Project

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Computational design of custom-tailored chemokine receptor blockers

English title Computational design of custom-tailored chemokine receptor blockers
Applicant Schneider Gisbert
Number 159737
Funding scheme Interdisciplinary projects
Research institution Institut für Pharmazeutische Wissenschaften ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Information Technology
Start/End 01.09.2015 - 31.08.2018
Approved amount 306'007.00
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All Disciplines (4)

Discipline
Information Technology
Pharmacology, Pharmacy
Biochemistry
Organic Chemistry

Keywords (4)

Computer-assisted drug design; Medicinal chemistry; Chemical Biology; Bioinformatics

Lay Summary (German)

Lead
Die Wirkstoff- und chemisch biologische Forschung wird durch die fortlaufende Entdeckung neuer Substanzen mit gewünschten pharmakologischen Eigenschaften angetrieben. Das Forschungsprojekt leistet einen Beitrag zur Entwicklung konzeptionell neuer Methoden, die das zielgerichtete Finden solcher Molekülstrukturen innerhalb kurzer Zeitspannen ermöglicht. Dabei steht der Einsatz von Computerprogrammen für den Entwurf neuartiger Molekülstrukturen und der Vorhersage von gewünschten und potentiell unerwünschten Effekten im Vordergrund.
Lay summary
Eine wichtige Rolle in der Wirkstofforschung spielen  kleine Moleküle, welche die biologische Aktivität von G-Protein gekoppelten Rezeptoren (GPCR) beeinflussen können. Im geplanten Forschungsprojekt zielen wir auf die Entdeckung neuartiger GPCR-Modulatoren ab, die bestimmte Chemokinrezeptoren aus der Klasse der GPCR zu blockieren, um zielgerichtet immunologische und entzündliche Vorgänge im Organismus beeinflussen zu können. Wir verfolgen einen Design-Synthese-Test Kreislauf, um in möglichst wenigen Zyklen zu den neuen GPCR-Modulatoren zu finden. Dabei kommen neben modernen Synthese- und Testverfahren insbesondere innovative "lernende" Computerverfahren zum Einsatz. Das Projekt bildet damit einen Brückenschlag zwischen experimentellen und theoretischen Ansätzen in den pharmazeutisch-chemischen und biologischen Wissenschaften.
Direct link to Lay Summary Last update: 01.06.2015

Responsible applicant and co-applicants

Employees

Name Institute

Publications

Publication
Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning
Fuchs Jens-Alexander, Grisoni Francesca, Kossenjans Michael, Hiss Jan A., Schneider Gisbert (2018), Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning, in MedChemComm, 9(9), 1538-1546.
Automating drug discovery
Schneider Gisbert (2017), Automating drug discovery, in Nature Reviews Drug Discovery, 17(2), 97-113.
Deep Learning in Drug Discovery
Gawehn Erik, Hiss Jan A., Schneider Gisbert (2016), Deep Learning in Drug Discovery, in Molecular Informatics, 35(1), 3-14.
Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors
Reker D., Schneider P., Schneider G. (2016), Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors, in Chemical Science, 7(6), 3919-3927.

Collaboration

Group / person Country
Types of collaboration
Dr. Martin Baumgartner und Dr. Karthiga Kumar, Universitätskinderspital Zürich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure
Prof. Dr. Cornelia Halin, ETH Zürich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Research Infrastructure
- Exchange of personnel

Abstract

Obtaining new chemical entities (NCEs) with a predictable macromolecular target engagement profile through rational molecular design is seminal for innovation in chemical biology and medicinal chemistry. G-protein coupled receptors (GPCRs) constitute an important class of macromolecular drug targets, for which the close intra-class relationships among GPCRs may easily result in undesired off-target modulation by druglike effector molecules. Under the research program described herein we aim at the multi-objective generation and computationally driven optimization of NCEs for cytokine receptors as promising anti-cancer targets in immunity and inflammation. We specifically target the C-C cytokine receptor 7 (CCR7) and C-X-C chemokine receptors 4 and 7 (CXCR4/7), while displaying negligible engagement of GPCR off-targets. The expected outcome will be first-in-class dual chemokine receptor blockers identified through computational molecular design and optimization. Advanced structure-based (receptor-based) pharmacophore models will aim at orthosteric and allosteric ligand binding sites, and ligand-based virtual screening and reaction-driven molecular de novo design will provide access to innovative chemotypes. By iterating through synthesize-test-learn cycles the computational structure-activity relationship model adapts to the given tasks. Batch and microfluidics-assisted synthesis as well as compound purchasing from a large pool of available screening compounds shall achieve the required experimental throughput. Direct receptor binding measurements will provide information about the binding affinity of the test compounds, and in vitro cell-based testing of the ligand effects on receptor signaling will complement the biophysical compound profiling. The most potent leads will be tested for computationally predicted off-target liabilities. Taking the challenging example of CCR7 and the twin CXCR4/7 receptor system, the study will result in new technology for rapidly generating GPCR-modulating lead molecules with designer polypharmacology and tool compounds for chemical biology and chemogenomics studies.
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