berg ellen 7th braz medchem 12nov2014
TRANSCRIPT
Human Cell Systems for Drug Discoveryand Chemical Safety
Ellen L. Berg, Scientific Director
The 7th Brazilian Symposium on Medicinal Chemistry Campos do Jordão-SP, Brazil, November 9-12, 2014
Agenda
• Challenges in pharmaceutical research
• Primary human cell systems – BioMAP
platform
• Case studies
- Understanding ADRs - thrombosis-related side
effects
- Drug combinations
2
• Problem:
- Pharmaceutical productivity is at an all time low
- We are swimming in oceans of data
• A need for new approaches
- Better physiological relevance
- More predictive of clinical effects
Challenges in Drug Discovery
We need to do something different: A Turning Point
3
Complexity of Biology
Scale (meters)
molecules pathways cells tissues humans
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
Human exposureMolecular targets
4
• Human biology is complex
- Modular, redundant, highly networked, & full of feedback loops
Complexity of Biology
Scale (meters)
molecules pathways cells tissues humans
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
Human exposureMolecular targets
5
• Human biology is complex
- Modular, redundant, highly networked, & full of feedback loops
• Prediction (and understanding!) is difficult
- Emergent properties
Primary human cell systems
Solution: Primary Human Cell Systems
• BioMAP® Profiling:
- In Vitro testing in primary human cell based tissue and disease models
• Data driven chemical biology approach
- Data-driven research methodology
- Leverages the analysis of a large chemical biology dataset
• Applications in drug discovery
- Compound characterization across a broad range of biology
- Drug mechanisms of action – anchored on clinical outcomes
- Guidance for translational studies, indications & biomarkers
Confidential6
Data Driven Research
NEW
010101010101010101001010101010101
100101001110010100110100101001110
001000100011101001010001000100011
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
101010101010001110100101010101010
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
001000100011101001010001000100011
010101010101010101001010101010101
100101001110010100110100101001110
001000100011101001010001000100011
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
101010101010001110100101010101010
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
001000100011101001010001000100011
010101010101010101001010101010101
100101001110010100110100101001110
001000100011101001010001000100011
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
101010101010001110100101010101010
111010010101010101000110100101010
101010101010001110101010000110100
010000110100010001001111010010101
001000100011101001011101010101010
001000100011101001010001000100011
Hypothesis 1
Hypothesis 2
Hypothesis 3
Hypothesis 4 . . .
OLD or
Data Driven Research
IssuesMany hypotheses are generated
Each hypothesis requires validation
Validation requires both computational
and “domain” expertise
SolutionIncorporate “domain” expertise upfront
BioMAP® Technology Platform
BioMAP®
Assay Systems
Reference
Profile Database
Predictive
Informatics Tools
Human primary cells
Disease-models
30+ systems
Biomarker responses to drugs
are stored in the database
>3000 drugs
Custom informatics tools are
used to predict clinical outcomes
High Throughput Human Biology
10
BioMAP® Systems – Key Features
11
Primary human cell types
Physiologically relevant “context”
Complex activation settings
Co-cultures
Translational biomarker endpoints
Feature Mice ManLifespan 2 Years 70 Years
Size 60 g 60 kg
EnvironmentAnimal facility, cage-mates
Outside world, people, animals, etc.
Why Human?
Key differences:
DNA repair mechanisms
Control of blood flow, hemostasis
Immune system status
12
Closer to the disease process
Downstream of multiple pathways and integrate information
“Decision-making”
Used by clinicians to guide therapy - Provide clinical “line of site”
Predictive
Why Translational Biomarkers?
