Exploring Compound Combina1ons in High Throughput Se9ngs Going Beyond 1D Metrics Rajarshi Guha NCATS June 2014, Novar:s, Boston.
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D Metrics
Aug 23, 2014
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Exploring Compound Combina1ons in High Throughput Se9ngs
Going Beyond 1D Metrics
Rajarshi Guha NCATS
June 2014, Novar:s, Boston.
Background
• Cheminforma:cs methods – QSAR, diversity analysis, virtual screening, fragments, polypharmacology, networks
• More recently – RNAi screening, high content imaging, combina:on screening
• Extensive use of machine learning • All :ed together with soMware development – User-‐facing GUI tools – Low level programma:c libraries, APIs, databases
• Believer & prac::oner of Open Source
Outline
hUp://origin.arstechnica.com/news.media/pills-‐4.jpg
Why combine?
Physical infrastructure & workflow
Summarizing and exploring the data
Screening for Novel Drug Combina1ons
• Increased efficacy • Delay resistance • AUenuate toxicity
• Inform signaling pathway connec:vity
• Iden:fy synthe:c lethality • Highlight polypharmacology
Transla5onal Interest Basic Interest
How to Test Combina1ons
• Many procedures described in the literature – Fixed dose ra:o (aka ray) – Ray contour – Checkerboard – Gene:c algorithm
C5,D5 C5
C4,D4 C4
C3,D3 C3
C2,D2 C2
C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1
D5 D4 D3 D2 D1 0
Mechanism Interroga1on PlateE • Collec:on of ~ 2000 small molecules of diverse mechanism of ac:on. • 745 approved drugs • 420 phase I-‐III inves:ga:onal drugs • 767 preclinical molecules
• Diverse and redundant MOAs represented
AMG-47a Lck inhibitor Preclinical
belinostat HDAC inhibitor Phase II
Eliprodil NMDA antagonist Phase III
JNJ-38877605 HGFR inhibitor Phase I
JZL-184 MAGL inhibitor Preclinical
GSK-1995010 FAS inhibitor Preclinical
Development VEGF signaling and activation
Translation Non-genomic (rapid) action of Androgen Receptor
Transcription PPAR Pathway
Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR
Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling
Cell adhesion Chemokines and adhesion
Apoptosis and survival Anti-apoptotic action of Gastrin
Development VEGF signaling via VEGFR2 - generic cascades
Some pathways of EMT in cancer cells
Development EGFR signaling pathway
0 5 10 15-log10(pValue)
Mechanism Interroga1on PlateE Top 10 enriched GeneGo pathway maps
Combina1on Screening Workflow
Run single agent dose responses
6x6 matrices for poten1al synergies
10x10 for confirma1on + self-‐cross
Acoustic dispense, 15 min for 1260 wells, 14 min for
1200 wells"
Where Are We Now?
• 382 screens in total – 65,960 combina:ons – 3,024,224 wells
• 244 cell lines – Various cancers – Mainly human
• Combined with target annota:ons we can look at combina:on behavior as a func:on of various factors
0
50
100
150
0 500 1000 1500 2000Number of combinations
Num
ber o
f ass
ays
Screening Challenges
• A key challenge is automated quality control • Plate level data employs standard metrics focusing on control performance
• Combina:on level is more challenging – Single agent performance is one approach
– MSR across all combina:on can provide a high level view
– But how to iden:fy bad blocks?
