Bio it worldexpoeurope2012_shublaq

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

[email protected]

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





•  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





•  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

agtgttaccgtactcatcagactcgaggttcaccgtactcatcagactcgaattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaaacccttggatcaagtgttaccgtactcatcagactcgsattcaccgtactcatcagactcgattcaccgtactcatcagactcgsattcaccgtactcatcagactcgdsaddttcaaaccgggtcacacaagg

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





•  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

31

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





•  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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