Predicting novel targets for existing drugs using side effect information

Lars Juhl Jensen

the problem

new uses for old drugs

drug–drug network

shared target(s)

chemical similarity

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

similar drugs share targets

only trivial predictions

the idea

chemical perturbations

phenotypic readouts

drug treatment

side effects

the implementation

information on side effects

package inserts

Campillos & Kuhn et al., Science, 2008

text mining

side-effect ontology

backtracking

Campillos & Kuhn et al., Science, 2008

side-effect correlations

Campillos & Kuhn et al., Science, 2008

GSC weighting

side-effect frequencies

Campillos & Kuhn et al., Science, 2008

raw similarity score

Campillos & Kuhn et al., Science, 2008

p-values

Campillos & Kuhn et al., Science, 2008

side-effect similarity

chemical similarity

Campillos & Kuhn et al., Science, 2008

reference set

drug–target pairs

Campillos & Kuhn et al., Science, 2008

drug–drug pairs

score bins

benchmark

Campillos & Kuhn et al., Science, 2008

fit calibration function

Campillos & Kuhn et al., Science, 2008

probabilistic scores

the results

drug–drug network

ATC codes

Campillos & Kuhn et al., Science, 2008

categorization

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

map onto score space

Campillos & Kuhn et al., Science, 2008

the experiments

20 drug–drug relations

in vitro binding assays

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

Ki<10 µM for 11 of 20

cell assays

Campillos & Kuhn et al., Science, 2008

9 of 9 showed activity

the future

target side-effect profiles

drug–target network

integration with STITCH

Acknowledgments

Monica Campillos

Michael Kuhn

Anne-Claude Gavin

Peer Bork