advances in phenotypic screening: accelerating the ...quantitative pharmacology in complex models...

Edinburgh Cancer Research UK Centre MRC Institute of Genetics and Molecular Medicine

www.igmm.ac.uk

Advances in Phenotypic Screening: Accelerating the Discovery of New Chemical Entities and

Drug Combinations toward in vivo proof-of-concept

NeilCarragher([email protected])

UniversityofEdinburgh

Topics

1.   UniversityofEdinburgh“PhenomicsDrugDiscovery”

2.Multiparametrichighcontentanalysisacrossgeneticallydistinctcells:

GENOTYPE-TO-PHENOTYPE

3.HighContent-CaseStudies(INVIVOproof-of-concept):

1.   NovelKinaseInhibitorDrugCombinationDiscovery(NovelFAKinhibitorcombinations)

2.   RapidDiscoveryofNewChemicalEntitieswithinvivoefficacy(BreastCancer)

Image-informatics Novel in vitro Bioassays ImageXpress-XL

IncuCyte Zoom & NPSC

Pathway-profiling

Infra Red detection

710IR

Solid pin tool arrayer

RPPA NGS Proteomics New cell assay technologies

High-Content Imaging Developed together with clinician scientists to represent

key segments of disease pathophysiology Quantitative pharmacology

in complex models

Edinburgh Phenomics Drug Discovery

SGCchemicalprobes

(12k)120,000

PrestwickFDA/EMA(1,280)

Annotated(>200)Sub-libraries

EPDDcompoundset

AsierUnciti-Broceta

Innovative

Therapeuticslab

NewChemicalEntities/Patents

Chemicalprobes/marketed:

http://www.chemicalprobes.org/search/site/mTOR

https://www.axonmedchem.com/product/2630

Poster'1015'–'find'out'about'joining'PDI'

Bestinclassphenotypicset

Outputs:NovelCell/TissueBasedScreeningAssays

Identify/Validatenoveltargets

Confirm:Drug/TargetMechanism

Identify:DrugResponseBiomarkers

Anticipate:DrugResistanceMechanisms

Identify:RationaleDrugCombinations

IndustryPartnerships

Analysis:

CellProfiler

(340features)

14,000small

molecules

ImageXpress–PAArobotic

PhenotypicFingerprints

HighContentPhenotypicProfiling

MachinelearningpredictMOA

actin

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HCC1569accuracy = 81.00%

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KPL4accuracy = 82.98%

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MCF7accuracy = 80.30%

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MDA 157accuracy = 81.59%

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SKBR3accuracy = 81.00%

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T47Daccuracy = 80.42%

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Pixels:

Deeplearning/

CNNclassifier:

predictMOA

ImageXpress–PAArobotic

HighContentPhenotypicProfiling

ScottWarchal

MoApredictionwhentrainedon7cell-linesandtestedonan“unseen”cell-line

FeatureExtraction

Ensemblebasedtreeclassifier

DeepLearning

ResNet18CNNclassifier

ScottWarchal

θ

θ

Theta Comparative Cell Scoring: “TCCS” Warchal et al., Assay Drug Dev. Tecnol. 2016 Sep;14(7)

High Content Phenotypic Profiling across Genetically Distinct Cell Types

Serotonin Receptor Modulators

Differential Cellular Phenotypic Responses: across genetically distinct breast cancer cell types

T47D

DMSO

Protriptyline Triflupromazine

DMSO DMSO

Protriptyline Triflupromazine

DMSO

HCC1954

Differential Cellular Phenotypic Responses: Protriptyline & Triflupromazine

NanoString Analysis

NanoString & Reverse Phase Protein Array (RPPA): Network Analysis

Infra Red detection

710IR

Solid pin tool arrayer

•  High Throughput and Quantitative •  >560 post translational markers validated: (285 phospho, 60 histone modifications (Met,Ac) •  Small sample required (96-well / FNA) •  = 11,500 western blots overnight

pAKT-ser473:

