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,…

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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

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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