1. Characteriza*on
and
visualiza*on
of
compound
combina*on
responses
in
a
high
throughout
se8ng
Rajarshi
Guha,
Lesley
Mathews,
John
Keller,
Paul
Shinn,
Craig
Thomas,
Anton
Simeonov,
Marc
Ferrar
NIH-­‐NCATS
April
7,
2013,
New
Orleans

2. Outline
Why
combine?
Physical
infrastructure
&
workflow
Summarizing
and
exploring
the
data
hRp://origin.arstechnica.com/news.media/pills-­‐4.jpg

3. Screening
for
Novel
Drug
Combina*ons
• Drug
combina*ons
offer
advantages
for
both
efficacy
and
poten*al
reduc*on
of
target
related
toxici*es
• Combina*on
studies
also
offer
insight
into
systems
level
interac*ons

4. How
to
Test
Combina*ons
• Many
procedures
described
in
the
literature
– Fixed
dose
ra*o
(aka
ray)
– Ray
contour
C5,D5 C5
– Checkerboard
C4,D4 C4
– Gene*c
algorithm
C3,D3 C3
C2,D2 C2
C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1
D5 D4 D3 D2 D1 0

5. Scaling
Response
Surface
Screening
5e+07
Combination type
• Response
surfaces
All pairs
Fixed library
Dose matrix size
4e+07
imply
a
DxD
matrix
4
Number of combinations
6
10
for
each
combina*on
3e+07
• All
pairs
screening
is
2e+07
imprac*cal
for
more
1e+07
than
tens
of
0e+00
compounds
250 500 750
Number of compounds
1000
• Instead
we
consider
N
compounds
versus
a
fixed
size
library

6. Mechanism
Interroga*on
PlateE
Top
10
Panther
gene
classes
Top 10 Panther gene classes
200
kinase
nucleic acid binding
Number of compounds
150
receptor
signaling molecule
transferase
100
50
Top
10
enriched
GeneGo
pathway
maps
Development EGFR signaling pathway
0
Some pathways of EMT in cancer cells
&D
I
II
III
ed
al
t
Development VEGF signaling via VEGFR2 - generic cascades
d
en
e
ue
e
ic
as
e
ov
R
as
lim
lin
as
in
Ph
pr
Ph
ec
nt
Ph
pp
Ap
Apoptosis and survival Anti-apoptotic action of Gastrin
co
Pr
Su
is
D
Cell adhesion Chemokines and adhesion
Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling
Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR
Transcription PPAR Pathway
Translation Non-genomic (rapid) action of Androgen Receptor
Development VEGF signaling and activation
0 5 10 15
-log10(pValue)

7. Combina*on
Screening
Workflow
Run
single
agent
dose
responses
6x6
matrices
for
poten5al
synergies
10x10
for
confirma5on
+
self-­‐cross
Acoustic dispense, 15 min
for 1260 wells, 14 min for
1200 wells"

8. Repor*ng
Combina*on
Results

9. Repor*ng
Combina*on
Results

10. Repor*ng
Combina*on
Results
• These
web
pages
and
matrix
layouts
are
a
useful
first
step
• Does
not
scale
as
we
grow
MIPE
• S*ll
need
to
do
a
beRer
job
of
ranking
and
aggrega*ng
combina*on
responses
taking
into
account
– Response
matrix
– Compounds,
targets
and
pathways

11. A
Simpler
Visual
Summary
• Convert
mul*ple
individual
1 7 13 19 25 31
heatmaps,
to
a
single
heatmap
2
3
8
9
14
15
20
21
26
27
32
33
by
unrolling
response
matrices
4 10 16 22 28 34
• Examine
effects
of
A
at
fixed
5
6
11
12
17
18
23
24
29
30
35
36
concentra*ons,
on
dose
response
of
B
{1, 2, 3, 4, …, 34, 35, 36}
• Zoom
in
on
combina*ons
that
show
extensive
ac*vity
throughout
the
dose
matrix

