The impact of large-scale genetic data on drug targets –From 1000 genomes to Drug Discovery

Josef Scheiber, PhDwww.biovariance.com

10th International Conference on Chemical Structures10th German Conference on Chemoinformatics

June 1-5 2014, NoordwijkerhoutThe Netherlands

Overview

• General Introduction• Approaches taken with learnings and results• Further possibilities

Significant unmet medical need

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

diseases

Dr

ug re

spon

sera

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NSAIDS 80 % response rate

Alzheimer 25 % response rate

Several thousand diseases withoutknown treatment

Biological/PharmacologicalUnderstanding

drugs

targets pathways

diseases

Disease understanding getting better and better

2010

1970

1960

1950Disease of the

Blood

Leukemia

ChronicLeukemia

AcuteLeukemia

Preleukemia

Lymphoma

Indolent Lymphoma

Aggressive Lymphoma

Increasing understanding of underlying biologyopens up new hypotheses –

even/particularly for Cheminformatics!

5 YearSurvival~ 0 %

~ 70%

Example: Leukemia and Lymphoma

From single targets to …

… polypharmacology to…

Major driver

… individual polypharmacology

The human genome contains roughly 3 billion nucleotides and the genomes of any 2 individuals vary in 3 million of them

A significant likelihood that individuals respond differently to the same medicine

This is rooted in differences for drug absorption, distribution, metabolism and excretion

There are ~7 billion human genomes and each responds differently to drugs

- Cystic fibrosis caused by defects in the CFTR ion channel (orange in diagram) - Just under 5% of cases are caused by the amino acid glycine being replaced by

an aspartate in position 551 of the CFTR protein - patients with this mutation are unable to transport chloride to the CFTR

Why this study?A single mutation can have massive impact

Example: Carbamazepine/Steven Johnsons Syndrome

Courtesy: Dr. Thomas Habifdermnet.com

Why this study?A single mutation can have massive impact

Difference in European and Korean populations

HLA allele B*1502

The molecular reason behind

From Wei, CY et al.Direct interaction between HLA-B and carbamazepine activates T cells in patients with Stevens-Johnson syndrome

Our large-scale analysis

Workflow (simplified)

Call Variants for eachdrug target

Compare intraindividual variability with a focus on binding sites

Raw Data Analysis

Image Processing and base calling

Whole Genome MappingAlignment to

reference genome

Variant Calling

Detection of genetic variation (SNP, CNV etc.)

AnnotationLinking variants to

biological information

BioVariance focus

Basic NGS workflow

Classes of structural variation

Alkan, C. et al. Genome structural variation discovery and genotyping. Nature Reviews Genetics 12, 363-376 (2011).

Single Nucleotide AberrationsSingle Nucleotide Polymorphisms (SNPs)Single Nucleotide Variations (SNVs)

Short Insertions or Deletions (indels)Larger Structural Variations (SVs)

Workflow

Identify drug targets(primary and off-targets,

from DrugBank)

Call variations on a per-individuum basis

Workflow

Analyse mutation rates in the targets and in

particular drug bindingpockets

Example: Donepezil / Acetylcholinesterase

• PDB 4EY7

Image extracted from Cheung et al.,

2012 [2]

Example: Donepezil / Acetylcholinesterase

Example: Acetylcholinesterase

Integrative Genomics Viewer

Not very successful

Alignment of the 3D structures of mutant number 52 (yellow) and PDB 4EY7 AChE protein (green). The only changed residue is the Y150 (magenta) to H150 (red). The white surface represents the molecular surface of donepezil.

Why is this a bad example?

AChE a key enzyme in human biology these arethe most highly conserved, even interspecies

Learning: Look at that stuff before investingtime

CFTR

• 1000 genomes are not enough for finding thesecases

SLCO1B1 – Simvastatin example

- Mutation 37041T>C or V174A, is a SNP in this gene, which encodes the 'organic anion transporting polypeptide 1B1' (OATP1B1) protein.

- found primarily in the liver, it regulates the uptake of numerous drugs and natural compounds. The rs4149056(C) SNP defines the SLCO1B1*5 allele.

- This allele described by mentioned amino acid change has reduced uptake/transport activity

- Therefore, drugs metabolized by OATP1B1 tend to build up to higher circulating concentrations than they would otherwise

SLCO1B1 – Simvastatin example

- 17fold higher probability for Rhabdomyolosis for statin patients (6fold if one allele)

Ultimately: An individual profile

What analyses are enabled?

• For Drug Design: Do we work on the „right“ targetand specifically on the right pocket to design compounds?

• Is there a specific population where the drug will work best?

• Are there off-targets that can be problematic in certain cases?

• Certainly good for ligand-based approaches as well…

Project: Personalized Medicine forchildren

Thank you for your attention!

josef.scheiber@biovariance.comPhone: +49 – 89 – 189 6582 – 80Garmischer Str. 4/V80339 Munich / Germany