.

1. Enhancing Prioritization &
Discovery of Novel Combinations
using an HTS Platform
Rajarshi Guha
NIH NCATS
ACoP 7
Bellevue, WA

2. Screening for novel drug combinations
• Increased efficacy
• Delay resistance
• Attenuate toxicity
• Treat multiple aspects
of a disease
• Inform signaling
pathway connectivity
• Identify synthetic
lethality
• Polypharmacology
Translational Interest Basic Interest

3. Mechanism Interrogation PlateE
• 1911 small molecules, with a primary focus on
oncology, but also addressing infectious
disease and stem cell biology
• Diverse and redundant MoA’s
• Employed in 1-vs-all & all-vs-all modes
AMG-47a
Lck inhibitor
Preclinical
belinostat
HDAC inhibitor
Phase II
GSK-1995010
FAS inhibitor
Preclinical
Approved
Phase III
Phase II
Phase I
Preclinical
Other

4. High Throughput Combination Screening
Run single agent dose responses
6x6 matrices for
potential synergies
10x10 for confirmation
+ self-cross
Acoustic dispense, 15 min
for 1260 wells, 14 min for
1200 wells

5. Where are we now?
• 81 projects, 773 screens
• 140,730 combinations
• 4.8M wells
• 320 cell lines
• Opportunities to look at
global trends in combination
behavior in the context of
physicochemical properties,
biological functionality, …
0
50
100
150
200
2011 2012 2013 2014 2015 2016
Year
NumberCombinationScreens
• Cancers
• Hodgkins lymphoma
• DLBCL
• Neuroblastoma
• Leukemia
• Malaria
• Transcriptional mechanics
Baranello, L et al, Cell, 2016
Jun, W et al, PNAS, 2016
Lewis, R et al, J. Cheminf, 2015
Bogen, D et al, Oncotarget, 2015
Mott BT et al, Sci Rep, 2015
Zhang, M et al, PNAS, 2015
Ceribelli, M et al, PNAS, 2014
Mathews, L et al, PNAS 2014

6. Digging into the data
• Lots of data across lots of cell lines for lots of (mostly
annotated) compounds
• How can we slice & dice?
• How do we characterize quality of combination response?
• Are there global trends in synergy based on target class, MoA,
chemical structure/property?
• What is the role of selectivity vs promiscuity?
• What is the relation between single & combination responses?
• Can we better prioritize large sets of combinations?
• Can we find interesting subsets of combinations?
• Are there alternatives to the table view?
• How does (can) the data inform us on polypharmacology?
• How do we prospectively predict combination responses

7. Quantifying combination quality
• A key challenge is automated quality control
• Control separation
– control performance ≠ combination performance
• Intra-plate or inter-plate pattern
– no room for lots of replicates and
– the assumption used in primary screen can’t be satisfied
• Data consistency
– IC50 not always available (we are searching for synergy!)
– Consistent single agent IC50 ≠ consistent synergy
Lu Chen (NCATS)

8. Deviation of block
control
mQC: Interpretable QC model
Feature name Importance Explanation
dmso.v 20.71 Normalized response of the negative control
smoothness.p 18.88 p-value for smoothness
moran.p 18.82 p-value for spatial autocorrelation (tested by Moran’s I)
mono.v 12.62 Likelihood of monotonic dose responses
sa.min 12.84 The smaller relative standard deviation of the single-agent dose response
sa.matrix 8.78 The relative standard deviation of the dose combination sub-matrix
sa.max 7.36 The larger relative standard deviation of the single-agent dose response
Smoothness Randomness Monotonicity Activity variance
Feature importance encoded by mQC is consistent with human intuition
Chen, L. et al, Sci. Rep., submitted https://matrix.ncats.nih.gov/mQC/ Lu Chen (NCATS)