12. A
Simpler
Visual
Summary
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Concentration Combination

13. When
are
Combina*ons
Similar?
• Differences
and
their
aggregates
such
as
RMSD
can
lead
to
degeneracy
0.06
• Instead
we’re
interested
in
0.04
0.010
the
shape
of
the
surface
0.005
0.02
0.00 0.000
0 25 50 75 100 0 25 50 75 100
• How
to
characterize
shape?
0.15
– Parametrized
fits
0.10
0.05
– Distribu*on
of
responses
0.00
0 50 100
D, p value

14. Similarity
via
the
KS
Test
• Quan*fy
distance
between
response
distribu*ons
via
KS
test
– If
p-­‐value
>
0.05,
we
assume
9
distance
is
0
• But
ignores
the
spa5al
density
6
distribu*on
of
the
responses
3
on
the
concentra*on
grid
0
0.00 0.25 0.50 0.75 1.00
D

15. Similarity
via
the
Syrjala
Test
• Syrjala
test
used
to
compare
10.0
popula*on
distribu*ons
over
a
spa*al
grid
7.5
– Invariant
to
grid
orienta*on
density
5.0
– Provides
an
empirical
p-­‐value
2.5
• Less
degenerate
than
just
considering
1D
distribu*ons
0.0
0.00 0.25 0.50 0.75
D
Syrjala,
S.E.,
“A
Sta*s*cal
Test
for
a
Difference
between
the
Spa*al
Distribu*ons
of
Two
Popula*ons”,
Ecology,
1996,
77(1),
75-­‐80

16. Datasets
• Primary
focus
is
on
inves*ga*ng
combina*ons
with
Ibru*nib
for
treatment
of
DLBCL
– Btk
inhibitor
– In
Phase
II
trials
– Experiments
run
in
the
TMD8
cell
line,
tes*ng
for
cell
viability

17. 0.8
Clustering
Response
Surfaces
C1
(24)
0.6
0.4
C3(35)
C2(47)
0.2
C4(24)
0.0

18. Cluster
C3
0.30
0.25
0.20
0.15
0.10
0.05
0.00
302
281
128
174
285
153
177
210
144
35
60
457
180
39
111
272
288
166
231
104
106
417
319
44
218
279
219
121
119
34
102
286
230
178
179
macromolecule catabolic process
regulation of interferon-gamma-mediated signaling pathway
• Vargatef,
vorinostat,
ubiquitin-dependent protein catabolic process
cellular process involved in reproduction flavopiridol,
…
negative regulation of cell cycle
peptidyl-amino acid modification
• Not
par*cularly
interphase specific
given
the
cell cycle checkpoint range
of
primary
peptidyl-tyrosine phosphorylation
response to stress
targets
0 1 2 3
-log10(Pvalue)

19. 0.08
0.06
0.04
0.02
0.00
361
Cluster
C4
254
215
164
143
82
125
327
241
194
145
116
139
371
163
165
384
339
322
217
184
150
52
136
cellular carbohydrate biosynthetic process
regulation of polysaccharide biosynthetic process
cellular macromolecule localization
• Focus
on
sugar
peptidyl-serine phosphorylation
metabolism
regulation of generation of precursor metabolites and energy • Ruboxistaurin,
cellular polysaccharide metabolic process cycloheximide,
2-­‐
glucan metabolic process
methoxyestradiol,
…
glucan biosynthetic process
regulation of glycogen biosynthetic process • PI3K/Akt/mTOR
glycogen metabolic process signalling
pathways
0 1 2 3
-log10(Pvalue)

20. Combina*ons
across
Cell
Lines
• Cellular
background
affects
responses
• Can
we
group
cell
lines
based
on
combina*on
response?

