Genomics and drug development
Aroon Hingorani
Director, UCL Institute of Cardiovascular Science
Professor of Genetic Epidemiology
University College London
a.hingorani@ucl.ac.uk
UCL Hospitals National Institute of Health Research
Biomedical Research Centre
Human Genetic Variation
Hingorani A D et al. BMJ 2010;341:bmj.c5945
©2010 by British Medical Journal Publishing Group
Genetic spectrum of human disease
Polygenic disorder
Monogenic disorder
Mutation
X
Y
X
X
Single
nucleotide
Polymorphism
Polymorphism
(SNP)
Y
Y
Othergenes
genes
Other
Environment
Environment
Disease
Health
Health
Disease
Disease
Genomics and the Whitehall II study
•
•
•
•
•
•
Creation of a DNA biorepository
Targeted genotyping
Cardiochip - 50,000 variants
Metabochip – 200,000 variants imputed to 2M
DrugDev Array – 480,000 variants imputed to ?
The UCLEB Consortium
University College-London School-Edinburgh-Bristol (UCLEB) Consortium
Clinical events
>5000 CVD events including CHD and stroke.
Additional CV events: angina, heart failure, and DVT/PE
>4000 cancer events
>2000 Type-2 diabetes cases
Phenoytpes:
wide coverage of organs/systems allows efficacy and safety profiling for most common
~2000 COPD cases
disorders
> 52 blood markers in up to 27,000 samples
~ 216 NMR metabolomic traits in >11,000 samples and leveraged funding for additional 15,000 samples
Organ/System
Phenotype
Associated disease outcome
Brain:
Heart:
Blood:
Blood vessel:
Lung:
Kidney:
Liver:
Bone:
Cognitive function
ECG traits
Ultra-dense lipids
Carotid atherosclerosis
FEV1, FVC
eGFR
AST, ALA, GGT
Bone mineral density
Alzheimer’s disease
AF, sudden death
Type-2 diabetes
Atherosclerotic vascular disease
COPD
End-stage renal disease
Fatty liver and chirrosis
Osteoporosis
Genomics and Drug Development -Overview
• Process of drug development and the potential
benefits of genomic support for drug target
selection and validation
• The Druggable Genome
• Design of a new genotyping array to support drug
development
Drug development process
Late-stage failure
Pre-clinical development
Clinical development
Programme attrition
Cost
After:
Kola & Landis, Nature Reviews Drug Discovery 2004; 3, 711-716
Arrowsmith, Nature Reviews Drug Discovery 2011; 10, 328-329; and
Nature Reviews Drug Discovery 2013; 12, 569
PaulS et al. Nature Reviews Drug Discovery 2010; 9, 203-214
Drug development process
Late-stage failure
Pre-clinical development
Poor predictive accuracy
of preclinical studies
Clinical development
Definitive target validation
experiment (the phase III RCT)
is the final step
The RCT is the pivotal drug target validation experiment
Randomised controlled trial (RCT; Phase III)
Patients
Design feature
Attribute
In humans
Avoids limitations of
experiments in cells,
isolated organs and animal
models
Randomised experimental
intervention
Overcomes confounding
and reverse causation
inherent in human
observational studies
Pre-specified efficacy and
safety outcomes, careful
sample size determination
Low risk of false positive
findings
Randomisation
Intervention
Target affected
Outcome
Placebo
Target unaffected
Outcome
Genetic studies as Nature’s randomised trials
Hingorani A, Humphries S. Lancet 2005; 1906–1908
RCT (Phase III)
Mendelian randomisation trial
Patients
Population
Randomisation
Intervention
Target affected
Outcome
Random allocation of alleles
Placebo
Target unaffected
Outcome
Target genotype aa
Target expression or activity
modified
Outcome
Target genotype AA
Target activity
unchanged
Outcome
Variants of a gene encoding a drug target, allocated at random at conception,that affect
its expression or function,
can be used as a tool to infer the outcome of modifying the same target pharmacologically
Relationship between gene, target and compound
Compound
Target protein
On-target effect
Intended
outcome
Encoding
gene
Relationship between gene, target and compound
Statin
HMGCR
HMG-coA reductase
Target protein
On-target effect
Intended
outcome
HMGCR variants, statins, LDL-C and coronary events
RCT (Phase III)
Mendelian randomisation Trial
Sample
Population
Randomisation
Random allocation of alleles
Protein target: HMGCR
Protein target: HMGCR
Placebo
HMG-CoA red inhibitor
LDL-C reduced
CV event
rate lower
LDL-C unchanged
Off target
HMGCR inhibitors (statins)
LDL-C reduced 1 mmol/L
CHD risk reduction 25%
CTT Lancet 2010, 376, 1670–1681
CV event
rate higher
HMGCR aa
LDL-C reduced
CV event
rate lower
Genotype AA
LDL-C unchanged
CV event
rate higher
HMGCR variant (rs12916)
LDL-C reduced by 0.07 mmol/L
CHD risk reduction 6%.
