bio it worldexpoeurope2012_shublaq
DESCRIPTION
Bio/Healthcare-IT Research Priorities in EuropeTRANSCRIPT
Towards Personal Health Records, Transla3onal Research, and a Truly IT Revolu3on of Medicine
Nour Shublaq, PhD Centre for Computa-onal Science University College London, UK
From Drug Discovery Informatics to Personalised Therapeutics – Oct 2012, Vienna
Overview
• Why Personalised Medicine?
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Study – towards personalised drug design
• Infrastructure suppor-ng drug discovery – Improving the odds of the Medical LoMery
• Conclusions
Human Genome Project
Sequencing of the human genome was profoundly important science that led to fundamental shifts in our understanding of biology.
30,000 – 40,000 protein coding genes in the human genome and not more than 100,000 previously thought.
Thousands of DNA variants have now been associated with traits/diseases.
Human Genome Project, International HapMap Project, and Genome wide association studies (GWAS) in the last decade
Structure Mol. Profiles Genomic
2
10
3000 30,000
4
New Sequencers 1 Human Genome in: 5 years (2001) 2 years (2004) 4 days (Jan 2008) 16 Hours (Oct 2008) 3 Hours (Nov 2009) 6 minutes (Now!)
Cost of whole genome sequencing expected to drop to $100 in a few years
hMp://www.inbiomedvision.eu
Challenges ahead
Biological challenges – Do we understand biology and
diseases enough to develop reliable computa-onal models?
– How to integrate growing knowledge into models?
ICT Challenges – Data quality – Data management – Data security – User interfaces
Societal challenges – Privacy – How to prevent inequali-es in
access to health care? – Health care economics – Implementa-on in health care – How to prevent adverse
effects/misuse?
Genotype-phenotype resources
Molecular-level models (GWAS, PPI, …)
Translational Systems Biology
Clinical phenotypes (EHR, multi-scale
physiological models…)
Exposome (drugs, diet, environmental
chemicals,…)
Developments
1
2
3
System-level models (organ networks,…)
Text m
ining
& sem
antic
web
Complex disease networks
Pharmacogenomics
Disease susceptibility
Disease gene Oinding
Phenotypic variation
Nour Shublaq et al. (2012) – under review
• Why Personalised Medicine?
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Study – towards personalised drug design
• Infrastructure suppor-ng drug discovery – Improving the odds of the Medical LoMery
• Conclusions
• The Virtual Physiological Human is a methodological and technological descriptive, integrative and predictive, framework that is intended to enable the investigation of the human body as a single complex system
• Aims • Enable collaborative
investigation of the human body across all relevant scales
• Introduce multiscale methodologies into medical and clinical research
Organism Organ Tissue
Cell Organelle Interaction
Protein Cell
Signals Transcript
Gene Molecule
€207M initiative in EU-FP7
What is the VPH?
Tissue Osteon Nephron Acinus Liver lobule Lymph node Cardiac sheets
Organ
Heart Lungs Diaphragm Colon Eye Knee Liver
Environment
Organ system Organism
Cell
x 1million 20 generations
The challenge: organs to proteins
→ Medical informatics → Personalised medicine
Protein Gene Atom
Network
• Why Personalised Medicine?
