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

n.shublaq@ucl.ac.uk

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
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!)

Cost of whole genome sequencing expected to drop to $100 in a few years
4

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

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

•  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

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

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

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

WIREs Syst Biol
Med, Aug 2012

Chem Biol Drug
Des special
theme, Jan 2013
Epub Jul 2012

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

Clinical
SeSng
–
HIV
drug
ranking

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

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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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