AF-BSA

RFI value: QC

RPPA Analysis

UpregulatedbyTriflupromazine

#enrichedgenes category description FDRvalue

19 ReactomePathways SignalingbyInterleukins 8.98E-18

30 ReactomePathways SignalTransduction 3.1E-15

26 ReactomePathways ImmuneSystem 3.06E-14

12 ReactomePathways CellularSenescence 1.27E-13

10 ReactomePathways TollLikeReceptor3(TLR3)Cascade 1.27E-12

10 ReactomePathways TRIF(TICAM1)-mediatedTLR4signaling 1.27E-12

14 ReactomePathways Cellularresponsestostress 2.68E-12

9 ReactomePathways

MyD88cascadeinitiatedonplasma

membrane 1.39E-11

10 ReactomePathways DeathReceptorSignalling 1.81E-11

9 ReactomePathways

TRAF6mediatedinductionofNFkBand

MAPkinasesuponTLR7/8or9activation 1.87E-11

9 ReactomePathways

MyD88:MAL(TIRAP)cascadeinitiatedon

plasmamembrane 2.07E-11

6 ReactomePathways RAF-independentMAPK1/3activation 4.62E-10

5 ReactomePathways

ActivationoftheAP-1familyof

transcriptionfactors 1.87E-09

8 ReactomePathways Interleukin-4andInterleukin-13signaling 1.87E-09

7 ReactomePathways MAPkinaseactivation 2.26E-09

6 ReactomePathways

MAPKtargets/Nucleareventsmediatedby

MAPkinases 2.42E-09

7 ReactomePathways

DDX58/IFIH1-mediatedinductionof

interferon-alpha/beta 6.93E-09

7 ReactomePathways

Senescence-AssociatedSecretory

Phenotype(SASP) 7.95E-09

6 ReactomePathways IntrinsicPathwayforApoptosis 1.26E-08

15 ReactomePathways InnateImmuneSystem 2.02E-08

7 ReactomePathways SignalingbyNTRKs 3.04E-08

8 ReactomePathways Apoptosis 3.29E-08

DownregulatedbyTriflupromazine

#enrichedgenes category description FDRvalue

34 ReactomePathways CellCycle 7.33E-43

25 ReactomePathwaysG1/STransition 2.64E-41

25 ReactomePathways SPhase 1.21E-39

30 ReactomePathways CellCycle,Mitotic 1.09E-37

26 ReactomePathways CellCycleCheckpoints 1.66E-36

22 ReactomePathwaysDNAReplication 1.7E-35

22 ReactomePathwaysG2/MCheckpoints 7.35E-34

20 ReactomePathways SynthesisofDNA 8.57E-32

23 ReactomePathways TranscriptionalRegulationbyTP53 8.66E-28

21 ReactomePathwaysDNARepair 4.15E-26

18 ReactomePathwaysDNADouble-StrandBreakRepair 7.77E-26

17 ReactomePathwaysHomologyDirectedRepair 2.36E-25

16 ReactomePathways

HDRthroughHomologousRecombination(HRR)or

SingleStrandAnnealing(SSA) 1.03E-23

27 ReactomePathwaysGenericTranscriptionPathway 6.87E-23

14 ReactomePathways ProcessingofDNAdouble-strandbreakends 9.93E-22

14 ReactomePathwaysDNAReplicationPre-Initiation 1.1E-21

16 ReactomePathways RegulationofTP53Activity 1.23E-21

12 ReactomePathwaysActivationofATRinresponsetoreplicationstress 2.04E-21

14 ReactomePathways RegulationofTP53ActivitythroughPhosphorylation 4.1E-21

13 ReactomePathwaysHDRthroughHomologousRecombination(HRR) 6.11E-21

13 ReactomePathways CyclinEassociatedeventsduringG1/Stransition 6.47E-20

13 ReactomePathways CyclinA:Cdk2-associatedeventsatSphaseentry 8.32E-20

NanoString: Network Analysis

Drug discovery & development in oesophageal cancer an alternative phenotypic-led approach

Cancer Research UK Edinburgh Centre MRC Institute of Genetics & Molecular Medicine

at the University of Edinburgh

www.ed.ac.uk/cancer-centre

Oesophageal Cancer: •  7th most common cancer worldwide

•  6th most common cause of cancer mortality

•  aggressive disease, spreading quickly and a 5 year survival rate of 20%, incidence rates in western countries increasing

•  treatment options are limited: surgery combined with standard chemotherapy and/or radiotherapy: 'unmet clinical need'

•  very heterogeneous disease; no useful molecular biomarkers to guide treatment or provide insight into 'druggable' targets