Ference et al. J Am Coll Cardiol 2012; 60(25):2631-9
Common genetic variants and small phenotypic effect
size
0.06 mmol/L
per allele
Courtesy Daniel Swerdlow
Genomic support for drug target selection
and validation: selected examples
MR trials: Example 1 –
PLA2G2A, sPLA2, varespladib and
CVD events
Pre-clinical development
Progression of a new therapeutic at a
critical decision point
sPLA2, Varespladib and Vascular
Events: Phase-III trial (JACC 2013)
Clinical trials
Holmes MV et al. JACC 2013 Nov 19;62(21):1966-76
Varespladib and Cardiovascular Events in Patients With an Acute Coronary Syndrome
JAMA. 2014;311(3):252-262. doi:10.1001/jama.2013.282836
Summary findings pre Phase-III trial: sPLA2-IIA concentration and activity is associated
with incident and recurrent major vascular events
Drug-target and therapeutic: a small molecule sPLA2 inhibitor (Varespladib; Anthera)
reduced sPLA2 mass by ~90%
VISTA-16: Randomised 5145 patients with acute coronary syndrome to varespladib
500mg daily or placebo. Outcome was assessed at 16 weeks. The trial was stopped at
prespecified interim analysis for futility or possible harm.
Summary results (http://www.anthera.com/VISTA-16.pdf)
HR for primary outcome (CVD death, non-fatal MI, stroke):
HR for stroke
HR for non-fatal MI
1.24, p=0.155
1.43, p=0.025
1.68, p=0.009
MR Trials: Example 1 - PLA2G2A rs11573156 allele and CVD outcomes
Association between PLA2G2A rs11573156
and CVD outcomes (per C allele)
Setting,
Outcome
Studies
(events/participants)
2
Odds ratio (per
allele) (95% CI)
I ,%
(95%CI)
General Population: Incident events
Major vascular events 13 (8021/56359)
Nonfatal MI
13 (4208/51016)
Nonfatal Stroke
11 (2304/46790)
Fatal MI/Stroke
12 (1509/48118)
1.02
1.04
1.00
1.01
1.06)
1.10)
1.07)
1.10)
26(0,51)
22(0,59)
19(0,59)
41(0,70)
General Population: Prevalent events
Major vascular events 12 (7513/55523)
MI
12 (6411/54884)
Stroke
8 (1102/37280)
0.99 (0.95, 1.03)
0.98 (0.93, 1.03)
1.03 (0.93, 1.15)
38(0,63)
52(7,75)
0(0,67)
Acute Coronary Syndrome: Recurrent events
Major vascular events 9 (2520/15768)
Nonfatal MI
8 (1158/14152)
Nonfatal Stroke
6 (223/12283)
Fatal MI/Stroke †
9 (1139/15724)
0.96
0.99
0.85
0.96
0(0,45)
28(0,67)
0(0,74)
0(0,64)
.5
1
Lower
(0.98,
(0.98,
(0.93,
(0.93,
(0.90,
(0.89,
(0.69,
(0.87,
1.03)
1.09)
1.06)
1.06)
2
Higher
Odds ratio
Holmes MV et al. J. Am Coll Cardiol 2013 Nov 19;62(21):1966-76
MR Trials – distinguishing on from off-target effects
Compound
Other protein
Target protein
Off-target effect On-target effect
Unintended
outcome
Intended
outcome
Encoding
gene
Unintended
outcome
MR Trials – distinguishing on from off-target effects
Compound
Other protein
Target protein
Encoding
gene
Target profile
Off-target effect On-target effect
Unintended
outcome
Intended
outcome
Unintended
outcome
Compound profile
MR trials:
Example 2 – CETP, torcetrapib,
HDL-C and BP
Trait
RCTs
(individuals)
Sample
Drug Randomisation
Torcetrapib/atorvastatin
vs
Atorvastatin alone
Torcetrapib
Mean difference (95%CI)
CETP-inhibition
HDL-C (mmol/L)
17911
0.78 (0.68, 0.87)
Systolic BP
(mmHg)
17911
4.471(4.09, 4.84)
15067
No-CETP inhibition
Change in lipid traits
No change in lipids
BP
(Off-target)?
Hazard ratio (95%CI)
CVD events
Control
1.25 (1.09,1.44)
HDL
LDL
BP
TRG
(On-target)?
Sofat R et al. Circulation 2010 Jan 5;121(1):52-62
CETP gene variants, lipids and BP
CETP
Lipids and apolipoproteins
Blood pressure
TheJan
BP5;121(1):52-62
raising effect of
2010
torcetrapib is off-target
Sofat R et al. Circulation 2010 Jan 5;121(1):52-62
CETP gene variants, lipids and BP
CETP
Lipids and apolipoproteins
Blood pressure
TheJan
BP5;121(1):52-62
raising effect of
2010
torcetrapib is off-target
Sofat R et al. Circulation 2010 Jan 5;121(1):52-62
CETP gene variants, lipids and BP
CETP
Lipids and apolipoproteins
Blood pressure
TheJan
BP5;121(1):52-62
raising effect of
2010
torcetrapib is off-target
Sofat R et al. Circulation 2010 Jan 5;121(1):52-62
MR trials: Example 3 – potential repurposing
Inflammation strongly linked to CHD but
no currently validated therapeutic target
Rheumatoid arthritis
Pre-clinical development
Repurposing
IL6R blockade (tocilizumab)
and CHD
(Lancet 2012)
Clinical trials
CHD
Pre-clinical development
Clinical trials
Repurposing IL6R as a target for CHD
Drug intervention
Patients with rheumatoid arthritis
Randomisation (Tocilizumab)
Protein target: IL6R
IL6R- blocker (MAB)
Placebo
Reduced IL6 signalling
IL6 signalling unchanged
RA disease
activity higher
RA disease
Activity lower
Biomarker
Tocilizumab
IL-6

