Transcript GRID Computing at Novartis

How and Why Novartis is
Exploiting GRID Technology?
HPC and Semantic Web
Prof. Manuel C. Peitsch, PhD
Global Head of Systems Biology
The Challenges of Drug Discovery
Mechanism-based Drug Discovery
 Understanding Disease
 Pathways elucidation
 Target validation
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Systems Biology:
Combination of *Omics
& Mathematical
Modelling
 Clinical PoC
New drug candidates (to be tested in PoC
studies)
Reduce project life cycle
Increase PoS after D3 (Lead optimisation)
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Organizational complexity
United States
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Great Britain
Diabetes
• Respiratory
Infectious diseases
• Gastrointestinal
Cardiovascular
Oncology
Discovery Techologies
Discovery Chemistry
Animal Models
Pathways
Genome and Proteome Sciences
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Switzerland Austria
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Muscular and Bone • Autoimmunity
Nervous system
Oncology
Transplantation
Ophthalmology
Genome and Proteome Sciences
Discovery Techologies
Discovery Chemistry
Protease Platform
GPCR
Japan
• Oncology
• Diabetes
• Cardiovascular
Data and Information complexity
Molecular Structure
Literature
Raw data from instruments
Genomics and Proteomics
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The Vision
Enable and transform the Drug
Discovery process through:
Data Information and
Knowledge GRID 
Knowledge Space / Semantic
Web
- Comprehensive and reliable Data and
Information
- Seamless information integration for
easy navigation
Computational life
science and HPC
GRIDs
- Turning Data into Knowledge
using in silico science
- Simulate biomolecular processes using
in silico science
- E-Collaboration and v-communities
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People Networks
Computational Aspects in Drug Discovery
Bioinformatics Lab
Target
finding
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Macromolecular
Structure & Function Lab
Target
validation
Lead
finding
Computational
Chemistry Lab
Lead
optim.
Signal Transduction Networks
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In Silico Drug Discovery
SNP Translate &
SAP
Map/Align
Model &
Map
Human data
DB
Structures &
Modelling templates
DB
DB
Disease association
Validated Targets
DB
DB
Functional and
Structural insights
Virtual Drug Discovery
Compounds
In Silico Docking
In Silico “Chemogenomics”
Virtual Library Design QSAR
Predictive MedChem
Tox PK/PK ADME modelling
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Kinases
NR
Proteases
Proteins
DNA Sample
Sequencing
In Silico Drug Discovery Pipeline: Can it be done?
1990
1995
Productive
Automated Protein
modelling email server
2000
2005
First PC-GRID
at Novartis
GeneCrunch
SETI@Home
Productive
Automated Protein
modelling Web server
3D-Crunch
Genome scale Automated
Protein modelling
Protein Model
Structure database
SETI@Home recognised as a leading new concept (ComputerWorld Award)
GeneCrunch recognised as a leading new concept (ComputerWorld Award)
Docking
in production
at Novartis
In Silico Drug
Discovery and
Chemogenomics
pipeline
Full Transcriptome
Modelling at Novartis
First automated pipelines
Automated
ToxCheck and
other CIx tools
SWISS-MODEL and 3D-Crunch recognised as a leading new concept (ComputerWorld Award)
UD recognised for visionary use of information technology in the category of Medicine (ComputerWorld Award)
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Novartis’ HPC Grid Strategy
Shared Servers
Job submission layer
Linux Clusters
PC GRID
External
Collaborations
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Influencing Biomolecular Processes
Target
Ligand
Drug
ACTIVE
INACTIVE
Target = enzyme, receptor, nucleic acid, …
Ligand = substrate, hormone, other messenger, ...
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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).
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Virtual Screening by in silico Docking
> 400,000
Compounds
Docking
Process
and
Selection
of
possible
hits
< 10
Compounds
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Important results
Conclusion
We 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
“The associated
reported work
enzyme ever reported. Its high potency,
with clearly shows that large database
high selectivity, provides a valuable tool
for the study
of the
docking
in conjunction
with appropriate scoring and
biological function of CK2.
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.”
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Major benefits of GRID computing
Optimization of resources utilization:
 HPC platforms usage is maximized and Technology expertise is shared.
 Response to additional performance requirements is easier and faster
 No service downtime due to possibility to run same job on many platforms
across different sites.
Enable cross business units collaboration and synergies:
 Single efficient access path to Data and Compute resources.