mRNA,epigenome
Phospho-sites, intracellular proteins,
metabolome
Cell surface,secreted molecules
13
Panel of BioMAP® Systems3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
Endothelial Cells
Endothelial Cells
PBMC + Endothelial
Cells
PBMC + Endothelial
Cells
Bronchial epithelial cells
Coronary artery SMC
FibroblastsKeratinocytes + Fibroblasts
Th1 Th2 TLR4 TCR Th1 Th1 Th1 + GF Th1 + TGF
Acute Inflammation E-selectin, IL-8
E-selectin, IL-1a, IL-8, TNF-
a, PGE2 IL-8 IL-1a
IL-8, IL-6, SAA
IL-8 IL-1α
Chronic Inflammation
VCAM-1, ICAM-1, MCP-1, MIG
VCAM-1, Eotaxin-3,
MCP-1
VCAM-1, MCP-1
MCP-1, E-selectin, MIG
IP-10, MIG, HLA-DR
MCP-1, VCAM-1,MIG, HLA-
DR
VCAM-1, IP-10, MIG
MCP-1, ICAM-1, IP-10
Immune Response HLA-DR CD40, M-CSFCD38, CD40, CD69, T cell
Prolif., Cytotox.HLA-DR M-CSF M-CSF
Tissue Remodeling uPAR, MMP-1, PAI-1, TGFb1, SRB, tPA, uPA
uPAR,
Collagen III, EGFR, MMP-1, PAI-1, Fibroblast
Prolif., SRB, TIMP-1
MMP-9, SRB, TIMP-2, uPA,
TGFβ1
Vascular Biology
TM, TF, uPAR, EC
Proliferation, SRB, Vis
VEGFRII, uPAR, P-
selectin, SRB
Tissue Factor, SRB
SRB
TM, TF, LDLR, SMC
Proliferation, SRB
Vascular Biology,
Cardiovascular
Disease, Chronic
Inflammation
Asthma, Allergy,
Oncology,
Vascular Biology
Cardiovascular
Disease, Chronic
Inflammation,
Infectious Disease
Autoimmune
Disease, Chronic
Inflammation,
Immune Biology
COPD,
Respiratory,
Epithelial Biology
Vascular Biology,
Cardiovascular
Inflammation,
Restenosis
Tissue Remodeling,
Fibrosis, Wound
Healing
Skin
Biology,Psoriasis,
Dermatitis
En
dp
oin
t Ty
pe
s
Disease / Tissue Relevance
BioMAP System
Primary Human Cell Types
Stimuli
! ! ! ! !
Endothelial Cells
Bronchial Epithelial Cells
Keratinocytes
Smooth Muscle Cells
Dermal Fibroblasts
Peripheral Blood Mononuclear Cells
Profile compounds
across a panel of assays
14
Panel of BioMAP® Systems3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
Endothelial Cells
Endothelial Cells
PBMC + Endothelial
Cells
PBMC + Endothelial
Cells
Bronchial epithelial cells
Coronary artery SMC
FibroblastsKeratinocytes + Fibroblasts
Th1 Th2 TLR4 TCR Th1 Th1 Th1 + GF Th1 + TGF
Acute Inflammation E-selectin, IL-8
E-selectin, IL-1a, IL-8, TNF-
a, PGE2 IL-8 IL-1a
IL-8, IL-6, SAA
IL-8 IL-1α
Chronic Inflammation
VCAM-1, ICAM-1, MCP-1, MIG
VCAM-1, Eotaxin-3,
MCP-1
VCAM-1, MCP-1
MCP-1, E-selectin, MIG
IP-10, MIG, HLA-DR
MCP-1, VCAM-1,MIG, HLA-
DR
VCAM-1, IP-10, MIG
MCP-1, ICAM-1, IP-10
Immune Response HLA-DR CD40, M-CSFCD38, CD40, CD69, T cell
Prolif., Cytotox.HLA-DR M-CSF M-CSF
Tissue Remodeling uPAR, MMP-1, PAI-1, TGFb1, SRB, tPA, uPA
uPAR,
Collagen III, EGFR, MMP-1, PAI-1, Fibroblast
Prolif., SRB, TIMP-1
MMP-9, SRB, TIMP-2, uPA,
TGFβ1
Vascular Biology
TM, TF, uPAR, EC
Proliferation, SRB, Vis
VEGFRII, uPAR, P-
selectin, SRB
Tissue Factor, SRB
SRB
TM, TF, LDLR, SMC
Proliferation, SRB
Vascular Biology,
Cardiovascular
Disease, Chronic
Inflammation
Asthma, Allergy,
Oncology,
Vascular Biology
Cardiovascular
Disease, Chronic
Inflammation,
Infectious Disease
Autoimmune
Disease, Chronic
Inflammation,
Immune Biology
COPD,
Respiratory,
Epithelial Biology
Vascular Biology,
Cardiovascular
Inflammation,
Restenosis
Tissue Remodeling,
Fibrosis, Wound
Healing
Skin
Biology,Psoriasis,
Dermatitis
En
dp
oin
t Ty
pes
Disease / Tissue Relevance
BioMAP System
Primary Human Cell Types
Stimuli
! ! ! ! !
