Human genetic evidence
supports approved drug
indications
Matt Nelson
October 12, 2015
Acknowledgements
• GSK
– Hannah Tipney
– Judong Shen
– Jeff Painter
– Mark Hurle
– Pankaj Agarwal
– John Whittaker
– Philippe Sanseau
• GWASdb – Hong Kong University
– Mulin Jun Li
– Pak Chung Sham
– Junwen Wang
• Pointer variant-gene mapping –
Columbia University
– Paola Nicoletti
– Yufeng Shen
– Aris Floratos
• ENCODE DHS correlations – University
of Washington
– John Stamatoyannopoulos
• Manual review of top GWAS diseases –
McGill University
– Rui Li
– Vincent Forgetta
– Mark Lathrop
– Brent Richards
2
Failure due to lack of efficacy is a major challenge in drug
development
Drug will progress to
next phase
Overall probability
of success
Drug in current phase
will be approved
×
×
~$1.5B in R&D costs per drug
Hay et al. (2014) Nat. Biotech. 32:40-51
Arrowsmith & Miller (2013) Nat. Rev. Drug. Disc. 12:569
3
Genetics provides natural human experiments that can
help guide target selection and quality assessment
Preclinical
Studies
Experimental
Medicine
Phenotypic
Screens
Clinical
Validation
Molecule
Target
Disease
Mechanism
Cellular
Models
Rare
Diseases
Animal
Models
GWAS &
PheWAS
Literature
Mining
Human
Epidemiology
Regulatory
Networks
4
Genome-wide association studies identify genetic
factors that affect human health
~20,000 subjects
QQ (PP) Plot
5
Kathiresan et al. (2009) Nat. Genet. 41:56-65
Genome-wide association studies have identified
thousands of variants that influence human traits
Associations
P ≤ 5×10-8
Publications
New Unique
Traits
Source: NHGRI/EBI GWAS Catalog, July 10, 2015
6
Next-generation sequencing is uncovering genetic
causes of rare monogenic disorders
10 discovery probands, 7 validation, followed up in 53 total families
7
Ng et al (2010) Nat Genet 42:790–3; Bamshad et al (2011) Nat Rev Genet 12:745–55
Next-generation sequencing is uncovering genetic causes
of rare monogenic disorders at a rapid rate (~3/week)
Chong et al. (2015) Am. Jour. Hum. Genet 97:199-215
% LDL-C reduction
LDL-C –log10(p)
Can human genetics improve drug discovery, development
and repositioning decisions?
Kathiresan et al. (2009) Nat. Genet. 41:56-65
Rossi S, ed. Australian Medicines Handbook. Adelaide, 2010
Questions
Is genetic evidence enriched within targets
for successful drugs?
How often are successful drug
mechanisms supported by a genetic
association?
Does this vary among diseases and
therapeutic areas?
STOPGAP Project: Systematic Target
Opportunity Assessment by Genetics
Association Predictions
9
STOPGAP project integrated three sources of data to assess
the genetic evidence for successful drug targets
10
Genetic variants are mapped to genes through LD, gene
expression (eQTL), and regulatory elements
Disease
Kawasaki disease
Variant
PubMed
Pvalue
rs1600249
22446962
2.40E-10
Rheumatoid arthritis rs1600249
21225715
5.00E-06
Physical
position
Linkage
disequilibrium
Regulatory
control
Prior development efforts
in liver fibrosis, AD, and
cancer
DHS Correlation
11
GeneScore: quantifying the causal connection between
variant and gene
Association source
GWAS GWAS
Other
Suppl. Catalog
OMIM
<0.75
≥0.75
≥ 0.9
-log10(p-value)
LD with reported variant
6060
–log10 (p-value)
Variant function
DHS eQTL or eQTL &
Other
Missense
Rdb 3 DHS (2) DHS
4040
Number of independent associations
<3
≥3
≥5
≥ 10
2020
Gene Score Contribution
0
1
Low
2
3
4
5
6
Gene Score
Medium
7
8
9
10
11
High
12
Genetic variants around targets of approved drugs are
more likely to affect human traits than other genes
*Protein sequence variation
*Protein sequence variation
Odds Ratio (log scale)
Enrichment of target genes with any genetic evidence
*RVIS: Residual variation intolerance score from Petrovski et al. PLoS Genet. 9:1003709
13
Using the “is a” relationships among MeSH→UMLS terms to
assess similarity for analysis
Applied both Lin and Resnik “path + information
content” methods, normalized, and averaged
From Pedersen et al. IHI 2012 Tutorial
http://www.comp.hkbu.edu.hk/ihi2011/Tutorials%20-%20IHI2012.htm
Using the relationships among MeSH→UMLS terms to
assess trait and indication similarity for analysis
Example similarity measures
Cardiovascular
Diseases
Heart
Diseases
Neoplasms by
Histologic Type
Neoplasms by
Site
Mycardial
Ischemia
Glandular and
Epithelial
Neoplasms
Neoplasms,
Genitourinary
Carcinoma
Neoplasms,
Urologic
Squamous cell
carcinoma
Neoplasms,
Kidney
Coronary
Disease
Coronary
Stenosis
Neoplasms
Coronary Artery
Disease
Relative similarity = 0.79
Relative similarity = 0.37
15
Most approved drug indications have 1+ similar traits that
have been investigated genetically
60
Count
40
20
0
0.00
0.25
0.50
0.75
1.00
Relative Similarity
16
Direct genetic evidence for approved target–indications
varies significantly among disease areas
Test of heterogeneity among disease categories: p = 10-12
17
The percentage of targets with genetic evidence increases
