Mie2012 27 aug12-shublaq

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

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

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

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!)

4

Organism
=
Computer

Genome
&
the
Environment

Genome

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,
(PentiumV) (neuronal net visualisation)

given
a
speciﬁc
environment

Phenotype

Slide Courtesy of Hans Lehrach

•  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

What
is
the
VPH?

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

Modelling
how
the
human
body
works

…pa-ent-‐tailored
computer

models,
used
for
diagnosis,

preven-on,
drug
treatment

and
surgical
planning
–

assess
treatment
before

administering

Slide Courtesy of S. Kashif Sadiq

IntegraLon
across..

Environment

Population organ
systems

Organism

Organ System temporal
scales

Organ

Tissue

Cell

Molecule

dimensional
scales
Atom

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

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.

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

PaLent-‐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

agtgttaccgtactcatcagactcgaggttcaccgta
ctcatcagactcgaattcaccgtactcatcagactcg
attcaccgtactcatcagactcgsattcaaacccttg
gatcaagtgttaccgtactcatcagactcgsattcac
cgtactcatcagactcgattcaccgtactcatcagac
tcgsattcaccgtactcatcagactcgdsaddttcaa
accgggtcacacaagg

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

Simulator
for
Personalised
Drug
Ranking

Simulator: a decision support software to assist clinicians for cancer treatment, and to reliably
predicts patient-specific drug susceptibility.

Array of available drugs

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.

The
Life
Science
Problem

ExponenLal
development
of
science,

discovery,
and
engineering,
yet

This
does
not
seem
to
empower
medicine!

Promises
without
Delivery

RESEARCH

MEDICINE

Research Clinic

Reference datasets
Population view Individual Patient
Open Data Closed data
English Language National Language
Low legal involvement High level of legislation
Trans-national National Entities

Slide Courtesy of Ewan Birney

Bridging
gaps
between
BioinformaLcs
and

Medical
InformaLcs

Translational
Bioinformatics

Bioinformatics Linking Medical informatics
in biomedical research Genotype In health care &
(molecular, “omics”, To clinical research
systems biology) Phenotype (EHR)

Research re-use of
clinical information

Challenges
ahead

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

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

Medical
data

-‐
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.

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

•  Logis-cs

medical
data
from
resource

–  IT
infrastructure
handling
vast
providers

amounts
of
data

–  Burn
anonymised
data
on
CDs/
–  Availability
of
data
in
due
Lme
DVDs
and
pass
them
on
to

–  Data
storage/volume
researchers
vs
electronic

–  Access
to
HPC

transfer
from
provider
to
data

storage
directly?

–  Network
connecLvity
for
large

simulaLons
and
data

movements

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.

Legal
and
ethical
issues

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?

Autonomy
Well-‐being
JusLce

Scien-sts
Freedom
to
Facili-es
and
Appropriate

research
funding
reward
e.g.
IP

Pa-ents
Right
to
know
or
Improved
Access
to

not
to
know
treatment
op-ons
resources

Vulnerable
groups
Right
to
be
heard
Allevia-on
of
Equality

disadvantage

Professional
Professional
Increased
Implica-ons
for

groups
judgment
burden?
prac-ce

IT
Future
of
Medicine
h[p://www.ijom.eu

Up
to
€1B
EU
FET
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

plajorm.

•  Turn
this
informa-on
into
knowledge
that

assists
in
taking
medical,
clinical
and
lifestyle

decisions

Medicine
as
driver
of
ICT
innovaLon

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

A
virtual
paLent
integraLon
of
models

Tissues Anatomy

Molecules Statistics

35

Conclusions

•  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

Thank
you
for
your
a^enLon!

Nour
Shublaq

Centre
for
Computa-onal
Science

University
College
London,
UK

n.shublaq@ucl.ac.uk

Mie2012 27 aug12-shublaq

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a Mie2012 27 aug12-shublaq

Semelhante a Mie2012 27 aug12-shublaq (20)

Mais de INBIOMEDvision

Mais de INBIOMEDvision (6)

Último

Último (20)

Mie2012 27 aug12-shublaq