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Study – towards personalised drug design
• Infrastructure suppor-ng drug discovery – Improving the odds of the Medical LoMery
• Conclusions
Assessment of the binding of small molecules to proteins key to both drug discovery and treatment selec-on
Techniques applicable to one area can also be used in another
Quan-fying drug – protein binding strength requires atomis-cally detailed models
Time to comple-on key in both drug discovery and clinical applica-ons
Drug Selec3on and Drug Design
Chem Biol Drug Des special theme, Jan 2013 Epub Jul 2012
WIREs Syst Biol Med, Aug 2012
HIV-‐1 Protease is a common target for HIV drug therapy
• Enzyme of HIV responsible for protein matura-on
• Target for An--‐retroviral Inhibitors • Example of Structure Assisted Drug
Design • 9 FDA inhibitors of HIV-‐1 protease
So what’s the problem? • Emergence of drug resistant
muta-ons in protease • Render drug ineffec-ve • Drug resistant mutants have emerged
for all FDA inhibitors
Monomer B 101 - 199
Monomer A 1 - 99
Flaps
Leucine - 90, 190
Glycine - 48, 148
Catalytic Aspartic Acids - 25, 125
Saquinavir
P2 Subsite
N-terminal C-terminal
EU FP6 ViroLab project and EU FP7 CHAIN project
Pa3ent-‐specific HIV Drug Therapy
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Clinical SeSng – HIV drug ranking
Too many muta-ons to interpret by a clinician
Support so]ware is used to interpret genotypic assays from pa-ents
Uses both in vivo and in vitro data
Is dependent on Size and accuracy of in vivo clinical data set
Amount of in vitro phenotypic informa-on available -‐ e.g. binding affinity data
Patient sequence for which existing clinical decision support tools provide differing resistance assessments
Simulator for Personalised Drug Ranking BAC Simulator: a decision support software to assist clinicians for cancer treatment, and to reliably predicts patient-specific drug susceptibility.
Variant of target from patient
Array of available drugs
BAC Simulator
Ranking of drug binding
The system could be used to rank proteins of different sequence with the same drug
Rapid and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. Stoica I, Sadiq SK, Coveney PV. J Am Chem Soc. 2008 Feb 27;130(8):2639-48. Epub 2008 Jan 29.
High Throughput Automa3on
• Needs a grid or grid-of-grids
• We calculate “many” binding affinities rapidly
• Do not need to manually launch each simulation
Technological environment accesses worldwide Grid resources
HIV-‐1 Protease: Mul3ple Drug Resistance
• Simulate 5 clinically relevant variants bound to inhibitor lopinavir
• Reproduce experimental* binding affinity ranking
• Require mul-ple simula-ons to efficiently explore relevant ensemble of structures
Sadiq et al. J Chem Inf Model 50(5), 890-905 * Ohtaka et al. Biochemistry 2003, 42(46), 13659-13666
HIV-‐1 Protease: Mul3ple Drug Resistance
• Effect of muta-onal combina-ons superaddi-ve
• 50 replica simula-ons performed for each data point
• Results replicated to within 1.3 kcal/mol
EGFR muta3ons for lung cancer
L747-E749 del
A750P
L858R G719S
EGFR Tyrosine Kinase Domain
• Over expression of Epidermal Growth Factor Receptor (EGFR) is associated with cancer
• Target for inhibitory drugs • Important muta3ons include dele3ons
• Again binding affinity calcula3ons can be used to determine muta3onal effects
• Why Personalised Medicine?
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Study – towards personalised drug design
• Infrastructure suppor-ng drug discovery – Improving the odds of the Medical LoMery
• Conclusions
E-‐infrastructure
-‐ Collec3on of pa3ent data & storage -‐ Access to high performance
compu3ng infrastructure to perform
drug response simula3ons based on
the characteris3cs of an individual
IMENSE: Individualised Medicine Simula3on Environment • Central integrated repository of pa-ent data for project clinicians &
researchers
– Storage of and audit trail of computa-onal results – Interfaces for data collec-on, edi-ng and display – Provides a data environment for integra-on of mul--‐scale data &
decision support environment for clinicians
• Cri-cal factors for Success and longevity – Use Standards and Open Source solu-ons – Use pre-‐exis-ng EU FP6/FP7 solu-ons and interac-on with VPH-‐
NoE Toolkit
S. J. Zasada et al., “IMENSE: An e-Infrastructure Environment for Patient Specific Multiscale Modelling and Treatment, Journal of Computational Science, In Press, Available online 26 July 2011, ISSN 1877-7503, DOI: 10.1016/j.jocs.2011.07.001.
