GRIDs in Drug Discovery and Knowledge Management Dr. Olivier Schwartz / Science Photo Library Manuel C. Peitsch Novartis

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”


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


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 ?


Systems BiologyStudy and Understand Biological Networks / “GRIDs ;-)”

5

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...

“Omics”

Experiments

Mathematical

Models


Influencing Biomolecular Processes

Target

Drug

Target = enzyme, receptor, nucleic acid, …Ligand = substrate, hormone, other messenger, ...

Target

ACTIVE

Ligand

INACTIVE


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


Virtual Screening by in silico Docking

> 400,000 Compounds

DockingProcess

andSelection

ofpossible

hits

< 10 Compound

s


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.”


Peru

In silico DD for Dengue ( Talk by M. Posdvinec)


IsolateIsolate

Proteome Informatics (Talk by P. Hernandez)

ExtractExtract

DigestDigest

Trypsin

[KR]|{P}

HPLC

AcCN AcOH

SeparateSeparate

LC column

+++

m/z

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Knowledge GRIDs Data and Information complexity

Raw data from instruments

Literature

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Mass (m/z)

% I

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1500 2200 2900 3600 4300 5000

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3876

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2738

.9

2324

.7

2495

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3832

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4174

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Genomics and Proteomics

Molecular StructureAnatom

y & Clinical

Pathways


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.


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.


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

…


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


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


MouseOver

Click

Color coding according to concept type.In this example:

Yellow = Gene Name; Red = Institution


BLAST Interface



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

Documents

GRIDs in Drug Discovery and Knowledge Management Dr. Olivier Schwartz / Science Photo Library Manuel C. Peitsch Novartis