21. Working
in
Combina*on
Space
• Each
cell
line
is
represented
as
a
vector
of
response
matrices
L 1
L2
• “Distance”
between
two
,
=
d1
cell
lines
is
a
func*on
of
the
distance
between
component
,
=
d2
response
matrices
,
=
d3
D ( L1, L2 ) = F({d1, d2 ,…, dn }) ,
=
d4
• F
can
be
min,
max,
mean,
…
,
=
d5

22. 0.00 0.05 0.10 0.15 0.20 0.25 0 1 2 3 4
INA-6 KMS-34
MM-MM1 INA-6
min
sum
8226 L363
XG-1 OPM-1
U266 XG-2
ANBL-6 FR4
SKMM-1 AMO-1
EJM XG-6
OPM-1 MOLP-8
XG-2 ANBL-6
OCI-MY1 KMS-20
KMS-20 XG-7
L363 OCI-MY1
KMS-11LB XG-1
AMO-1 8226
XG-6 EJM
FR4 U266
KMS-34 KMS-11LB
MOLP-8 SKMM-1
XG-7 MM-MM1
0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.1 0.2 0.3 0.4 0.5 0.6
L363 L363
OPM-1 OPM-1
euc
max
XG-2 XG-2
KMS-34 KMS-20
INA-6 XG-1
KMS-11LB XG-7
SKMM-1 ANBL-6
EJM OCI-MY1
U266 U266
MM-MM1 XG-6
FR4 INA-6
AMO-1 MOLP-8
XG-6 AMO-1
Many
Choices
to
Make
8226 KMS-34
MOLP-8 KMS-11LB
ANBL-6 SKMM-1
OCI-MY1 MM-MM1
XG-1 EJM
KMS-20 FR4
XG-7 8226

23. Exploi*ng
Polypharmacology
• Vargatef
exhibited
anomalous
matrix
response
compared
to
other
VEGFR
inhibitors
Linifanib Axitinib Sorafenib Vatalanib
Motesanib Tivozanib Brivanib Telatinib
Cabozantinib Cediranib BMS-794833 Lenvatinib
OSI-632 Foretinib Regorafenib
Vargatef

24. Exploi*ng
Polypharmacology
Vargatef DCC-2036 PD-166285 GDC-0941
• PD-­‐166285
is
a
SRC
&
FGFR
inhibitor
PI-103 GDC-0980 Bardoxolone methyl AT-7519
AT7519
• Lestaurnib
has
ac*vity
against
FLT3
SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024
Src
Lyn
Lck
ISOX Belinostat PF-477736 AZD-7762
Flt-3
PDGFRb
PDGFRa
FGFR-4
FGFR-3
FGFR-2 Chk1 IC50 = 105 nM
FGFR-1
VEGFR-3
VEGFR-2
VEGFR-1
0 200 400 600
Potency (nM)
Hilberg,
F.
et
al,
Cancer
Res.,
2008,
68,
4774-­‐4782

25. Predic*ng
Synergies
• Related
to
response
surface
methodologies
• LiRle
work
on
predic*ng
drug
response
surfaces
– Peng
et
al,
PLoS
One,
2011
– Jin
et
al,
Bioinforma5cs,
2011
– Boik
&
Newman,
BMC
Pharmacology,
2008
– Lehar
et
al,
Mol
Syst
Bio,
2007
• But
synergy
is
not
always
objec*ve
and
doesn’t
really
correlate
with
structure

26. Structural
Similarity
vs
Synergy
beta gamma
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
Similarity
0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05
ssnum Win 3x3
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0 5 10 15 20 25 -40 -30 -20 -10 0
Synergy measure

27. Predic*on
Strategy
• Don’t
directly
predict
synergy
• Use
single
agent
data
to
generate
a
model
surface
• Predict
combina*on
responses
• Characterize
synergy
of
predicted
response
with
respect
to
model
surface
• Reduced
to
a
mixture
predic*on
problem
• Will
likely
be
beRer
addressed
by
(also)
considering
target
connec*vity

28. Conclusions
• Use
response
surfaces
as
first
class
descriptors
of
drug
combina*ons
– Surrogate
for
underlying
target
network
connec*vity
(?)
• Response
surface
similarity
based
on
distribu*ons
is
(fundamentally)
non-­‐parametric
• Going
from
single
-­‐
chemical
space
to
combina*on
space
opens
up
interes*ng
possibili*es
• Manual
inspec*on
is
s*ll
a
vital
step

29. Acknowledgements
• Lou
Staudt
• Beverly
Mock,
John
Simmons