Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform
1 like879 views
This document summarizes work done using a high throughput screening (HTS) platform to discover novel drug combinations. Over 140,000 combinations across 320 cell lines have been screened involving 1911 small molecules focused on oncology, infectious disease, and stem cell biology. Analysis of the large dataset seeks to identify global trends in synergistic combinations based on targets, mechanisms of action, and physicochemical properties. Challenges include accurately characterizing combination quality and predicting synergies. Ongoing work involves exploring differential responses, other readout measures beyond viability, and translating combinations to in vivo models.
![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](https://image.slidesharecdn.com/acop7-guha-sl-161024181745/85/Enhancing-Prioritization-Discovery-of-Novel-Combinations-using-an-HTS-Platform-9-320.jpg)
Enhancing Prioritization & Discovery of Novel Combinations using an HTS Platform
- 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
- 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