dci for clinical translational research
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DCI for Clinical Translational Research
Shantenu Jha, LSU & UC-LondonPeter Coveney, UC-London
Slide acknowledgement Barbara Alving, NIH
Opportunities for Research and NIH Francis Collins
Applying high throughput technologies
Translating basic science discoveries into new and better treatments
Benefiting health care reform • Comparative effectiveness research• Prevention and personalized medicine• Health disparities research• Pharmacogenomics• Health research economics
Focusing on global health
Reinvigorating and empowering the biomedical research community
1 January 2010 Vol 327 Science, Issue 5961, Pages 36-37
The Translation Gap
Source: Butler D. Translational research: Crossing the valley of death. Nature. 2008;453:840–2.
National Health Expenditures as a Percent of GDP
Scope: from basic discovery to clinical research Scale: from molecule to organism
Technology forStructural
Biology Synchrotron
x-ray technologies
Electron microscopy
Magnetic resonance
Technology forSystems Biology Mass
spectrometry Proteomics Glycomics &
glycotechnology
Flow cytometry
Optics & LaserTechnology Microscopy Fluorescence
spectroscopy In Vivo
diagnosis
Imaging Technology• MRI• Image-guided
therapy• PET• CAT• Ultrasound
Informatics Resources Genetics Modeling of
complex systems
Molecular dynamics
Visualization Imaging
informatics
Biomedical Technology Research
VPH: Ambitious Way Forward
“The predictive paradigm in the treatment of disease”
“We need adaptable tools able to cope with multi-physics and multi-scale problems ranging from molecular to physiological levels. In-house tools must be developed, maintained and updated, or the scientists must rely on available software, adapting it to their specific needs“
The key to successful computational physiology is the capture of structure-function relationships in a computationally efficient manner. [Crampin et al., 2003]
In order to obtain patient-specific simulations, simulations must be performed on a routine basis in the clinical setting. … high performance computing required for transient CFD simulation must be accessible, possibly using Grid technology
What is the Physiome?The Physiome is the quantitative and
integrated description of the functional behaviour of the physiological state of an individual or species
Physiome at IUPS Conference
1993 20091997 2005 2006 2007 2008
Roadmap for Physiome
EC/ICT Health Start discussing Physiome research
Molecular Biology
Microcomputers/home computers
Grid Computing
Finite Elements
White paper completed
FP6: STEP
VPH Roadmap for (STEP)
FP7 call 2 Objective ICT-2007.5.3: Virtual Physiological Human
VPH NoE starts
Systems Biology
Human Genome Project
ICT Bio: need for standards working group
1st meeting standards working group
Physiome Project
VPH/Physiome History -- Consilience
VPH- I FP7 projects
Networking NoE
OsteoporosisIP
Alzheimer's/ BM & diagnosis STREPHeart /CV
disease STREP
Cancer STREP
Liver surgery STREP
Heart/ LVD surgery STREP
Oral cancer/ BM D&T STREP
CV/ Atheroschlerosis IP
Breast cancer/ diagnosis STREP
Vascular/ AVF & haemodialysis STREP
Liver cancer/RFA therapy STREP
Security and Privacy in VPH CA
Grid access CA
Heart /CV disease STREP
Industry
ClinicsOther
Parallel VPH projects
HIV-1 Protease is a common target for HIV drug therapy
Monomer B101 - 199
Monomer A1 - 99
Flaps
Leucine - 90, 190
Glycine - 48, 148
Catalytic Aspartic Acids - 25, 125
Saquinavir
P2 Subsite
N-terminalC-terminal
Patient-specific HIV drug therapy
Enzyme of HIV responsible for protein maturation• Target for Anti-retroviral
Inhibitors• 9 FDA inhibitors of HIV-1
protease
So what’s the problem?• Emergence of drug resistant
mutations in protease• Render drug ineffective• Drug resistant mutants have
emerged for all FDA
One part of “HIV Cycle”• Need for speedy calculation
VPH: LONI-TeraGrid-DEISA Project
Aim: To enhance the understanding of HIV-1 enzymes using replica-based methods across federated TG-DEISA-LONI• Do so using general-purpose, extensible, scalable approach• Test limits of Distributed Scale-Out – both algorithmic and
infrastructure limits• As part of the VPH project, to ultimately help build the CI for
quick, efficient (patient-specific) decision-tools using predictive MD of drugs and enzymatic targets (HIV-1 protease)
Integration of SAGA into Binding Affinity Calculator (BAC) tools to facilitate distributed Scale-Out Simulation and calculation
workflow• Protonation study of Ritonavir bound to HIV-1 Protease wild type
• Study of binding affinity between 6 HIV-1 Protease mutants and the drug Ritonavir using SAGA-BAC Tools
Transatlantic 10Gb linkTeraGrid 40Gb
backboneDEISA 10Gb
network
JA.NET (UK) 40Gb network
.. And You Asked # What problem was your project designed to solve?
• True Grand Challenge – scientific and research infrastructure• Many elements to VPH/Translational Research. Focus on lowering TTC
# How did the community come together?• Collectively Seduced by Money…
# What were the challenges?• Trade off between General purpose vs Customised solutions/approaches• “Novel” Usage Modes of Research Infrastructure – viewed as disruptive
# What did you learn? • [Ongoing project] Difficult to interoperate across infrastructure • Establish Application-level Interoperability not just Service-level Interoperabilty
# What have you achieved• Utilized multiple resources in a given grid infrastructure, but still struggling to do
routine concurrent simulations across distinct DCI (Grid projects)
# What is left to be done?• Software, policies, interoperability … all in all: A lot!
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