9. Visualization & Ranking
3D7 DD2 HB3
Azalomycin−B
ABT−263 (Navitoclax)
Cabozantinib
AZD−2014
Selumetinib
Volasertib
Midostaurin
SB−415286
IC−87114
GDC−0941
Neratinib
NCGC00021305
LY2157299
GMX−1778
PCI−32765
Torin−2
BEZ−235
Ruxolitinib
INK−128
Tipifarnib
MK−2206
PD 0325901
Imatinib
G−Strophanthin
Ketotifen
Clomipramine
NCGC00014925
2−Fluoroadenosine
MK−0752
Rolipram
Alvespimycin hydrochloride
Ganetespib
NCGC00183656
Sulindac
Carfilzomib
Bardoxolone methyl
LLL−12
JQ1
Suberoylanilide hydroxamic acid
Panobinostat
Azalo
m
ycin
−B
ABT−263
(N
avitocla
x)
C
abozantin
ib
AZD
−2014
Selu
m
etin
ib
Vola
sertib
M
id
ostaurin
SB−415286
IC
−87114
G
D
C
−0941
N
eratin
ib
N
C
G
C
00021305
LY2157299
G
M
X−1778
PC
I−32765
Torin
−2
BEZ−235
R
uxolitin
ib
IN
K−128
Tip
ifarnib
M
K−2206
PD
0325901
Im
atin
ib
G
−Strophanthin
Ketotifen
C
lo
m
ip
ram
in
e
N
C
G
C
00014925
2−Flu
oroadenosin
e
M
K−0752
R
olipram
Alvespim
ycin
hydrochlo
rid
e
G
anetespib
N
C
G
C
00183656
Sulindac
C
arfilzom
ib
Bardoxolo
ne
m
ethyl
LLL−12JQ
1
Suberoyla
nilid
e
hydroxam
ic
acid
Panobin
ostat
DBSumNeg
(−7,−4]
(−4,−3]
(−3,−2]
(−2,−1]
(−1,0]
Azalomycin−B
ABT−263 (Navitoclax)
Cabozantinib
AZD−2014
Selumetinib
Volasertib
Midostaurin
SB−415286
IC−87114
GDC−0941
Neratinib
NCGC00021305
LY2157299
GMX−1778
PCI−32765
Torin−2
BEZ−235
Ruxolitinib
INK−128
Tipifarnib
MK−2206
PD 0325901
Imatinib
G−Strophanthin
Ketotifen
Clomipramine
NCGC00014925
2−Fluoroadenosine
MK−0752
Rolipram
Alvespimycin hydrochloride
Ganetespib
NCGC00183656
Sulindac
Carfilzomib
Bardoxolone methyl
LLL−12
JQ1
Suberoylanilide hydroxamic acid
Panobinostat
Azalo
m
ycin
−B
ABT−263
(N
avitocla
x)
C
abozantin
ib
AZD
−2014
Selu
m
etin
ib
Vola
sertib
M
id
ostaurin
SB−415286
IC
−87114
G
D
C
−0941
N
eratin
ib
N
C
G
C
00021305
LY2157299
G
M
X−1778
PC
I−32765
Torin
−2
BEZ−235
R
uxolitin
ib
IN
K−128
Tip
ifarnib
M
K−2206
PD
0325901
Im
atin
ib
G
−Strophanthin
Ketotifen
C
lo
m
ip
ram
in
e
N
C
G
C
00014925
2−Flu
oroadenosin
e
M
K−0752
R
olipram
Alvespim
ycin
hydrochlo
rid
e
G
anetespib
N
C
G
C
00183656
Sulindac
C
arfilzom
ib
Bardoxolo
ne
m
ethyl
LLL−12JQ
1
Suberoyla
nilid
e
hydroxam
ic
acid
Panobin
ostat
DBSumNeg
(−7,−4]
(−4,−3]
(−3,−2]
(−2,−1]
(−1,0]
0.00.20.40.60.8

10. LogP & Synergy?
• Yilancioglu et al (JCIM 2014) suggested that you can
predict synergicity using only logP
• Synergicity of a compound is the frequency of synergistic
pairs involving the compound
Synergy doesn’t correlate with logP
10
20
30
-4 0 4 8
logP
Numberofsynergisticcombinations
Synergicity may correlate with logP
http://blog.rguha.net/?p=1265