(n=1,446)
CRP

(n=3,010)
Fibrinogen
Soluble IL-6R


(n=409)
(n=1,465)
Albumin

(n=108)
Haemoglobin

(n=2,072)
The Interleukin-6 Receptor Mendelian Randomisation Analysis
(IL6R MR) Consortium* Lancet 2012; 379: 1214–24
Repurposing IL6R as a target for CHD
Drug intervention
Genetic study: natural randomisation
People at risk of CHD
Patients with rheumatoid arthritis
Randomisation (Tocilizumab)
Random allocation of IL6R alleles
Protein target: IL6R
Protein target: IL6R
Placebo
IL6R aa
IL6 signalling unchanged
Reduced IL6 signalling
IL6R- blocker (MAB)
Reduced IL6 signalling
RA disease
activity higher
RA disease
Activity lower
Biomarker
Tocilizumab
IL-6

(n=1,446)
CRP

(n=3,010)
Fibrinogen
Soluble IL-6R


(n=409)
(n=1,465)
Albumin

(n=108)
Haemoglobin

(n=2,072)
CV event
rate lower
IL6R AA
IL6 signalling unchanged
CV event
rate higher
Repurposing IL6R as a target for CHD
Drug intervention
Patients with rheumatoid arthritis
Genetic study: natural randomisation
Randomisation (Tocilizumab)
Protein target: IL6R
IL6R- blocker (MAB)
Placebo
Reduce IL6 signalling
IL6 signalling unchanged
RA disease
activity higher
RA disease
Activity lower
Biomarker
Tocilizumab
IL6R SNP rs7529229
IL-6

(n=1,446)

(n=29,838)
CRP

(n=3,010)

(n=76,527)
Fibrinogen
Soluble IL-6R


(n=409)
(n=1,465)


(n=52,667)
(n=1,454)
Albumin

(n=108)

(n=5,787)
Haemoglobin

(n=2,072)