Richard Elliott Becka Hughes

Rebecca Fitzgerald, Ted Hupp, Rob O’Neill

FLO1, 1991 OAC-P4C, 1996 MFD1, 2015 OE33, 1993

SKGT4, 1989

Drug discovery & development in oesophageal cancer: -Use of a cell line panel that represents the heterogeneity of the disease -20K chemical diversity libraries

EPC2, 2007 (normal epithelium)

CPA, 1995 (Barrett's Oesophagus)

JHesoAD1, 1997

Becka Hughes

FLO1, 1991 OAC-P4C, 1996 MFD1, 2015 OE33, 1993

SKGT4, 1989 EPC2, 2007 (normal epithelium)

CPA, 1995 (Barrett's Oesophagus)

JHesoAD1, 1997

tissue matched, non-transformed control cells

Drug discovery & development in oesophageal cancer: -Use of a cell line panel that represents the heterogeneity of the disease -20K chemical diversity libraries

Becka Hughes

Bespoke collection(s): 693 FDA approved drugs (Prestwick): 1280

LOPAC (Sigma): 1280 BioAscent Chemical Diversity set: 3200

CRT Chemical diversity: 13408 -------------------------------------------------

total: 19,861 compounds across 8 cell lines (159K representative datset)

signal transduction pathway inhibitors, etc – pre-clinical/in

clinical trials/biologically active tool compounds

Drug Repositioning opportunities (off patent drugs)

•  novel synergistic combinations with standard of care

therapies

Novel Therapeutics

Drug discovery & development in oesophageal cancer: -Use of a cell line panel that represents the heterogeneity of the disease -20K chemical diversity libraries (each)

High content multiparametric phenotypic profiling of oesophageal panel response to compound library screening:

Machine Learning MOA Prediction

morphological profiling = Mahalanobis

Hierarchical clustering

Becka Hughes

Novel kinase inhibitor Combination Discovery Platform

New focal adhesion kinase (FAK) inhibitor combinations

Novel kinase inhibitor Combination Discovery Platform

CASE STUDY 2

ProbableATPbindingsites•  428–434•  454(KDmutant)•  500-502

ActiveSite(protonacceptor)•  546

FERM=35–355Kinase=422-680FAT=C-terminus

IGEGQFG GXGXFG–CDC37consensusbindingmotif

KeyregulatorofATPloading

FAKG431A,F433Akinasedomain.....tocomplementK454RKinase-deficient

FAK G431A F433A Margaret Frame John Dawson

Margaret Frame John Dawson

Conventional kinase dead (?!) mutation:

Novel mutation that more accurately mimics an irreversible ATP competitive kinase inhibitor:

1.  Wild-Type FAK 2.  Novel Mutant FAK 3.  Traditional Kinase Dead FAK 4.  FAK Null

Genetically engineered Squamous Cell Cancer panel:

Multiparametric High-content phenotypic screen for novel synergy with mutant kinase (= novel kinase inhibitor drug combination opportunities)

14,000 small-molecules 1,280 FDA; 12,000 diversity, 250 reference

kinase domain

xxx

Lys Arg

kinase domain

Novel Kinase Inhibitor Combination Discovery Platform

Raf1 Ptpn14

Pkn2 Pkn1 Pgls Nckap1 Mb21d2 Sipa1l1 Eif2b4

Iqgap3 Nmd3 Kifc1 Prim1 Smc2 Sf3b14 Xrn1 Lage3 Dnajc7 Anln Ncaph Mapre2 Pola1 Kif4 Prkra Zfp2 Hectd1 Ube2o Arhgap10 Pdcl Gsk3b Cnn2 Scyl2 Map1s Fads2 Cdc37 Wdr6

WT FAKi

G431A

KD

WT FAKi

G431A

KD

WT FAKi

G431A

KD

CDC37

CDC37

FAK

FAK

FAK-Wt

FAK-/-

FAK-G4

31AF433A

FAK-KD

FAK

IP

Lysa

tes

CDC37

Control

V1

V2

Pf

GSK

FAK

FAK

FAKinhibitors

250nMCompound24hrsTreatment

CDC37

FAK pY397

250nM (24hrs)

G431A, F433A

Focal Adhesion Kinase (FAK)

ImageXpress Data Processing (multiparametric

phenotypic analysis)

Incubated for 24 hours

Incubated for 24 hours

Image Analysis Image Acquisition Compound Addition Plate Cells [Genetic cell series]

