, um neue Liganden‐Protein‐Beziehungen zu entdecken. Wir konnten auf diese Weise eine GPCR‐modulierende, kombinatorisch zugängliche Verbindungsklasse identifizieren. Diese effizienten Liganden binden mit antagonistischen Eigenschaften an die humanen Adenosin‐A1/2B‐ und adrenergen α1A/B‐Rezeptoren. Die Integration von Mikrofluidiksystemen für die chemische Synthese mit computergestützten Targetvorhersagen ist ein vielversprechendes Verfahren zur raschen Erstellung fokussierter Substanzbibliotheken mit hohen Trefferraten.
    Angewandte Chemie 01/2014; 126(2).
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    Tiago Rodrigues, Gisbert Schneider
    Synlett 12/2013; · 2.66 Impact Factor

Publication Stats

3k Citations
944.89 Total Impact Points

Institutions

  • 2010–2014
    • ETH Zurich
      • • Department of Chemistry and Applied Biosciences
      • • Institute of Pharmaceutical Sciences
      Zürich, Zurich, Switzerland
    • Helmholtz Zentrum München
      München, Bavaria, Germany
    • Paul-Ehrlich-Institut
      Langen, Hesse, Germany
  • 2012
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
    • University of Salzburg
      • Division of Microbiology
      Salzburg, Salzburg, Austria
  • 2011–2012
    • Novartis Institutes for BioMedical Research
      Cambridge, Massachusetts, United States
    • Justus-Liebig-Universität Gießen
      Gieben, Hesse, Germany
    • University of British Columbia - Vancouver
      Vancouver, British Columbia, Canada
  • 2008–2012
    • University of Tuebingen
      Tübingen, Baden-Württemberg, Germany
    • Columbia University
      New York City, New York, United States
  • 2005–2011
    • University Hospital Frankfurt
      Frankfurt, Hesse, Germany
    • Heinrich-Heine-Universität Düsseldorf
      • Institute of Neuropathology
      Düsseldorf, North Rhine-Westphalia, Germany
  • 2003–2011
    • Goethe-Universität Frankfurt am Main
      • Institut für Organische Chemie und Chemische Biologie
      Frankfurt am Main, Hesse, Germany
  • 2002
    • Roche
      • Pharmaceuticals Division
      Basel, BS, Switzerland
  • 1993–1998
    • Freie Universität Berlin
      Berlín, Berlin, Germany