11. Predicting Synergies
• Related to response surface methodologies
• Little work on predicting drug response surfaces
• Peng et al, PLoS One, 2011
• Boik & Newman, BMC Pharmacology, 2008
• Lehar et al, Mol Syst Bio, 2007 & Yin et al, PLoS One, 2014
• AZ-DREAM Challenge & Chen et al, PLoS Comp Bio, 2016
• But synergy is not always objective and doesn’t really
correlate with structure

12. -3
-2
-1
0
0.0 0.1 0.2 0.3 0.4
Tanimoto Similarity
DBSumNeg
Structural similarity vs synergy?
• Do structurally
similar compounds
lead to synergistic
combinations?
• No reason they
should
• Synergy driven by
(off-)targets

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

14. Predictive models (fail)
• 10x10, all-vs-all screen
• Random forest, ECFP6
• Predict value of a synergy metric
https://tripod.nih.gov/matrix-client/rest/matrix/blocks/1763/table
-10.0
-7.5
-5.0
-2.5
0.0
-10.0 -7.5 -5.0 -2.5 0.0
Observed DBSumNeg
PredictedDBSumNeg
Test
Train
0.8
0.9
1.0
1.1
1.2
1.3
0.8 0.9 1.0 1.1 1.2 1.3
Observed Beta
PredictedBeta
Test
Train

15. Descriptors matter
Cell
lines
from
data set
5 fold Multiple
splitting
80%,
training sets
20%,
validation sets
1) Different
descriptors
2) Selection of the
decision threshold
for each model
Models creation
Models validation
54 data sets,
127119 mixtures
Alexey Zakharov (NCATS)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
PEO1
RH30
RH41
JHH136
BIRCH
RH5
JHM1
MT1
SAOS2
Cal-1
PANC1
Cal27
UOK161
ipNF95.6
JHH520
TC71
FL3
KMS28BM_onyx
TMD8
DD2
RDES
L1236
SCC47
HFF
EW8
Rec-1
HF4B
Balanced Accuracy
QNA descriptors_RF RDkit_RF
(and classification is easier)

16. Explicitly consider targets
Descriptors used for learning
Three classes of descriptors generated per combination
• StructuralFingerprint
• Morgan, 2,048 bits, radius 2 (RDKit).
• PredictedTargets
• 1,080 human target probabilities of affinity
(PIDGIN V1)
• Combined
• StructuralFingerprint and PredictedTargets.
Input data required:
• Compound structure for training and test data (names, SMILES)
• Combination data (which compounds, synergy score)
Output:
• New combinations predicted to be synergistic
• Probability of being synergistic (classifier model,
worked best for this project)
• Predicted synergy value (quantitative model,
did not work so well for this project)
Dan Mason, Andreas Bender (U. Cambridge)

17. Going in vivo?
• Translating combinations to in vivo setting is complex
• How does PK/PD affect combinations?
• What dosing schedule works? Is it optimal?
• Currently an open question from computational PoV
• Lack of PK/PD parameters and ability to generate data are
critical bottlenecks
• We depend on clinician input & experience

18. Outlook
• Accurate predictions will enable virtual screening of
combinations
• Many aspects of the process are yet to be explored
• Differential analysis of combination response
• Are some pathways or mechanisms more amenable to
combination screening than others?
• Viability is easy to measure. What about other readouts?
• Is there a better way to characterize synergy?
• Tang, J. et al, Frontiers. Pharmacol., 2015
https://tripod.nih.gov/matrix-client

19. Acknowledgements
• Lu Chen
• Alexey Zakharov
• Kelli Wilson
• Mindy Davis
• Xiaohu Zhang
• Richard Eastman
• Bryan Mott
• Craig Thomas
• Marc Ferrer
• Paul Shinn
• Crystal McKnight
• Carleen Klumpp-
Thomas
• Anton Simeonov
• Dan Mason
• Rich Lewis
• Yasaman Kalantar
Motamedi
• Krishna Bulusu
• Andreas Bender