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
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1569accuracy = 81.00%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1954accuracy = 80.89%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
KPL4accuracy = 82.98%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MCF7accuracy = 80.30%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 157accuracy = 81.59%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 231accuracy = 79.95%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
SKBR3accuracy = 81.00%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
T47Daccuracy = 80.42%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1569accuracy = 81.87%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1954accuracy = 83.04%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
KPL4accuracy = 79.53%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MCF7accuracy = 81.87%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 157accuracy = 55.62%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 231accuracy = 91.81%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
SKBR3accuracy = 77.78%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
T47Daccuracy = 78.36%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1569accuracy = 81.00%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1954accuracy = 80.89%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
KPL4accuracy = 82.98%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MCF7accuracy = 80.30%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 157accuracy = 81.59%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 231accuracy = 79.95%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
SKBR3accuracy = 81.00%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
T47Daccuracy = 80.42%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1569accuracy = 81.87%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
HCC1954accuracy = 83.04%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
KPL4accuracy = 79.53%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MCF7accuracy = 81.87%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 157accuracy = 55.62%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
MDA 231accuracy = 91.81%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
SKBR3accuracy = 77.78%
actin
auro
ra
dna
dam
aging
kinas
e
micr
otub
ule
prot
ein d
eg
prot
ein sy
nth
statin
actin
aurora
dna damaging
kinase
microtubule
protein deg
protein synth
statin
T47Daccuracy = 78.36%
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)
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)
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
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