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Using side effects for drug target identification
1.
Using side effects
for drug target identification Lars Juhl Jensen
2.
the problem
3.
new uses for
old drugs
4.
drug–drug network
5.
shared target(s)
6.
chemical similarity
7.
Campillos & Kuhn
et al., Science, 2008
8.
Campillos & Kuhn
et al., Science, 2008
9.
similar drugs share
targets
10.
only trivial predictions
11.
the idea
12.
chemical perturbations
13.
phenotypic readouts
14.
drug treatment
15.
side effects
16.
the hard work
17.
information on side
effects
18.
no database
19.
package inserts
20.
Campillos & Kuhn
et al., Science, 2008
21.
text mining
22.
side-effect ontology
23.
backtracking
24.
Campillos & Kuhn
et al., Science, 2008
25.
manual validation
26.
SIDER Kuhn et al.,
Molecular Systems Biology, 2010
27.
side-effect correlations
28.
Campillos & Kuhn
et al., Science, 2008
29.
GSC weighting
30.
side-effect frequencies
31.
Campillos & Kuhn
et al., Science, 2008
32.
raw similarity score
33.
Campillos & Kuhn
et al., Science, 2008
34.
p-values
35.
Campillos & Kuhn
et al., Science, 2008
36.
side-effect similarity
37.
chemical similarity
38.
Campillos & Kuhn
et al., Science, 2008
39.
confidence scores
40.
reference set
41.
incomplete databases
42.
text mining
43.
manual validation
44.
MATADOR Günther et al.,
Nucleic Acids Research, 2008
45.
Campillos & Kuhn
et al., Science, 2008
46.
text mining
47.
Reflect.ws
48.
Pafilis, O’Donoghue, Jensen
et al., Nature Biotechnology, 2009
49.
collaborate with industry
50.
51.
the results
52.
drug–drug network
53.
Campillos & Kuhn
et al., Science, 2008
54.
categorization
55.
Campillos & Kuhn
et al., Science, 2008
56.
20 drug–drug pairs
57.
in vitro binding
assays
58.
Ki<10 µM for
11 of 20
59.
cell assays
60.
9 of 9
showed activity
61.
the future
62.
link side-effects to
targets
63.
direct target prediction
64.
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
65.
larsjuhljensen
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