(n=17,898)
Additional examples
• Darapladib, LpPLA2 and CHD
MR trial: Casas JP. et al. Circulation 2010 Jun 1;121(21):2284-93
RCT: STABILITY N Engl J Med 2014 May 1;370(18):1702-11;
SOLID TIMI 52 JAMA. 2014 Sep 10;312(10):1006-15
• Folic acid, homocysteine and stroke
MR trial: Holmes MV et al. Lancet 2011 Aug 13;378(9791):584-94
RCT: Huo et al. JAMA 2015 Apr 7;313(13):1325-35
• Ezetimibe, LDL-C and CHD
MR trial: MI Genetics Consortium Investigators N Engl J Med 2014 Nov 27;371(22):2072-82
RCT: Cannon CP et al. N Engl J Med 2015 Jun 18;372(25):2387-97
Published Genome-Wide Associations through
12/2013
http://www.ebi.ac.uk/gwas/
GWAS ‘rediscoveries’ of human drug targets
GWAS Phenotype
Associated Gene (Ensembl ID)
Associated Gene Description
Total/LDL cholesterol
HMGCR
(ENSG00000113161)
Lovastatin,
3-hydroxy-3-methylglutaryl-CoA
Pravastatin,
reductase
Simvastatin
Type 2 diabetes
KCNJ11
(ENSG00000187486)
potassium inwardly-rectifying
Glyburide,
channel subfamily J member 11 Rosiglitazone
PPARG
(ENSG00000132170)
peroxisome proliferatoractivated receptor gamma
Rosiglitazone,
Repaglinide
CHRNA3
(ENSG00000080644)
cholinergic receptor, nicotinic,
alpha 3
Nicotine,
Varenicline
CHRNB4
(ENSG00000117971)
cholinergic receptor, nicotinic,
beta 4
Nicotine,
Varenicline
Nicotine dependence
Compound USAN/INN
Courtesy Chris Finan and Felix Kruger
Illumina Human Drug Core – Array Design
Developers: Casas, Finan, Shah, Kruger and Hingorani (UCL); Gaulton and Overington (EBI);
together with the Illumina bioinformatics team
Illumina Human Drug Core
~480,179 assays and ~499,367 beadtypes
Drug development custom content
Variants of interest: GWAS SNPs; APOE; AIM;
fingerprint
Extracellular or transmembrane targets
and members of drug target families
(~2370 genes)
Proteins with ‘drug-like compounds or
closely related to drug targets (~682 genes)
Targets of approved drugs and those
in clinical development; ADMET (~1426 genes)
Illumina Human Core Array
Whole genome tagSNP markers - 250,421
Indel/exome markers >20,000
Headroom for custom markers - 200,000
Coverage of druggable genome by genotyping
platforms
Illu DrugDev Consortium 24
Fraction 1kg ph. 3 variants
covered (r2> 0.8)
0
1
Courtesy
Dr Chris Finan, UCL
Tier 1
Tier 2 Tier 3a Tier 3b
Summary
• Genetic studies in populations share the design features of a randomised controlled
trial (RCT), the pivotal step in drug development
• Alleles in a gene encoding a drug target that affect its expression or activity can
help predict the effect of modifying the same target pharmacologically
• Genetic studies in populations and patients may help support target selection and
validation in drug development
Colleagues, collaborators and funders
Juan Pablo Casas
Meena Kumari
Tina Shah
Reecha Sofat
Jorgen Engmann
Dan Swerdlow
Michael Holmes
Philippa Talmud
Steve Humphries
Fotios Drenos
Sonia Shah
Delilah Zabaneh
Harry Hemingway
Martin Bobak
Aida Sanchez
Eric Brunner
Meena Kumari
Mika Kivimaki
Michael Marmot
Mike Hubank
Kerra Pearce
Jutta Palmen
David Balding
Chris Power
Elina Hyponnen
John Deanfield
Di Kuh
Andy Wong
Richard Morris
Peter Whincup
Jacky Pallas
John Whittaker
Liam Smeeth
Frank Dudbridge
Claudio Verzilli
Leonelo Bautista
Shah Ebrahim
Debbie Lawlor
Tom Gaunt
Ian Day
Yoav Ben-Shlomo
George Davey Smith
Jackie Price
Gerry Fowkes
Ann Rumley
Gordon Lowe
Naveed Sattar
Patsy Munroe
Toby Johnson
Mark Caulfield
Manj Sandhu
Claudia Langenberg
Ken Ong
Nick Wareham
Kay Tee Khaw
Frances Wensley
John Danesh
Rosetrees Trust
National Institute for
Health Research
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Chris Finan1
Felix Kruger1
Tina Shah1
Jorgen Engmann1
Juan-Pablo Casas1,2
John Overington1,3
Anna Gaulton3
Anneli Karlsson3
Rita Santos3
Luana Galver McAuliffe4
Ryan Kelley4
Cora Vacher5
Acknowledgements
1.
2.
3.
4.
5.
Institute of Cardiovascular Science, and
Farr Institute in London, University
College London, UK
Farr Institute in London, University
