enhancing prioritization & discovery of novel combinations using an hts platform
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EnhancingPrioritization&DiscoveryofNovelCombinations
usinganHTSPlatform
RajarshiGuhaNIHNCATS
ACoP 7Bellevue,WA
Screeningfornoveldrugcombinations
• Increasedefficacy• Delayresistance• Attenuatetoxicity• Treatmultipleaspectsofadisease
• Informsignalingpathwayconnectivity• Identifysyntheticlethality• Polypharmacology
TranslationalInterest BasicInterest
Mechanism Interrogation PlateE• 1911smallmolecules,withaprimaryfocusononcology,butalsoaddressinginfectiousdiseaseandstemcellbiology• DiverseandredundantMoA’s• Employedin1-vs-all&all-vs-allmodes
AMG-47aLck inhibitorPreclinical
belinostatHDAC inhibitorPhase II
GSK-1995010FAS inhibitorPreclinical
Approved
Phase III
Phase II
Phase I
Preclinical
Other
HighThroughputCombinationScreening
Runsingleagentdoseresponses
6x6matricesforpotentialsynergies
10x10forconfirmation+self-cross
Acoustic dispense, 15 min for 1260 wells, 14 min for
1200 wells
Wherearewenow?• 81projects,773screens• 140,730combinations• 4.8Mwells
• 320celllines• Opportunitiestolookatglobaltrendsincombinationbehaviorinthecontextofphysicochemicalproperties,biologicalfunctionality,…
0
50
100
150
200
2011 2012 2013 2014 2015 2016Year
Num
ber C
ombi
natio
n S
cree
ns
• Cancers• Hodgkins lymphoma• DLBCL• Neuroblastoma• Leukemia
• Malaria• Transcriptionalmechanics
Baranello,Letal,Cell,2016Jun,Wetal,PNAS,2016Lewis,Retal,J.Cheminf,2015Bogen,Detal,Oncotarget,2015
MottBTetal,SciRep,2015Zhang,Metal,PNAS,2015Ceribelli,Metal,PNAS,2014Mathews,Letal,PNAS2014
Diggingintothedata• Lotsofdataacrosslotsofcelllinesforlotsof(mostlyannotated)compounds• Howcanweslice&dice?• Howdowecharacterizequalityofcombinationresponse?• Arethereglobaltrendsinsynergybasedontargetclass,MoA,chemicalstructure/property?• Whatistheroleofselectivityvspromiscuity?• Whatistherelationbetweensingle&combinationresponses?• Canwebetterprioritizelargesetsofcombinations?• Canwefindinterestingsubsetsofcombinations?• Aretherealternativestothetableview?• Howdoes(can)thedatainformusonpolypharmacology?• Howdoweprospectivelypredictcombinationresponses
Quantifyingcombinationquality
• Akeychallengeisautomatedqualitycontrol• Controlseparation
– controlperformance≠combinationperformance
• Intra-plateorinter-platepattern– noroomforlotsofreplicatesand– theassumptionusedinprimaryscreencan’tbesatisfied
• Dataconsistency– IC50 notalwaysavailable(wearesearchingforsynergy!)– ConsistentsingleagentIC50 ≠consistentsynergy
LuChen(NCATS)
Deviationofblockcontrol
mQC:InterpretableQCmodel
Feature name Importance Explanationdmso.v 20.71 Normalized response of the negative controlsmoothness.p 18.88 p-value for smoothnessmoran.p 18.82 p-value for spatial autocorrelation (tested by Moran’s I)mono.v 12.62 Likelihood of monotonic dose responsessa.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-matrixsa.max 7.36 The larger relative standard deviation of the single-agent dose response
Smoothness Randomness Monotonicity Activityvariance
