mie2012 27 aug12_shublaq
TRANSCRIPT
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Personalised medicine: A legacy of promises without delivery. Can we get it right today?
Nour Shublaq
Centre for Computa-onal Science (CCS) University College London, UK
MIE 2012 – Process, Information, and Data Models, Monday Aug 27, 2012, Pisa
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Overview
• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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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
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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!)
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Life is the transla-on of the informa-on in the genome into the phenotype of the
organism:
The organism ‚computes‘ this phenotype from its genotype, given a specific environment
(PentiumV) (neuronal net visualisation)
Genome
Phenotype
Organism = Computer Genome & the Environment
Slide Courtesy of Hans Lehrach
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• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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• 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?
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…pa-ent-‐tailored computer models, used for diagnosis, preven-on, drug treatment and surgical planning – assess treatment before administering
Modelling how the human body works
Slide Courtesy of S. Kashif Sadiq
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Environment
Population
dimensional scales
temporal scales Organism
Organ System
Organ
Tissue
Cell
Molecule
Atom
IntegraLon across..
organ systems
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• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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GENIUS: Grid Enabled Neurosurgical Imaging Using SimulaLon
The GENIUS project aims to model large scale pa-ent specific cerebral
blood flow in clinically relevant -me frames
ObjecLves: To study cerebral blood flow using paLent-‐specific image-‐based models To provide insights into the cerebral blood flow & anomalies To develop tools and policies by means of which users can be[er exploit the ability to reserve and co-‐reserve HPC resources To develop interfaces which permit users to easily deploy and monitor simula-ons across mul-ple computa-onal resources To visualize and steer the results of distributed simula-ons in real -me
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Clinical SupercompuLng: Diagnosis and Decision Support in Surgery
• Provide simula-on support from within the opera:ng theatre for neuroradiologists
• Provide new informa.on to surgeons for pa.ent management and therapy: Diagnosis and risk assessment Predic-ve simula-on in therapy
• Provide pa-ent-‐specific informa-on which can help plan embolisa-on of arterio-‐venous malforma-ons, coiling of aneurysms, etc.
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GENIUS Clinical Workflow
Book compu-ng resources in advance or have a system by which simula-ons can be run urgently.
Shi^ imaging data around quickly over high-‐bandwidth low-‐latency dedicated links.
Interac-ve simula-ons and real-‐-me visualisa-on for immediate feedback.
15-20 minute turnaround
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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
PaLent-‐specific HIV Drug Therapy
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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
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Simulator for Personalised Drug Ranking 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
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.
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The Life Science Problem
ExponenLal development of science, discovery, and engineering, yet
This does not seem to empower medicine! Promises without Delivery
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• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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Reference datasets Population view Open Data English Language Low legal involvement Trans-national
Research Clinic
Individual Patient Closed data National Language High level of legislation National Entities
RESEARCH MEDICINE
Slide Courtesy of Ewan Birney
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Bioinformatics in biomedical research
(molecular, “omics”, systems biology)
Medical informatics In health care & clinical research
(EHR)
Translational Bioinformatics
Research re-use of clinical information
Linking Genotype
To Phenotype
Bridging gaps between BioinformaLcs and Medical InformaLcs
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h[p://www.inbiomedvision.eu
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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?
secure management of the clinically-derived data across hospital-university interfaces, via development of large scale data integration warehouses, and back into clinical decision support systems
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Data in hospitals
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-‐ Medical imaging (MRI, CT, etc.) in various formats (JPEG, DICOM, .xls …)
-‐ Pseudonymised pa-ent informa-on (therapy details, follow-‐up diagnosis, treatments, etc.)
-‐ Genomic, DNA, RNA, protein/proteomics data, etc.
Medical data
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Data integraLon & management • How to store heterogeneous data in one environment? • How to interface with the various types of data to understand and use?
(interoperability) • How to deal with the large size of data resul-ng from complex
simula-ons, e.g. terabytes and petabytes?
• How to acquire and transfer medical data from resource providers – Burn anonymised data on CDs/
DVDs and pass them on to researchers vs electronic transfer from provider to data storage directly?
– Network connecLvity for large simulaLons and data movements
• Logis-cs – IT infrastructure handling vast
amounts of data – Availability of data in due Lme
– Data storage/volume
– Access to HPC
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IMENSE: Individualised Medicine SimulaLon 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.
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Legal and ethical issues
Autonomy Well-‐being JusLce
Scien-sts Freedom to research
Facili-es and funding
Appropriate reward e.g. IP
Pa-ents Right to know or not to know
Improved treatment op-ons
Access to resources
Vulnerable groups Right to be heard Allevia-on of disadvantage
Equality
Professional groups
Professional judgment
Increased burden?
Implica-ons for prac-ce
Data breach is the unauthorised acquisi-on, access, use, or disclosure of protected health informa-on
ownership of data, compliance, what are the applicable laws and regula-ons
governing the data? Audi-ng in the cloud?
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PaLent Empowerment
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• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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• 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 plajorm.
• Turn this informa-on into knowledge that assists in taking medical, clinical and lifestyle decisions
IT Future of Medicine Up to €1B EU FET flagship proposal
h[p://www.ijom.eu
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Medicine as driver of ICT innovaLon
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
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A virtual paLent integraLon of models
Molecules
Tissues Anatomy
Statistics
35
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ICT Layers of ITFoM
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• The Human Genome Project
• The Virtual Physiological Human (VPH) ini-a-ve
• VPH Simula-on Case Studies – 1) clinical decision support in surgery 2) towards personalised drug design
• INBIOMEDvision – challenges ahead
• EU FET Flagship project IT Future of Medicine
• Conclusions
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• 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
– treatments
– lifestyle choices for global ci-zens
Conclusions
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Thank you for your a^enLon!
Nour Shublaq
Centre for Computa-onal Science University College London, UK