College London, UK
European Molecular Biology Laboratory European Bioinformatics Institute,
Cambridge, UK
Illumina Inc, San Diego, USA
Illumina UK Ltd, Little Chesterford, UK
Acknowledgements
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Aroon Hingorani1
Chris Finan1
Felix Kruger1
Tina Shah1
Jorgen Engmann1
Juan-Pablo Casas1,2
John Overington1,3
Anna Gaulton3
Anneli Karlsson3
Rita Santos3
Luana Galver McAuliffe4
Ryan Kelley4
Cora Vacher5
1.
2.
3.
4.
5.
Institute of Cardiovascular Science, and
Farr Institute in London, University
College London, UK
Farr Institute in London, University
College London, UK
European Molecular Biology Laboratory European Bioinformatics Institute,
Cambridge, UK
Illumina Inc, San Diego, USA
Illumina UK Ltd, Little Chesterford, UK
Extending the use of genetic studies
to support target selection and validation
in drug development
• The Druggable genome
• The design of a genotyping array to support target selection
and validation in drug development
The Druggable Genome
• With few exceptions, drug targets are proteins
• Not all proteins are amenable to targeting by the main classes of
therapeutics (small molecule drugs, therapeutic monoclonal
antibodies or peptides)
• The ‘druggable genome’: defines the set of genes encoding druggable
targets
• ‘Druggability’ refers to the potential for a protein to be modified by a
drug-like small molecule
Prior estimates of the Druggable Genome
• Predated contemporary estimates of the number of protein coding genes
• May not have considered targets of bio-therapeutic drugs (e.g. peptides and
and therapeutic monoclonal antibodies)
• May not have included targets of recently licensed first-in-class drugs
An array with custom coverage of the
druggable genome
• Possibility that existing arrays either provided sparse coverage
of druggable genes (e.g., GWAS arrays) or dense coverage of a
modest number of druggable genes (e.g., gene-centric arrays
such as metabochip, cardiochip etc)
• Advantage in having dense coverage of known and likely drug
targets across all disease areas
• Allow identification of tractable targets and drug repurposing
opportunities
The Illumina Infinium DrugDev Array
Co-developers: Casas, Finan, Shah, Kruger and Hingorani (UCL); Gaulton and Overington (EBI);
together with the Illumina bioinformatics team
Potential users of the array
• Investigators with patient or population samples but no
prior genotyping array
• Investigators with patient or population samples previously
genotyped using a disease-focused fine-mapping array
• Investigators with patient or population samples genotyped
using earlier generation whole genome arrays
• Investigators contemplating genotyping of large-scale
electronic health record datasets
Pleiotropy in human complex diseases and traits
“……233 (16.9%) genes and 77 (4.6%) SNPs show pleiotropic
effects”
Disease_15
Disease_667
Disease_1123
Gene 1
Gene 20,000
Sivakumaran et al. Am J Hum Genet. 2011
Nov 11; 89(5): 607–618
Potential applications of the array
• Drug target discovery - identification of druggable proteins playing a causal
role in a disease of interest
• Drug target validation and prioritisation - informing if and when to advance
an existing drug or drug-like compound through a drug-development
pipeline
• Drug repurposing studies - identifying the role of a drug target in a
different disease from the current drug indication
• Separating on- vs. off-target effects - for first-in-class and fast follower
drugs
• Stratified medicine studies - within randomised trials or non-randomised
research studies
Conclusions
• Genetic studies in populations, case collections and electronic health record
datasets may help support drug development
• A new array which incorporates GWAS capability with custom content of the
druggable genome as well as genes involved in drug handling
may help support such studies
• A consortium based on this new array is planned