FeatureimportanceencodedbymQCisconsistentwithhumanintuition
Chen,L.etal,Sci.Rep.,submitted https://matrix.ncats.nih.gov/mQC/ LuChen(NCATS)
Visualization&Ranking
3D7 DD2 HB3
Azalomycin−BABT−263 (Navitoclax)
CabozantinibAZD−2014
SelumetinibVolasertib
MidostaurinSB−415286
IC−87114GDC−0941
NeratinibNCGC00021305
LY2157299GMX−1778PCI−32765
Torin−2BEZ−235
RuxolitinibINK−128TipifarnibMK−2206
PD 0325901Imatinib
G−StrophanthinKetotifen
ClomipramineNCGC00014925
2−FluoroadenosineMK−0752Rolipram
Alvespimycin hydrochlorideGanetespib
NCGC00183656Sulindac
CarfilzomibBardoxolone methyl
LLL−12JQ1
Suberoylanilide hydroxamic acidPanobinostat
Azalom
ycin−
B
ABT−26
3 (Nav
itocla
x)
Caboz
antin
ib
AZD−20
14
Selumeti
nib
Volas
ertib
Midosta
urin
SB−41
5286
IC−87
114
GDC−09
41
Neratin
ib
NCGC0002
1305
LY21
5729
9
GMX−17
78
PCI−327
65
Torin−2
BEZ−23
5
Ruxolit
inib
INK−12
8
Tipifarn
ib
MK−22
06
PD 0325
901
Imati
nib
G−Stro
phan
thin
Ketotife
n
Clomipr
amine
NCGC0001
4925
2−Fluo
roade
nosin
e
MK−07
52
Rolipram
Alvesp
imyci
n hyd
rochlo
ride
Ganete
spib
NCGC0018
3656
Sulinda
c
Carfilzo
mib
Bardox
olone
meth
yl
LLL−
12JQ1
Subero
ylanili
de hy
droxa
mic acid
Panob
inosta
t
DBSumNeg(−7,−4](−4,−3](−3,−2](−2,−1](−1,0]
Azalomycin−BABT−263 (Navitoclax)
CabozantinibAZD−2014
SelumetinibVolasertib
MidostaurinSB−415286
IC−87114GDC−0941
NeratinibNCGC00021305
LY2157299GMX−1778PCI−32765
Torin−2BEZ−235
RuxolitinibINK−128TipifarnibMK−2206
PD 0325901Imatinib
G−StrophanthinKetotifen
ClomipramineNCGC00014925
2−FluoroadenosineMK−0752Rolipram
Alvespimycin hydrochlorideGanetespib
NCGC00183656Sulindac
CarfilzomibBardoxolone methyl
LLL−12JQ1
Suberoylanilide hydroxamic acidPanobinostat
Azalom
ycin−
B
ABT−26
3 (Nav
itocla
x)
Caboz
antin
ib
AZD−20
14
Selumeti
nib
Volas
ertib
Midosta
urin
SB−41
5286
IC−87
114
GDC−09
41
Neratin
ib
NCGC0002
1305
LY21
5729
9
GMX−17
78
PCI−327
65
Torin−2
BEZ−23
5
Ruxolit
inib
INK−12
8
Tipifarn
ib
MK−22
06
PD 0325
901
Imati
nib
G−Stro
phan
thin
Ketotife
n
Clomipr
amine
NCGC0001
4925
2−Fluo
roade
nosin
e
MK−07
52
Rolipram
Alvesp
imyci
n hyd
rochlo
ride
Ganete
spib
NCGC0018
3656
Sulinda
c
Carfilzo
mib
Bardox
olone
meth
yl
LLL−
12JQ1
Subero
ylanili
de hy
droxa
mic acid
Panob
inosta
t
DBSumNeg(−7,−4](−4,−3](−3,−2](−2,−1](−1,0]
0.0
0.2
0.4
0.6
0.8
LogP &Synergy?
• Yilancioglu etal(JCIM2014)suggestedthatyoucanpredictsynergicity usingonly logP• Synergicity ofacompoundisthefrequencyofsynergisticpairsinvolvingthecompound
Synergydoesn’tcorrelatewithlogP
10
20
30
-4 0 4 8logP
Num
ber o
f syn
ergi
stic
com
bina
tions
Synergicitymay correlatewithlogPhttp://blog.rguha.net/?p=1265
PredictingSynergies
• Relatedtoresponsesurfacemethodologies• Littleworkonpredictingdrugresponsesurfaces• Pengetal,PLoSOne,2011• Boik&Newman,BMCPharmacology,2008• Leharetal,MolSystBio,2007 &Yinetal,PLoSOne,2014• AZ-DREAMChallenge &Chenetal,PLoSCompBio,2016
• Butsynergyisnotalwaysobjectiveanddoesn’treallycorrelatewithstructure
-3
-2
-1
0
0.0 0.1 0.2 0.3 0.4Tanimoto Similarity
DBSumNeg
Structuralsimilarityvssynergy?
