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Identification of drug targets from side-effect similarity
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 hard work
information on side effects
no database
package inserts
Campillos & Kuhn et al., Science, 2008
text mining
side-effect ontology
backtracking
Campillos & Kuhn et al., Science, 2008
manual validation
SIDER
Kuhn et al., Molecular Systems Biology, 2010
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
confidence scores
reference set
incomplete databases
text mining
manual validation
MATADOR
Günther et al., Nucleic Acids Research, 2008
Campillos & Kuhn et al., Science, 2008
text mining
reflect.ws
Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
collaborate with industry
the results
drug–drug network
Campillos & Kuhn et al., Science, 2008
categorization
Campillos & Kuhn et al., Science, 2008
20 drug–drug pairs
in vitro binding assays
Ki<10 µM for 11 of 20
cell assays
9 of 9 showed activity
the future
link side-effects to targets
direct target prediction
Acknowledgments
Side effects– Monica Campillos
– Michael Kuhn
– Anne-Claude Gavin– Peer Bork
Reflect.ws– Heiko Horn
– Sune Frankild
– Evangelos Pafilis– Reinhardt Schneider– Sean O’Donoghue
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