GRIDs in Drug Discovery and Knowledge Management Dr. Olivier Schwartz / Science Photo Library Manuel...
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Transcript of GRIDs in Drug Discovery and Knowledge Management Dr. Olivier Schwartz / Science Photo Library Manuel...
GRIDs in Drug Discoveryand Knowledge Management
Dr.
Oliv
ier
Sch
wart
z /
Sci
en
ce Ph
oto
Lib
rary
Manuel C. PeitschNovartis
EGEE‘06 / M. Peitsch / Sept, 2006
Mechanism-based Drug Discovery
Understanding Disease
Pathways elucidation
Target validation
Clinical PoC
New candidate drug with maximised therapeutic window.
The Challenges in Drug Discovery
Systems Biology: Combination of *Omics & Mathematical Modelling
“Drug Discovery suffers from a high attrition rate as many candidates
prove ineffective or toxic in the clinic, owing to a poor understanding
of the diseases, and thus the biological systems, they target”
EGEE‘06 / M. Peitsch / Sept, 2006
In Silico Drug Discovery Pipeline
Fully Leverage in silico sciences
Targetfinding
Targetvalidation
Leadfinding
Leadoptim.
Bioinformatics LabMacromolecular
Structure & Function LabComputationalChemistry Lab
Text Informatics
Comparative Genomics
Protein Modeling
HT Docking
In silico Profiling
In silico Combichem
EGEE‘06 / M. Peitsch / Sept, 2006
3D-Crunch
In Silico Drug Discovery Pipeline: Can it be done?
ProductiveAutomated Protein
modelling email server
ProductiveAutomated Protein
modelling Web server
Genome scale Automated Protein modelling
SETI@Home
1990 1995 2000 2005
Protein Model Structure database
SETI@Home recognised as a leading new concept (ComputerWorld Award)
SWISS-MODEL and 3D-Crunch recognised as a leading new concept (ComputerWorld Award)
GeneCrunch
GeneCrunch recognised as a leading new concept (ComputerWorld Award)
First PC-GRID at Novartis
Docking in productionat Novartis
Automated ToxCheck and other CIx tools
Full TranscriptomeModelling at Novartis
UD recognised for visionary use of information technology in the category of Medicine (ComputerWorld Award)
First semi-automated
In Silico Drug DiscoveryPipeline ?
EGEE‘06 / M. Peitsch / Sept, 2006
Systems BiologyStudy and Understand Biological Networks / “GRIDs ;-)”
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“Omics”
Experiments
Mathematical
Models
EGEE‘06 / M. Peitsch / Sept, 2006
Influencing Biomolecular Processes
Target
Drug
Target = enzyme, receptor, nucleic acid, …Ligand = substrate, hormone, other messenger, ...
Target
ACTIVE
Ligand
INACTIVE
EGEE‘06 / M. Peitsch / Sept, 2006
Our 1st PC Grid Success Story: Protein Kinase CK2 Inhibition
Target finding:
Protein Kinase CK2 has roles in cell growth, proliferation and survival.
Protein Kinase CK2 has a possible role cancer and its over expression has been associated with lymphoma.
Target validation:
To elucidate the different functions and roles of CK2 and confirm it as a drug target for oncology, one needs a potent and selective inhibitor.
Approach:
The problem was addressed by in silico screening (docking).
Ste
ve D
sch
meis
sner
/ S
cien
ce Ph
oto
Lib
rary
EGEE‘06 / M. Peitsch / Sept, 2006
Virtual Screening by in silico Docking
> 400,000 Compounds
DockingProcess
andSelection
ofpossible
hits
< 10 Compound
s
EGEE‘06 / M. Peitsch / Sept, 2006
Important results
ConclusionWe have identified a 7-substitued Indoloquinazoline compound as a novel inhibitor of protein kinase CK2 by virtual screening of 400 000 compounds, of which a dozen were selected for actual testing in a biochemical assay. The compound inhibits the enzymatic activity of CK2 with an IC50 value of 80 nM, making it the mostpotent inhibitor of this enzyme ever reported. Its high potency, associated with high selectivity, provides a valuable tool for the study of the biological function of CK2.
“The reported work clearly shows that large database docking in conjunction with appropriate scoring and filtering processes can be useful in medicinal chemistry. This approach has reached a maturation stage where it can start contributing to the lead finding process. At the time of this study, nearly one month was necessary to complete such a docking experiment in our laboratory settings. The Grid computing architecture recently developed by United Devices allows us to now perform the same task in less than five working days using the power of hundreds of desktop PC’s. High-throughput docking has therefore acquired the status of a routine screening technique.”
“The reported work clearly shows that large database docking in conjunction with appropriate scoring and filtering processes can be useful in medicinal chemistry. This approach has reached a maturation stage where it can start contributing to the lead finding process. At the time of this study, nearly one month was necessary to complete such a docking experiment in our laboratory settings. The Grid computing architecture recently developed by United Devices allows us to now perform the same task in less than five working days using the power of hundreds of desktop PC’s. High-throughput docking has therefore acquired the status of a routine screening technique.”