• Dostructurallysimilarcompoundsleadtosynergisticcombinations?• Noreasontheyshould• Synergydrivenby(off-)targets
Structuralsimilarityvssynergy?
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
Predictivemodels(fail)
• 10x10,all-vs-allscreen• Randomforest,ECFP6• Predictvalueofasynergymetric
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 0.0Observed DBSumNeg
Pre
dict
ed D
BS
umN
eg
Test
Train
0.8
0.9
1.0
1.1
1.2
1.3
0.8 0.9 1.0 1.1 1.2 1.3Observed Beta
Pre
dict
ed B
eta
Test
Train
Descriptorsmatter
Celllinesfrom
dataset
5foldMultiplesplitting
80%,trainingsets
20%,validationsets
1)Differentdescriptors
2)Selectionofthedecisionthresholdforeachmodel
Modelscreation
Modelsvalidation54datasets,127119mixtures
AlexeyZakharov (NCATS)
0.000.100.200.300.400.500.600.700.800.901.00
PEO1
RH30
RH41
JHH1
36BIRC
HRH
5JHM1
MT1
SAOS2
Cal-1
PANC1
Cal27
UOK1
61ipNF95.6
JHH5
20TC
71 FL3
KMS28B
M_o
nyx
TMD8 DD2
RDES
L123
6SCC4
7HF
FEW
8Re
c-1
HF4B
Balanced
Accuracy
QNAdescriptors_RF RDkit_RF
(andclassificationiseasier)
Explicitlyconsidertargets
DescriptorsusedforlearningThreeclassesofdescriptorsgeneratedpercombination• StructuralFingerprint
• Morgan,2,048bits,radius2(RDKit).• PredictedTargets
• 1,080humantargetprobabilitiesofaffinity(PIDGINV1)
• Combined• StructuralFingerprint andPredictedTargets.
Inputdatarequired:• Compoundstructurefortrainingandtestdata(names,SMILES)• Combinationdata(whichcompounds,synergyscore)
Output:• Newcombinationspredictedtobesynergistic• Probabilityofbeingsynergistic(classifiermodel,
workedbestforthisproject)• Predictedsynergyvalue(quantitativemodel,
didnotworksowellforthisproject)
DanMason,AndreasBender(U.Cambridge)
Goinginvivo?
• Translatingcombinationstoinvivosettingiscomplex• HowdoesPK/PDaffectcombinations?• Whatdosingscheduleworks?Isitoptimal?
• CurrentlyanopenquestionfromcomputationalPoV• LackofPK/PDparametersandabilitytogeneratedataarecriticalbottlenecks
• Wedependonclinicianinput&experience
Outlook
• Accuratepredictionswillenablevirtualscreeningofcombinations• Manyaspectsoftheprocess areyettobeexplored• Differentialanalysisofcombinationresponse• Aresomepathwaysormechanismsmoreamenabletocombinationscreeningthanothers?• Viabilityiseasytomeasure.Whataboutotherreadouts?• Isthereabetterwaytocharacterizesynergy?• Tang,J.etal,Frontiers.Pharmacol.,2015
https://tripod.nih.gov/matrix-client
Acknowledgements
• LuChen• AlexeyZakharov• KelliWilson•MindyDavis• Xiaohu Zhang• RichardEastman• BryanMott• CraigThomas•MarcFerrer
• PaulShinn• CrystalMcKnight• CarleenKlumpp-Thomas• AntonSimeonov• DanMason• RichLewis• Yasaman KalantarMotamedi• KrishnaBulusu• AndreasBender
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