QC Examples
• Inves:ga:ng an:-‐malarial combina:ons • 300 10x10 combina:ons in duplicate • 15 compounds included more than ten :mes
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
Artemether Artesunate Dihydroartemisinin
Halofantrine Lumefantrine
log
IC50
(uM
)
0 5 10 15 20
MSR
Compound
10
20
30
40Freq
QC Examples
• Single agents with very high MSR’s could be used to flag combina:ons containing them
• Doesn’t help for compounds with only one or two replicates
QC Score
A heuris:c score that can be used to focus on good quality combina:ons
Acceptable DMSO response
Valid single agent curve fit & IC50
Sufficient variance in dose sub-matrix
Spatial autocorrelation in dose sub-matrix
Acceptable single agent efficacy
0
250
500
750
0 2 3 5 6 7 8 10 11 12 13 15 16QC Score
Frequency
Strain3D7
DD2
HB3
QC Score QCS = 0
QCS = 13 QCS = 2
• Depends on mul:ple subjec:ve thresholds
• Passes some poor quality blocks
• Quickly filters out very bad combina:ons
Repor1ng Combina1on Results
Repor1ng Combina1on Results
Repor1ng Combina1on Results
• These web pages and matrix layouts are a useful first step
• Does not scale as we grow MIPE • Need beUer ways of ranking and aggrega:ng combina:on responses taking into account – Response matrix – Compounds, targets and pathways – Clinical status and other external informa:on
Network Representa1ons
Combina:on screens lend themselves naturally to network representa:ons
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∆ Bliss+
−4.3
−3.8
−3.3
−2.9
−2.4
−1.9
−1.4
−1.0
−0.5
0.0
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∆ Bliss+
−3.4−3.1
−2.7
−2.3
−1.9
−1.5−1.2
−0.8
−0.4
0.0
immune system process
apoptotic process
transcription from RNApolymerase II promoter
protein phosphorylation
cell communication
immune response
Network Representa1ons
• Things get more interes:ng when we have n m screens
• Can be simplified using a variety of methods – Neighborhoods – Minimum Spanning Tree
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×
Comparing Neighborhoods
Combina:ons that have DBSumNeg < 1st quar:le value for that strain
3D7 DD2 HB3
Comparing Neighborhoods
Alterna:vely, consider all tested combina:ons, highligh:ng distribu:on of synergis:c and antagonis:c combina:ons
3D7 DD2 HB3
Iden1fying the Most Synergis1c Pairs
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When are Combina1ons Similar?
• Differences and their aggregates such as RMSD can lead to degeneracy
• Instead we’re interested in the shape of the surface
• How to characterize shape? – Parametrized fits – Distribu:on of responses
0.000
0.005
0.010
0 25 50 75 100
0.00
0.02
0.04
0.06
0 25 50 75 100
0.00
0.05
0.10
0.15
0 50 100
D, p value
0
3
6
9
0.00 0.25 0.50 0.75 1.00D
density
Similarity via the KS Test
• Quan:fy distance between response distribu:ons via KS test – If p-‐value > 0.05, we assume distance is 0
• But ignores the spa1al distribu:on of the responses on the concentra:on grid
0.0
2.5
5.0
7.5
10.0
0.00 0.25 0.50 0.75D
density
Similarity via the Syrjala Test
• Syrjala test used to compare popula:on distribu:ons over a spa:al grid – Invariant to grid orienta:on – Provides an empirical p-‐value
• Less degenerate than just considering 1D distribu:ons
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
Ibru1nib Combina1ons For DLBCL
• 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
Mathews-‐Griner, Guha, Shinn et al. PNAS, 2014, in press
Viable Cells
(% DMSO)
Ibrutinib* (nM) MK-2206 (µM)
Ibrutinib
MK-2206
Ibrutinib* + MK-2206
Clustering Response Surfaces 0.0
0.2
0.4
0.6
0.8
C1 (24)
C2(47)
C3(35)
C4(24)
response to stress
peptidyl-tyrosine phosphorylation
cell cycle checkpoint
interphase
peptidyl-amino acid modification
negative regulation of cell cycle
cellular process involved in reproduction
ubiquitin-dependent protein catabolic process
regulation of interferon-gamma-mediated signaling pathway
macromolecule catabolic process
0 1 2 3-log10(Pvalue)
Cluster C3
• Vargatef, vorinostat, flavopiridol, …
• Not par:cularly specific given the range of primary targets
0.00
0.05
0.10
0.15
0.20
0.25
0.30
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
Cluster C4
• Focus on sugar metabolism
• Ruboxistaurin, cycloheximide, 2-‐methoxyestradiol, …
• PI3K/Akt/mTOR signalling pathways glycogen metabolic process
regulation of glycogen biosynthetic process
glucan biosynthetic process
glucan metabolic process
cellular polysaccharide metabolic process
regulation of generation of precursor metabolites and energy
peptidyl-serine phosphorylation
cellular macromolecule localization
regulation of polysaccharide biosynthetic process
cellular carbohydrate biosynthetic process
0 1 2 3-log10(Pvalue)
0.00
0.02
0.04
0.06
0.08
361
254
215
164
143 82 125
327
241
194
145
116
139
371
163
165
384
339
322
217
184
150 52 136
Combina1ons across Cell Lines
• Cellular background affects responses • Can we group cell lines based on combina:on response?