EGEE‘06 / M. Peitsch / Sept, 2006
Peru
In silico DD for Dengue ( Talk by M. Posdvinec)
EGEE‘06 / M. Peitsch / Sept, 2006
IsolateIsolate
Proteome Informatics (Talk by P. Hernandez)
ExtractExtract
DigestDigest
Trypsin
[KR]|{P}
HPLC
AcCN AcOH
SeparateSeparate
LC column
+++
m/z
Rel
ativ
e I
nte
nsi
tät Slid
e f
rom
M. Pod
vin
ec
EGEE‘06 / M. Peitsch / Sept, 2006
Knowledge GRIDs Data and Information complexity
Raw data from instruments
Literature
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1
Mass (m/z)
% I
nte
ns
ity
1500 2200 2900 3600 4300 5000
50
100
3876
.3
2738
.9
2324
.7
2495
.6
3832
.1
4174
.9
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4503
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1911
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b27
b42 - D
b30
b38
y39 -D9
y11
y27
y33
y18 [M+H]+
y35
b39-D
b28-D (y26)
y24 -Db24-D (y22)
y20 -D
b23
b45 - D
Genomics and Proteomics
Molecular StructureAnatom
y & Clinical
Pathways
EGEE‘06 / M. Peitsch / Sept, 2006
Connecting the Knowledge Bodies (requirements)
Intelligent integration of heterogeneous data to enable “Seamless Navigation”:
One-stop shop.
Re-useable, in any Web and Office application.
Intelligent, i.e. knows about biology, medicine, chemistry, diseases, business, people, etc…
On demand and easy to use.
Configurable.
EGEE‘06 / M. Peitsch / Sept, 2006
Connecting the Knowledge Bodies (Components)
Indexing of large heterogeneous data collections (databases, full texts) to enable semantic expansion.
Information Retrieval and Extraction, entity recognition, semantic enrichment.
Knowledge Map (navigating the conceptual network).
Terminology Hub (thesauri and ontologies).
Ontology-associated business rules.
EGEE‘06 / M. Peitsch / Sept, 2006
What entities constitute our Terminology?
Chemical entities – IUPAC names, trivial names, trade names, INNs, compound codes, ligands.
Biological entities – targets, genes/protein, modes of actions…
Diseases, Indications, Side Effects, Contraindications
Institutions, Affiliations, People
Geographic locations
…
EGEE‘06 / M. Peitsch / Sept, 2006
The Ultralink is an “intelligent” context-sensitive Hyperlink created at run time.
The Ultralink is a menu of links instead of a single link.
This menu will only offers sensible actions/options based on a set of rules attached to an ontology.
The UltraLink allows the dynamic inter-connection of any piece of text or information with any database, search engine and application in the Knowledge Space.
The UltraLink enables seamless information Navigation
The Ultralink: Contextual Hyperlinking
EGEE‘06 / M. Peitsch / Sept, 2006
The Ultralink can be called from many applications:e.g. Internet Explorer
Internet Explorer IntegrationGPS Add-in
Internet Explorer IntegrationGPS Add-in
Web Page Tagged Document
2
Sends the document for
analysis
3
Gets back tagged parts
1
User requests for analysis
4
Injection of specific HTML
tags
Web
Serv
ice (
WS
DL)
Web
Serv
ice (
WS
DL)
GPS Lexical Analysis Server ToolsGPS Lexical Analysis Server Tools
TerminologyTerminology
Lexical ExtractionLexical Extraction
ZoningZoning
TaggingTagging
DocStructuresDocStructures
Meta-RulesMeta-Rules
EGEE‘06 / M. Peitsch / Sept, 2006
MouseOver
Click
Color coding according to concept type.In this example:
Yellow = Gene Name; Red = Institution
EGEE‘06 / M. Peitsch / Sept, 2006
BLAST Interface
EGEE‘06 / M. Peitsch / Sept, 2006
EGEE‘06 / M. Peitsch / Sept, 2006
Acknowledgements
University of Basel:
Torsten Schwede
Michael Podvinec
Jürgen Kopp
Rainer Pöhlmann
Konstantin Arnold
Dominique Zosso
Vital-IT:
Victor Jongeneel
Bruno Nyffeler
Heinz Stockinger
CSCS
Marie-Christine Sawley
Peter Kunszt
Sergio Maffioletti
Arthur Thomas
Novartis
Thérèse Vachon
Martin Romacker
Olivier Kreim
Uwe Plikat
Pierre Parisot
Nicolas Grandjean
Brigitte Charpiot
Jean-Marc von Allmen
Daniel Cronenberger
Eric Vangrevelinghe
Pascal Afflard, Armin Widmer
Christian Bartels & Said Karfane
Jan van Oostrum & Team
Carolyn Cho & Team