This document discusses the promises and challenges of personalized medicine. It begins by reviewing past initiatives like the Human Genome Project and the Virtual Physiological Human project. It then presents two case studies using physiological simulations for clinical decision support in surgery and for personalized drug design. The challenges of integrating data and models across multiple scales are discussed. The talk concludes that while significant progress has been made, fully realizing the promises of personalized medicine will require overcoming remaining technological and data integration challenges.

1. 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
n.shublaq@ucl.ac.uk
MIE 2012 – Process, Information, and Data Models,
Monday Aug 27, 2012, Pisa

2. 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

3. 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. 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!)

5. 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

6. • 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

7. • 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?

8. …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

9. Environment
Population
dimensional
scales
temporal
scales
Organism
Organ System
Organ
Tissue
Cell
Molecule
Atom
IntegraLon
across..
organ
systems

10. • 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

11. 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

12. 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.

13. 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

14. 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-terminalC-terminal
EU FP6 ViroLab project and EU FP7 CHAIN project
PaLent-­‐specific
HIV
Drug
Therapy

15. 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

17. 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.

18. The
Life
Science
Problem
ExponenLal
development
of
science,
discovery,
and
engineering,
yet
This
does
not
seem
to
empower
medicine!
Promises
without
Delivery

19. • 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

20. 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

21. 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

22. h[p://www.inbiomedvision.eu

23. 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

24. Data
in
hospitals

25. -­‐
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

26. 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

27. 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.

30. 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?

31. PaLent
Empowerment

32. • 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

33. • 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

34. 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
Innovation
Virtual patient
Better drugs, disease prevention, evidence-based decision-making

35. A
virtual
paLent
integraLon
of
models
Molecules
Tissues Anatomy
Statistics
35

36. ICT
Layers
of
ITFoM

37. • 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

38. • 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

39. Thank
you
for
your
a^enLon!
Nour
Shublaq
Centre
for
Computa-onal
Science
University
College
London,
UK
n.shublaq@ucl.ac.uk