15
• Challenges
- Cells and assays are expensive
- Primary cells (all cell-based assays!) are variable
- Very large number of assay components / choices
• Cell types, media, additives, time points, endpoints
Experimental Design
16
• Solutions
- Automation
• Microwell plate-based
- Standardized methods
• Quality management system (SOPs)
• Strict assay acceptance criteria
- Incorporate methods to reduce variability
• Cells from pooled donors, prequalified
• Normalize data within plate (Log10 ratio of compound/vehicle)
• 6+ vehicle replicates, two positive controls per plate
Experimental Design
17
• Compromises:
- Single well per endpoint, but:
• Multiple concentrations (4+) per compound
• Multiple assay systems per compound
• Multiple endpoints per assay system
- Single timepoint
• Suboptimal for some endpoints, but optimal for most endpoints (24 hr – 6 days)
- Pauciparameter (7-22 endpoints per assay system), but:
• Highly informative disease biomarker endpoints
Experimental Design
18
BioMAP Profile of Positive Control
• Colchicine is an inhibitor of microtubules - It is active in every system and used as a positive control on every plate
• Colchicine profile has a distinctive pattern of activities or “shape”
BioMAP Systems
Readout Parameters (Biomarkers)
Cytotoxicity Readouts
Colchicine 1.1 μM
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
Vehicle Control
(no drug)
95%
significance
envelope
19
Reproducibility of Profiles
• 16 Experiments over many months
• Pairwise correlation of profiles (Pearson’s) were > 0.8
BioMAP Systems
Readout Parameters (Biomarkers)
Houck, K.A., J. Biomolecular Screening, 2009, 14:1054-66.20
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
Vehicle Control
(no drug)
95%
significance
envelope
• Assess cytotoxicity in primary human cells
- Cytotoxicity mechanisms are cell type and activation dependent
- Note: cytotoxicity is a confounder• Flag compounds (concentrations) that are overtly cytotoxic
• Analyze overall activity profiles
- Profile characteristics
- Unsupervised and supervised approaches to compare profiles
• Focus on individual endpoints
- Correlate to external data
- Build an understanding of clinical mechanisms
What Can We Do With BioMAP Profile Data?
21
Types of BioMAP Profiles
InactiveActive – Sharp dose-response
Active – Dose resistantActive – Selectively
22
Rapamycin (mTOR) Genistein (multi-target)
Dose ResistanceA Profile “Characteristic”
• “Dose resistant” compounds have similar activity profiles over a wide range of concentrations- No sharp activity jumps; Rapamycin > Genistein
• Characteristic of approved drugs & target-selective compounds- Rapamycin is highly selective for mTOR; Genistein has multiple targets
- The dose resistance index of Rapamycin is > 60,000x
23
BioMAP Profiling: Example ProfileReference p38 MAPK Inhibitor
Lo
g e
xp
ressio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
Control (no drug)
99%
significance
envelope
BioMAP Systems
Readout Parameters (Biomarkers)
Dose
Response
Cytotoxicity Readouts
24
This profile shows dose-resistance – similar over a range of concentrations
BioMAP Profiling: Example ProfileReference p38 MAPK Inhibitor
Lo
g e
xp
ressio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
25
Activities relevant to the role of p38 in monocyte / Th1-type inflammation
p38 kinase is important for Th1-dependent inflammatory responses
Takanami-Ohnishi Y, et al., Essential role of p38 mitogen-activated protein kinase in contact hypersensitivity. J Biol Chem. 2002, 277:37896-903.
IL-8
HLA-DR
Monocyte
activation
IL-6IL-1aCD38HLA-DR
TNF-a
BioMAP Profiling: Example ProfileReference p38 MAPK Inhibitor
Lo
g e
xp
ressio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
26
Activities relevant to anti-thrombotic effects of p38 inhibitors
Tissue factor is the primary cellular initiator of coagulation
p38α deficiency impairs thrombus formation
Sakurai K, et al. Role of p38 mitogen-activated protein kinase in thrombus formation. J Recept Signal Transduct Res. 2004;24(4):283-96.
