Bio/Healthcare-IT Research Priorities in Europe

1. 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
n.shublaq@ucl.ac.uk
From Drug Discovery Informatics to Personalised
Therapeutics – Oct 2012, Vienna

2. 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

3. Human
Genome
Project
30,000
3000
Sequencing of the human genome was 10
profoundly important science that led
2
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
Genomic
Mol.
Profiles
Structure

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
4

5. hMp://www.inbiomedvision.eu

6. Challenges
ahead
Biological
challenges
Societal
challenges
– Do
we
understand
biology
and
– Privacy
diseases
enough
to
develop
– How
to
prevent
inequali-es
in
reliable
computa-onal
models?
access
to
health
care?
– How
to
integrate
growing
– Health
care
economics
knowledge
into
models?
– Implementa-on
in
health
care
– How
to
prevent
adverse
ICT
Challenges
effects/misuse?
– Data
quality
– Data
management
– Data
security
– User
interfaces

7. 1 Genotype-­phenotype
resources
Complex
disease
networks
Molecular-­level
models
(GWAS,
PPI,
…)
Disease
gene
Oinding
&
sema ing
ntic
2
in
Disease
web
Developments
Text
m
susceptibility
System-­level
models
(organ
networks,…)
Phenotypic
variation
Pharmacogenomics
Clinical
phenotypes
(EHR,
multi-­scale
physiological
models…)
Exposome
3 Translational
Systems
Biology
(drugs,
diet,
environmental
chemicals,…)
Nour Shublaq et al. (2012) – under review

8. • 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

9. What
is
the
VPH?
• The Virtual Physiological Human is
a methodological and technological Organism
descriptive, integrative and
Organ
predictive, framework that is
Tissue
intended to enable the investigation
of the human body as a single Cell
complex system Organelle
Interaction
Protein
• Aims Cell
• Enable collaborative Signals
investigation of the human Transcript
body across all relevant scales Gene
• Introduce multiscale Molecule
methodologies into medical €207M initiative
and clinical research in EU-FP7

10. The
challenge:
organs
to
proteins
Environment
→ Medical informatics
Organism
Organ
system
→ Personalised medicine
Heart Lungs Diaphragm Knee Colon Liver Eye
Organ
x 1million 20 generations
Cardiac sheets Acinus Osteon Lymph node Liver lobule Nephron
Tissue
Cell
Network
Protein
Gene
Atom

11. • 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

12. Drug
Selec3on
and
Drug
Design
 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

13. WIREs Syst Biol
Med, Aug 2012
Chem Biol Drug
Des special
theme, Jan 2013
Epub Jul 2012

14. Pa3ent-­‐specific
HIV
Drug
Therapy
HIV-­‐1
Protease
is
a
common
target
for
HIV
drug
therapy
Monomer B Monomer A
• Enzyme
of
HIV
responsible
for
protein
101 - 199 1 - 99
matura-on
Flaps
Glycine - 48, 148
• Target
for
An--­‐retroviral
Inhibitors
• Example
of
Structure
Assisted
Drug
Design
Saquinavir
• 9
FDA
inhibitors
of
HIV-­‐1
protease
So
what’s
the
problem?
• Emergence
of
drug
resistant
P2 Subsite Catalytic Aspartic
muta-ons
in
protease
Acids - 25, 125
• Render
drug
ineffec-ve
Leucine - 90, 190 C-terminal N-terminal
• Drug
resistant
mutants
have
emerged
for
all
FDA
inhibitors
EU FP6 ViroLab project and EU FP7 CHAIN project

15. Clinical
SeSng
–
HIV
drug
ranking
agtgttaccgtactcatcagactcgaggttcaccgta
ctcatcagactcgaattcaccgtactcatcagactcg
attcaccgtactcatcagactcgsattcaaacccttg
gatcaagtgttaccgtactcatcagactcgsattcac
cgtactcatcagactcgattcaccgtactcatcagac
tcgsattcaccgtactcatcagactcgdsaddttcaa
accgggtcacacaagg

16. 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

17. 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.
Array of available drugs
BAC Simulator
Variant of target from patient
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.

18. 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

19. 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

20. 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

21. EGFR
muta3ons
for
lung
cancer
A750P
• Over
expression
of
Epidermal
Growth
Factor
L747-E749 del Receptor
(EGFR)
is
associated
with
cancer
L858R
G719S • Target
for
inhibitory
drugs
• Important
muta3ons
include
dele3ons
• Again
binding
affinity
calcula3ons
can
be
used
to
determine
muta3onal
effects
EGFR Tyrosine Kinase Domain

22. • 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

23. 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

24. 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.

27. P-­‐Medicine
Disease Disease Disease Multi-scale therapy
Modelling at the Modelling at the G S G M
1 2
G
0 Modelling at the predictions/disease
molecular Level cellular Level N A
tissue/organ evolution results
Level
• 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
Led
by
a
clinical
oncologist
-­‐
Prof
Norbert
Graf!
€13M,
2011-­‐2013,
EU
FP7

28. VPH-­‐Share
HIV Heart Aneurisms Musculoskeletal
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.
€11M,
2011-­‐2015,
EU
FP7
–
Promotes
cloud
technologies

29. IT
Future
of
Medicine
Up
to
€1B
EU
Future
Emerging
Technologies
Flagship
proposal
• 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
hMp://www.ikom.eu

30. ITFoM
Health care Industry
& society Computational
models of
Innovation
User needs biological systems:
cells ICT
organs &
individuals Biotech
Personalised medicine populations Pharma
Public health
Virtual patient
Better drugs, disease prevention, evidence-based decision-making

31. Use
Case:
Cancer
Treatment
Mutation Database
The Cancer Model
Drug Database
X 31
Tumor sampling Genome
Tumor stem cell extraction/ and Transcriptome
expansion
sequencing
X X
Modeling Drug treatment
Drug Response recommendation
Patient Specific Model

32. ICT
Layers
of
ITFoM

33. Rela3on
to
EU
Infrastructures
European Sequencing and
Genotyping Infrastructure
ISBE
Integrated Structural
Biology Infrastructure
Infrastructure for Systems
Biology – Europe
Partnership for Advanced
Computing in Europe
33

34. Computational Life and ife
and
Medical
Sciences
UCL
Computa3onal
L Medical Science
(CLMS)
Network
hMp://www.clms.ucl.ac.uk
Management:
UCL Partners: 14 NHS
Supported by the Dean’s Committee
Trusts and affiliated
healthcare institutes/clinics Provost's Strategic Fund Steering Committee

35. CLMS Goals
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

36. • 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

37. 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

38. Thank
you
for
your
aden3on!
Nour
Shublaq,
PhD
CREST University
College
London,
UK
n.shublaq@ucl.ac.uk
CREST
CREST
CREST
epcc|cresta
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