An integrated dataset for in silico drug discovery

Simon J Cockell

Drug Repositioning Novel drugs

10 – 15 years $500 million - $2

billion Most candidates fail

in or before clinic Repositioning

Bypasses many pre-approval tests

Faster Cheaper

http://www.recyclenow.com

Serendipity or Design? High profile

repositioning resulting from chance discoveries: Viagra Zyban Thallidomide

A more rational approach is desirable

Can we exploit data to find new uses for old drugs?

http://commons.wikimedia.org/wiki/File:Viagra_in_Pack.jpg

Ondex Data integration &

visualisation platform

Everything as a network

Nodes & edges annotated with metadata

Backed by data model & ontology

http://www.flickr.com/photos/sjcockell/4405616339/

Ondex

Input Integration Analysis

Parse

Parse

Parse

Map

Map

Map Visualise

Filter

Annotate

Ondex & Repositioning Integrate Drug and

Target information Add further info:

Protein similarity Small molecule

similarity Protein families Metabolic pathways

Look for known examples

http://www.ondex.org/

Data

DrugBank UniProt HPRD KEGG BLAST 2D-Tanimoto PFam G-Sesame SymAtlas

Workflow

2D-Tanimoto

G-Sesame

DrugBank

KEGG

HPRD

Parse

Map

UniProt

Dataset v1

Dataset v2

SymAtlas

http://bsu.ncl.ac.uk/ondex/ib2010_data.xml.gz

Chlorpromazine

http://en.wikipedia.org/wiki/File:Chlorpromazine-3D-balls.png

Chlorpromazine Known interactions

with 3 targets Serum Albumin D(2) Dopamine

Receptor 5-Hydroxytryptamine

2A Receptor Structurally similar to

4 other drugs Trimeprazine Prochlorperazine Perphenazine Promazine

Chlorpromazine

Serum Albumin

D(2) Dopamine Receptor

5-Hydroxytryptamine 2A Receptor

- Drug

- Target

- Protein

- has similar structure

- has similar sequence

- is a

Chlorpromazine

Semantic Motifs

Potential Interaction

Trimeprazine

Chlorpromazine

Histamine H1 Receptor

drug (2)

drug (1)

targetbinds to

similar to

similar to

binds to

Semantic motifs

Protein 1

Protein 2

Disease 1

Disease 2

Drug A

Drug B

Conclusions Integrated dataset can be used to find

repositioning examples

Added semantic richness allows semantic motifs to be defined

Semantic motif search + scoring algorithm for automated repositioning search

New targets?

Acknowledgements Newcastle

Phillip Lord Jochen Weile Matthew Pocock Darren Wilkinson Jennifer Hallinan Anil Wipat

e-Therapeutics Dmytro Andrychenko Claire Wipat Malcolm Young

BBSRC Grant # BB/F006039/1

Everyone involved in the Ondex project See here: http://ondex.org/people.html

http://twitter.com/sjcockell