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!