Cells fixed and labelled with Hoechst, Phalloidin and

HCS Cell Mask

Collagen I coated 384 well plates

FAK (chemical-genetic) combination phenotypic screens……

BioMek FX

Basic analysis: QC

Cell Number Cell-Cycle Distribution

Clustering and classification of similar / disimilar compound

activity/cell phenotypes Principle Component Analysis

Compound Libraries:

13,977 Compounds Tested •  80 Kinase Inhib. •  53 Protease Inhib. •  43 Epigenetic Inhib. •  41 SGC chemical probes •  1,280 FDA Approved Agents

•  12K Bioascent Chemical Diversity Library

Targets known (supposedly).

Targets unknown –novel inhibitor development.

180 x 384 Well Plates: 16,896 Wells (1,216,512 images) 1,408 DMSO Wells 704 STS Wells 704 PAC Wells 144 Untreated Wells

John Dawson

High content identification of synergistic drug combinations

FAK and HDAC inhibitor combinations effectively inhibit SCC growth in 3D spheroid model

VS-4718 FAK inhibitor (Verastem Oncology)

Identification of cell lines sensitive to FAK and HDAC inhibitor combinations

Lung Adenocarcinoma Oesophageal Adenocarcinoma

Over another 172 potential novel FAK inhibitor combinations to validate!

Dose

Squamous Cell Carcinoma SCC Xenograft

Dose

in vivo proof-of-concept…..

Lung Adenocarcinoma A549 Xenograft

Oesophageal Adenocarcinoma Flo1 Xenograft

0.000

100.000

200.000

300.000

400.000

500.000

600.000

700.000

800.000

900.000

1000.000

0 2 5 7 9 12 15 19 22

Volum

e(mm3)

Day

Control

Panobinostat

VS-4718

Combination

0.000

100.000

200.000

300.000

400.000

500.000

600.000

700.000

800.000

900.000

1000.000

0 2 5 7 9 12 15 19 22

Volume(m

m3)

Day

Control

Panobinostat

VS-4718

Combination

***

A. Unciti-Broceta L. Patton C. Fraser C. Temps

CASE STUDY 3: Rapid Discovery of New Chemical Entities

A B Competitor

Target knock-out

Patent pending; Manuscript in preparation

Target ID kinome panel: Highly specific Src inhibitor: no dual activity upon Abl

Zebrafish Neuromast cell migration assay

High content cell cycle/apoptosis/migration assays

Discovery of novel Src Inhibitor class via a dual Ligand-Based-Phenotypic screening strategy. Craig Fraser, Jason Weiss, John Dawson, Liz Patton, Neil Carragher and Asier Unciti-Broceta

Rapid Discovery of New Chemical Entities – eCF506

Dasatanib eCF506

Discovery of novel Src Inhibitor class via a dual Ligand-Based-Phenotypic screening strategy. Craig Fraser, Jason Weiss, John Dawson, Liz Patton, Neil Carragher and Asier Unciti-Broceta

•  Highly specific Src inhibitor: no dual activity upon Abl •  Rapid and cost effective development of NCE with potent and specific cancer cell activities, excellent

physiochemical/ADME properties and oral bioavailability.

UK patent application (GB1508747.1) / Composition of matter for medical use ;

p416Src p416Src

Fraser et al., J Med Chem. 2016 May 26;59(10):4697-710.

Rapid Discovery of New Chemical Entities – eCF506

ECDU/EPAC: Uni. of Edinburgh: Chemistry John Dawson Margaret Frame Asier Unciti-Broceta Kenny Macleod Val Brunton Craig Fraser Dahlia Doughty-Shenton Kev Dahliwal Carolin Temps Ashraff Makda Bryan Serrels Alison Munro Steve Pollard, Paul Brennan Richard Elliott Alan Serrels Informatics Scott Warchal Takanori Kitamura Guido Sanguinetti Pierre Rome Alex Von Kriegsheim Stuart Aitken John Marwick Siddharthan Chandran, Dario Magnani Becka Hughes Ted Hupp, Rob O’Neill Leolie Telford-Hughes

NPSC: Andrew Hopkins; Daniel Ebner, Paul Andrews, Den Barrault

Acknowledgements Acknowledgements

Anne Forrest Fund for Oesophageal Cancer Research

advances in phenotypic screening: accelerating the ...quantitative pharmacology in complex models...

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