• Or find “fingerprints” that characterize cell lines?
Working in Combina1on Space
• Each cell line is represented as a vector of response matrices
• “Distance” between two cell lines is a func:on of the distance between component response matrices
• F can be min, max, mean, …
L1 L2
= d1
= d2
= d3
= d4
= d5
D L1,L2( ) = F({d1,d2,…,dn})
,
,
, , ,
Many Choices to Make 0
12
34
KMS-34
INA-6
L363
OPM-1
XG-2
FR4
AMO-1
XG-6
MOLP-8
ANBL-6
KMS-20
XG-7
OCI-MY1
XG-1
8226
EJM
U266
KMS-11LB
SKMM-1
MM-MM1
sum
0.0
0.1
0.2
0.3
0.4
0.5
0.6
L363
OPM-1
XG-2
KMS-20
XG-1
XG-7
ANBL-6
OCI-MY1
U266
XG-6
INA-6
MOLP-8
AMO-1
KMS-34
KMS-11LB
SKMM-1
MM-MM1
EJM FR4
8226
max
0.00
0.05
0.10
0.15
0.20
0.25
INA-6
MM-MM1
8226
XG-1
U266
ANBL-6
SKMM-1
EJM
OPM-1
XG-2
OCI-MY1
KMS-20
L363
KMS-11LB
AMO-1
XG-6
FR4
KMS-34
MOLP-8
XG-7
min
0.0
0.2
0.4
0.6
0.8
1.0
1.2
L363
OPM-1
XG-2
KMS-34
INA-6
KMS-11LB
SKMM-1
EJM
U266
MM-MM1
FR4
AMO-1
XG-6
8226
MOLP-8
ANBL-6
OCI-MY1
XG-1
KMS-20
XG-7
euc
• Vargatef exhibited anomalous matrix response compared to other VEGFR inhibitors
Exploi1ng Polypharmacology
Vargatef
Linifanib Axitinib Sorafenib Vatalanib
Motesanib Tivozanib Brivanib Telatinib
Cabozantinib Cediranib BMS-794833 Lenvatinib
OSI-632 Foretinib Regorafenib
Exploi1ng Polypharmacology
• PD-‐166285 is a SRC & FGFR inhibitor
• Lestaurnib has ac:vity against FLT3
Vargatef DCC-2036 PD-166285 GDC-0941
PI-103 GDC-0980 Bardoxolone methyl AT-7519AT7519
SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024
ISOX Belinostat PF-477736 AZD-7762
Chk1 IC50 = 105 nM
VEGFR-1
VEGFR-2
VEGFR-3
FGFR-1
FGFR-2
FGFR-3
FGFR-4
PDGFRa
PDGFRb
Flt-3
Lck
Lyn
Src
0 200 400 600Potency (nM)
Hilberg, F. et al, Cancer Res., 2008, 68, 4774-‐4782
Predic1ng Synergies
• Related to response surface methodologies • LiUle work on predic:ng drug response surfaces – Peng et al, PLoS One, 2011 – Jin et al, Bioinforma1cs, 2011 – Boik & Newman, BMC Pharmacology, 2008 – Lehar et al, Mol Syst Bio, 2007 & Yin et al, PLoS One, 2014
• But synergy is not always objec:ve and doesn’t really correlate with structure
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 0Synergy measure
Similarity
Predic1on 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 • Need to incorporate target connec:vity
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
Acknowledgements
• Lou Staudt • Beverly Mock, John Simmons • Lesley Griner, Craig Thomas, Marc Ferrer, Bryan MoU, Paul Shinn, Sam Michaels
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