 Tools are easily exchanged between scientists/programs.
Favor “out of the box” thinking:
 Apply HPC to areas which one would not even have considered a year ago.
This has created a fertile ground for a new paradigms in Drug Discovery
leading to Business Process transformation.
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Performance of the PC-GRID (today)
Computing Power:
 Theoretical >5 TeraFLOPS harvested from 3000 PCs in all
geographical locations.
 Acceleration of the in silico Docking process versus 1
standard 2002 PC (start of project): ~4000x
Financial:
 Immediate savings in excess of 2m$.
 No need for additional data centre to support this
computing power.
 Optimally use of existing hardware (associates’ PCs)
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Building a GRID: Management focus
You need a champion!
Do not punctuate every sentence with the GRID word
and avoid the Hype!
Demonstrate value through pilots:
 Think “Iterative Improvement”. The conceptual layers are there,
prototype are emerging, improvements and optimization is
essential, maturity will follow
Leadership, transcendence, entrepreneurship and
tenacity are the essence of transformation!
 Concepts are easy to draw on a napkin over beer!
 But new and great things are hard to achieve!
 Use external goodwill to create internal acceptance!
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Peru
Community projects help with acceptance
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Building a GRID: User base
You need a clearly defined and communicated HP
Computing strategy.
 Address unmet computational needs.
 Apply HPC to areas which one would not even have considered
two years ago. This has created a fertile ground for a new
paradigms in Drug Discovery leading to Business Process
transformation.  Are all problems “GRIDable”?
Further applications:
 Sequence identification in proteomics from LC-MS/MS data
 Text Mining and semantic Web infrastructure
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Building a GRID: Software
The Software licensing models will have to evolve
 Do not stop because of software licensing issues.
 Show success with freeware and home grown algorithms.
 Demonstrate business value and cost leadership.
 Opportunity to develop your own code?
Unification of HPC applications environment:
 Ensure that applications can run on maximum number of
systems.
Introduce HPC software management:
 Influence licensing models. The classical models do not fit the
GRID and HPC paradigm.
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Building a GRID: PC owners
Education and awareness.
 Ensure that the HelpDesk is well trained and gives the right
answers.
 Ensure that PC owners know about the REAL impacts,
including network.
The PCs are company and not personal assets!
 Strategy to use them when they are idle is not a user but a
company decision.
 Address power saving policies in a transparent manner.
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Knowledge Space - Vision
The "Knowledge Space Portal” is a Drug Discovery oriented
implementation of the Semantic Web. Through a single
customizable interface it:
• Federates heterogeneous data resources and provide precise
organization of the content
• Provides quick and intuitive access to information
• Provides data extraction, analysis and exploration tools
• Allows data integration, data exchange and interoperability of
applications
• Provides mechanisms for data capture and annotation
• Provides knowledge sharing and collaborative tools
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Basic principles behind the Knowledge Space
The Knowledge Space consists of:
The collection of all types of data and information within the
scope of interest defined by a particular business. There is no
conceptual difference between internal and external
data/information.
The Meta Data and the Knowledge Map which describe the
collection in terms of content and location.
The Text Mining platform which allows the identification of
entities (using vocabularies) and the concepts they belong to
using ontologies.
The Ultralinker, which associates identified entities and concepts
with specific contextual rules.
A user interface.
HPTS Asilomar / M. Peitsch / September, 2005
What is an Ultralink?
The Ultralink is an “intelligent” context-sensitive Hyperlink created at run time by the
Ultralinker.
The Ultralink is generally a menu of links instead of a single link.
This menu will only offers sensible actions/options:
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No dead ends due to a verification process ensuring that the link has a target.
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The Ultralink provides direct interaction between any type of entity (gene name,
compound name, mode of action, disease name, company name, etc… with an
appropriate set of tools and resources as defined by the rules encoded in the
Ultralinker.
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The Ultralink functionality allows the selection of any portion of text in the Web
browser and sends it as input to the Ultralinker for analysis and menu creation.
The Ultralink allows easy navigation across the information domains contained in the
Knowledge Space.
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How the Ultralinker works
The Ultralinker is a Web service which analyses any information (such as a complete
web pages) it receives for recognisable entities using text mining and pattern
recognition methods.
Each recognised item is mapped onto the ontologies and the Knowledge Map.
The Expert System will define what can be done with the identified entities e.g.