P-‐Medicine
• Predic-ve disease modelling • Exploi-ng the individual data of the pa-ent • Op-miza-on of cancer treatment (Wilms tumor, breast cancer and
acute lymphoblas-c leukemia) • Scalable for any disease, as long as:
– predic-ve modeling is clinically significant in one or more levels
– development of such models is feasible
Disease Modelling at the molecular Level
Disease Modelling at the cellular Level N
S G 1 G
2 M G 0
A Disease Modelling at the tissue/organ Level
Multi-scale therapy predictions/disease evolution results
Led by a clinical oncologist -‐ Prof Norbert Graf! €13M, 2011-‐2013, EU FP7
VPH-‐Share
VPH-‐Share will provide the organisa>onal fabric realised as a series of services, offered in an integrated framework, to expose and to manage data, informa>on and tools, to
enable the composi>on and opera>on of new VPH workflows and to facilitate collabora>ons between the members of the VPH community.
HIV Heart Aneurisms Musculoskeletal
€11M, 2011-‐2015, EU FP7 – Promotes cloud technologies
• Exploit unprecedented amounts of detailed biological data being accumulated for individual people
• Harness the latest developments in ICT
– large scale data integra-on and mining, cloud compu-ng, high performance compu-ng, advanced modelling and simula-on,
– all brought together in a highly flexible plakorm.
• Turn this informa-on into knowledge that assists in taking medical, clinical and lifestyle decisions
IT Future of Medicine Up to €1B EU Future Emerging Technologies Flagship proposal
hMp://www.ikom.eu
Health care & society
User needs
Personalised medicine Public health
ITFoM Industry
ICT &
Biotech Pharma
Computational models of
biological systems: cells
organs individuals populations In
nova
tion
Virtual patient
Better drugs, disease prevention, evidence-based decision-making
Use Case: Cancer Treatment
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Drug treatment recommendation
Genome and Transcriptome sequencing
Tumor sampling Tumor stem cell extraction/expansion
Modeling Drug Response
The Cancer Model
X
X
X
Patient Specific Model
Drug Database
Mutation Database
ICT Layers of ITFoM
Rela3on to EU Infrastructures
Integrated Structural Biology Infrastructure
European Sequencing and Genotyping Infrastructure
ISBE
Infrastructure for Systems Biology – Europe
Partnership for Advanced Computing in Europe
33
Supported by the Provost's Strategic Fund
Computational Life and Medical Science
Management: Dean’s Committee
Steering Committee
UCL Computa3onal Life and Medical Sciences (CLMS) Network
UCL Partners: 14 NHS Trusts and affiliated healthcare institutes/clinics
hMp://www.clms.ucl.ac.uk
1. Maintain and expand UCL’s world-leading position in life and biomedical sciences
2. Improve collaboration with academic institutions: within UCL, with UCLP and the NHS, Francis Crick Institute, Yale, and others
3. Take advantage of new initiatives in integrative biomedical systems science from the UK Research Council, EU and others around the world
4. Improve collaboration with industry, create business and commercial opportunities, promote UCL IP licensing
5. Plan for the next stages of activity in computational life and medical sciences at UCL
CLMS Goals
• Why Personalised Medicine?
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Study – towards personalised drug design
• Infrastructure suppor-ng drug discovery – Improving the odds of the Medical LoMery
• Conclusions
• Medicine today is a driver of ICT innova-on and vice versa. Data-‐intensive projects, and more future projects will be. – biomedicine community is starving for storage; – network bandwidth now limi-ng: a faster network is needed for
data movement.
• Advanced IT allows us to analyse pa-ents all the way up from their own DNA sequences
• A personalised approach is expected to lead to improved – health outcomes – drugs/treatments – disease preven-on – evidence-‐based decision-‐making – lifestyle choices for global ci-zens
Conclusions
Thank you for your aden3on! Nour Shublaq, PhD
University College London, UK [email protected]
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