with later stages of clinical development
18
Both Mendelian and complex trait genetics correlate with
successful drug targets
19
Genetic effect size is not related to approved drug status
Effect size does not predict likelihood of success
Target for approved drug
with genetic evidence
In collaboration with Brent Richards, McGill University
20
The relative advantage of genetic association in predicting
success is hampered by a lack of failure data
>127,000 drug trials
Trial
Outcome
Recorded
Phase 2
and/or 3
Failed
Due to
Efficacy
Disease Area
Count (%)
Neoplasms
152 (68%)
Digestive System
17 (8%)
Musculoskeletal
15 (7%)
Hemic Lymphatic
10 (4%)
Nutritional and Metabolic
5 (2%)
Only 15% of approved targetindication for Neoplasms
224 Target-indication
failures due to efficacy
21
Estimating the relative advantage of targets with genetic
support
Assuming the overall probability of a drug in phase II progressing to
approval is 27% we estimate the impact of genetic information on
probability of success
– P(Success | Genetic Assn) ≈ 0.52
– P(Success | No Genetic Assn) ≈ 0.26
– Relative value of genetic information (ratio): 2.0 (95% CI = {1.6, 2.4})
Genetic evidence increases the probability that a drug will
progress to approval
23
Increasing the proportion of drug targets with genetic
evidence can lead to higher clinical success rates
Assume 15% of
historic portfolio with
genetic evidence
30%
with 95% confidence intervals (blue)
2-fold
24
Uncovering the genetic contributions to human health will
have measurable contributions to discovering new
medicines
60
Count
40
20
Associations
P ≤ 5×10-8
0
0.00
0.25
0.50
0.75
1.00
Relative Similarity
Publications
New Unique
Traits
Source: NHGRI/EBI GWAS Catalog, July 10, 2015
25
Summary
• Targets of successful drugs are more likely to be coded by genes that
influence human health
• About 8% of successful drug mechanisms have direct genetic evidence
for the approved indication
– This is a ~3.5 fold enrichment over target–indications that did not progress beyond
phase 1
– As genetic knowledge continues to accrue, this proportion should increase
• Based on historical drug development data and existing GWAS and
OMIM data, direct genetic evidence supporting target role with the
indication doubles the probability of success over drugs without it
• There is ample opportunity and need for genetic studies of disease that
lack adequate therapies and further elucidation of the genetic risk factors
that influence them
26
Important provisos
• This study does not mean that genetic evidence is a
panacea
– Not all drugs with genetically supported targets and indications will succeed
– Most successful drugs do not currently have genetic evidence
• All other things being equal genetics evidence increases
probability of success
• Genetic evidence provides guidance for choice of
indications
– Currently, most genetic studies deal with relatively crude disease outcomes
in broadly defined populations
• Increasing targets with genetic evidence should reduce
attrition due to lack of efficacy
27
Thank you
Permutation algorithm
Permute this
relationship
Variant-Trait
Drug-Indication
Gene-Trait
Gene-Indication
Permute by creating new trait-trait
mappings to retain relationships
among variants/genes and new traits
within each region
Original
Trait
Trait Permutation
HDL-C
T2D
LDL-C
Allopecia
Total-C
Kawasaki Disease
Etc.
Maintains structure at gene-trait level,
but does break structure among
correlated traits
Merge on bestmatch trait-indication
for each gene
Gene-Trait-Indication
Compute overlap at several
relative similarity and pvalue thresholds
Permutation test demonstrates enrichment of genetic
associations among approved drugs is highly significant
Result of 1000 permutations
Observed overlap
Applying human genetics to target validation
Criteria for gene–drug pairs in drug discovery
• The gene harbors a causal variant that is unequivocally
associated with a medical trait of interest
• The biological function of the causal gene and causal
variant are known
• The gene harbors multiple causal variants of known
biological function, thereby enabling the generation of
genotype–phenotype dose–response curves
• The gene harbors a loss-of-function allele that protects
against disease, or a gain-of-function allele that
increases the risk of disease
• The genetic trait is related to the clinical indication
targeted for treatment
• The causal variant is associated with an intermediate
phenotype that can be used as a biomarker
• The gene target is druggable
• The causal variant is not associated with other adverse
event phenotypes
• Corroborating biological data support genetic findings
31
Genome-wide association studies identify genetic
factors that affect human health
~37,000 schizophrenia cases
~113,000 controls
32
Schizophrenia Working group of the Psychiatric Genomics Consortium (2014) Nature 511:421-7