human genetic evidence supports approved drug indications matt nelson october 12, 2015
TRANSCRIPT
Human genetic evidence supports approved drug indicationsMatt NelsonOctober 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
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Failure due to lack of efficacy is a major challenge in drug development
Drug in current phase will be approved
Drug will progress to next phase
~$1.5B in R&D costs per drug
Overall probability of success
Hay et al. (2014) Nat. Biotech. 32:40-51Arrowsmith & Miller (2013) Nat. Rev. Drug. Disc. 12:569
× ×
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Genetics provides natural human experiments that can help guide target selection and quality assessment
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Rare Diseases
GWAS &PheWAS
HumanEpidemiology
RegulatoryNetworks
CellularModels
Literature Mining
AnimalModels
Phenotypic Screens
Clinical Validation
Experimental Medicine
PreclinicalStudies
DiseaseTarget
Mechanism
Molecule
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Genome-wide association studies identify genetic factors that affect human health
~20,000 subjects
QQ (PP) Plot
Kathiresan et al. (2009) Nat. Genet. 41:56-65
Genome-wide association studies have identified thousands of variants that influence human traits
Publications
AssociationsP ≤ 5×10-8
New Unique Traits
Source: NHGRI/EBI GWAS Catalog, July 10, 2015 6
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Next-generation sequencing is uncovering genetic causes of rare monogenic disorders
10 discovery probands, 7 validation, followed up in 53 total families
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
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Can human genetics improve drug discovery, development and repositioning decisions?
QuestionsIs 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
LDL-
C –
log 1
0(p)
Kathiresan et al. (2009) Nat. Genet. 41:56-65Rossi S, ed. Australian Medicines Handbook. Adelaide, 2010 9
STOPGAP project integrated three sources of data to assess the genetic evidence for successful drug targets
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Physical position
Genetic variants are mapped to genes through LD, gene expression (eQTL), and regulatory elements
Disease Variant PubMed Pvalue
Kawasaki disease rs1600249 22446962 2.40E-10
Rheumatoid arthritis rs1600249 21225715 5.00E-06
Linkagedisequilibrium
Prior development efforts in liver fibrosis, AD, and cancer
Regulatorycontrol
DHS Correlation 11
GeneScore: quantifying the causal connection between variant and gene
Association source
OMIMGWAS Catalog
GWAS Suppl.
Other
Variant function
MissenseeQTL &
DHSeQTL orDHS (2)
DHSRdb 3
Other
LD with reported variant
≥ 0.9≥0.75<0.75
Number of independent associations
≥ 5≥3<3 ≥ 10
Gene Score Contribution
20
40
60
0 1 2 3 4 5 6 7 8 9 10 11Gene Score
-log1
0(p-
valu
e)
Low Medium High
–log
10 (
p-va
lue)
20
40
60
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Genetic variants around targets of approved drugs are more likely to affect human traits than other genes
*RVIS: Residual variation intolerance score from Petrovski et al. PLoS Genet. 9:1003709 13
Odds Ratio (log scale)Enrichment of target genes with any genetic evidence
*Protein sequence variation
*Protein sequence variation
Using the “is a” relationships among MeSH→UMLS terms to assess similarity for analysis
From Pedersen et al. IHI 2012 Tutorialhttp://www.comp.hkbu.edu.hk/ihi2011/Tutorials%20-%20IHI2012.htm
Applied both Lin and Resnik “path + information content” methods, normalized, and averaged
Using the relationships among MeSH→UMLS terms to assess trait and indication similarity for analysisExample similarity measures
Cardiovascular Diseases
Coronary Stenosis
Heart Diseases
Mycardial Ischemia
Coronary Disease
Coronary Artery Disease
Relative similarity = 0.79
Neoplasms
Squamous cell carcinoma
Carcinoma
Glandular and Epithelial
Neoplasms
Neoplasms by Site
Neoplasms by Histologic Type
Neoplasms, Kidney
Neoplasms, Urologic
Neoplasms, Genitourinary
Relative similarity = 0.37
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Most approved drug indications have 1+ similar traits that have been investigated genetically
0
20
40
60
0.00 0.25 0.50 0.75 1.00Relative Similarity
Co
un
t
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Direct genetic evidence for approved target–indications varies significantly among disease areas
Test of heterogeneity among disease categories: p = 10 -12
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The percentage of targets with genetic evidence increases with later stages of clinical development
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Both Mendelian and complex trait genetics correlate with successful drug targets
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Genetic effect size is not related to approved drug statusEffect size does not predict likelihood of success
In collaboration with Brent Richards, McGill University 20
Target for approved drug with genetic evidence
The relative advantage of genetic association in predicting success is hampered by a lack of failure data
>127,000 drug trials
224 Target-indication failures due to efficacy
Failed Due to Efficacy
Trial Outcome Recorded
Phase 2 and/or 3
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 target-indication for Neoplasms
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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
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Increasing the proportion of drug targets with genetic evidence can lead to higher clinical success rates
Assume 15% of historic portfolio with genetic evidence
2-fold30%
with 95% confidence intervals (blue)
Uncovering the genetic contributions to human health will have measurable contributions to discovering new medicines
Publications
AssociationsP ≤ 5×10-8
New Unique Traits
Source: NHGRI/EBI GWAS Catalog, July 10, 2015 25
0
20
40
60
0.00 0.25 0.50 0.75 1.00Relative Similarity
Co
un
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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
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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
Thank you
Permutation algorithm
Variant-Trait
Gene-Trait
Drug-Indication
Gene-Indication
Gene-Trait-Indication
Merge on best-match trait-indication for
each gene
Compute overlap at several relative similarity and p-
value thresholds
Permute this relationship
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
Permutation test demonstrates enrichment of genetic associations among approved drugs is highly significant
Observed overlap
Result of 1000 permutations
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 findings31
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Genome-wide association studies identify genetic factors that affect human health
Schizophrenia Working group of the Psychiatric Genomics Consortium (2014) Nature 511:421-7
~37,000 schizophrenia cases~113,000 controls