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

2

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

5

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

7

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

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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

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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