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If a gene name is recognised then Ultralinks are created to:
 get its sequence and perform sequence similarity searches;
 query genetic disorder databases and map it onto the chromosome;
 produce a 3D structure by comparative modelling;
 look for hits from High Throughput Screening;
 etc…
Automated predefined processes can thus be activated by a single click (Ultraaction or
work-flow).
The Ultralinker will create a menu that will be sent to the User interface.
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What constitutes the Knowledge Space
Literature
Defined
workflows
Ultralinker
Analytics
Thesaurii
Ontologies
Meta Data
K map
Comp. Inf.
Text
Mining
Semantic
Search
Rules
Bioinformatics
Chemistry
Research
Documentation
Other
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Internet
Biology
Knowledge Space Search Modes
Text
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Structure
Concepts
Knowledge Space: Text search
Expansion: EMTREE + Novartis proprietary dictionary  expansion
for protease modulators + respective synonyms
ACE modulator
ACE-related carboxypeptidase modulator
Acrosin modulator
Aggrecanase modulator
Alpha 1 protease modulator
Alpha 1 proteinase inhibitor
Alpha 1 proteinase modulator
Aminopeptidase modulator
Amyloid protease modulator
Antitrypsin modulator
Aspartic protease modulator
Atriopeptidase modulator
Calpain inhibitor
Calpain modulator
Carboxypeptidase modulator
Caspase modulator
Cathepsin B modulator
Cathepsin D modulator
Cathepsin F modulator
Cathepsin G modulator
Cathepsin K modulator
Cathepsin L modulator
Cathepsin modulator
Cathepsin S modulator
Cathepsin V modulator
Cathepsin X modulator
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Cholecystokinin modulator
Chymase modulator
Chymotrypsin modulator
Clipsin modulator
Collagenase modulator
Complement cascade modulator
Complement factor modulator
Cysteine protease modulator
Dipeptidase modulator
Elastase modulator
Endopeptidase modulator
Endothelin converting enzyme modulator
Factor IX modulator
Factor VII modulator
Factor X modulator
Factor XII modulator
Gelatinase modulator
Interleukin 1 converting enzyme modulator
Kallikrein modulator
Metalloprotease 1 modulator
Metalloprotease 11 modulator
Metalloprotease 12 modulator
Metalloprotease 13 modulator
Metalloprotease 2 modulator
Metalloprotease 3 modulator
Metalloprotease 4 modulator
Metalloprotease
Antiviral 7 modulator
Metalloprotease
8 modulator
CMV protease
inhibitor
Metalloprotease
9 modulator
CMV protease
modulator
Metalloprotease
modulator
Hepatitis C protease inhibitor
NAALADase
modulator
Hepatitis
C protease modulator
Pepsin modulator
Herpes simplex virus protease inhibitor
Peptidase
modulator
Herpes
simplex virus protease modulator
Plasmepsin
modulator
HIV protease inhibitor
PlasminHIV
modulator
protease modulator
Protease
inhibitor
HIV-1
protease inhibitor
Protease
stimulant
HIV-1 protease modulator
Proteasome
HIV-2inhibitor
protease inhibitor
Proteasome
HIV-2modulator
protease modulator
Renin modulator
NS3 protease inhibitor
Secretase
modulator
NS3
protease modulator
Serine protease
modulator
Picornavirus
protease inhibitor
Thrombin
modulator
Picornavirus protease modulator
Thrombokinase modulator
Trypsin modulator
Tryptase modulator
Ubiquitin-specific protease inhibitor
Ubiquitin-specific protease modulator
Ubiquitin-specific protease stimulant
Urokinase modulator
Viral protease modulator
Display-Navigation-Ultralink
Protease modulator in Literature DB (Medline-Embase)
Easy navigation in
record titles
Sort capabilities
Ranking value
and access to
document
Analysis tools
Search report:
Number of Docs,
Key-words
extracted
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Document view
Take advantage of the full-text
article provided by PubMed
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Analysis Tools
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Data Analysis
Protease modulators in CI DBs July 2004 - ADIS & Pharmaprojects
Univariate - Companies
Univariate - Diseases
conditionned by Companies
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Univariate - MOA
Clustering Diseases -MOAs
Graph Navigator
Protease modulators in CI DBs July 2004 - ADIS & Pharmaprojects
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Clustering
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Chemistry, Chemoinformatics and Structural
Biology
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