Tissue
Factor
BioMAP Profiling: Example ProfileReference p38 MAPK Inhibitor
Lo
g e
xp
ressio
n r
ati
o
(Dru
g/D
MS
O c
on
tro
l)
27
Activities relevant to side effects – clinical finding: skin rash
Upregulation of VCAM and ITAC are characteristic of skin hyperreactivity
Melikoglu M, et al., Characterization of the divergent wound-healing responses occurring in the pathergy reaction and normal healthy volunteers. J Immunol. 2006, 177:6415-21.
ITAC
VCAM
MMP1
VCAM
28
BioMAP® Data Analysis
Predictive
Informatics Tools
Custom informatics tools are
used to predict clinical outcomes
• Unsupervised Analyses
- Similarity Search of our reference database
- Clustering
• Supervised Analyses
- Computational models (classifiers) for mechanism of action
29
BioMAP® Reference Database
BioMAP®
Reference Database
Biomarker responses to drugs
are stored in the database
>3000 drugs
• More than 3000 agents
- Drugs – Clinical stage, approved, and failed
- Experimental Chemicals - Research tool
compounds, environmental chemicals,
nanomaterials
- Biologics – Antibodies, cytokines, factors,
peptides, soluble receptors
• Availability of reference data
- EPA ToxCast and selected reference data
are published and have been made
available (Houck, 2009; Berg, 2010; Berg,
2013; Kleinstreuer, 2014)
Similarity Analysis of Profiles
Highly correlated Similar
Pearson’s correlation of r > 0.7
Low correlation Not similar
Pearson’s correlation of r < 0.7
30
Microtubule
Stabilizers
Mitochondrial
ET chain
Retinoids
Hsp90
CDK
NFkB
MEK
DNA
synthesis
JNK
Protein
synthesis
Microtubule
Destabilizers
Estrogen R
PI-3K
Ca++
Mobilization
Clustering of Compound Profile DataCompounds Cluster According to Mechanisms of Action
mTOR
PKC Activation
p38 MAPK
HMG-CoA
reductase
Calcineurin
Transcription
31
Each circle represents a compound tested at a single dose
Lines are drawn between compounds whose profiles are similar (r > 0.7)
Figure adopted from Berg, JPTox Meth. 2010
Microtubule
Stabilizers
Mitochondrial
ET chain
Retinoids
Hsp90
CDK
NFkB
MEK
DNA
synthesis
JNK
Protein
synthesis
Microtubule
Destabilizers
Estrogen R
PI-3K
Ca++
Mobilization
BioMAP Data Can Cluster CompoundsAccording to Mechanisms of Action
mTOR
PKC Activation
p38 MAPK
HMG-CoA
reductase
Calcineurin
Transcription
p38 MAPK
Calcineurin
mTOR
Mitochondrial ATPase
32
Each circle represents a compound tested at a single dose
Lines are drawn between compounds whose profiles are similar (r > 0.7)
Figure adopted from Berg, JPTox Meth. 2010
Microtubule
Stabilizers
Mitochondrial
ET chain
Retinoids
Hsp90
CDK
NFkB
MEK
DNA
synthesis
JNK
Protein
synthesis
Microtubule
Destabilizers
Estrogen R
PI-3K
Ca++
Mobilization
BioMAP Data Can Cluster CompoundsAccording to Mechanisms of Action
mTOR
PKC Activation
p38 MAPK
HMG-CoA
reductase
Calcineurin
Transcription
Mechanism of Action
(On-Target)
Pathway
Relationships
33
Consensus Profiles for Mechanism Classes
p38 MAPK inhibitor 1
p38 MAPK inhibitor 2
p38 MAPK inhibitor 3
Consensus profile reflects target-specific biology
Can define mechanism class
34
1 1 1 1 1 1 1 1 1
Mechanism Class Consensus Profiles
AhRAgonist
CalcineurinInhibitor
EGFRInhibitor
EPAgonist
ERAgonist
GRAgonist(Full)
H1Antagonist
HDACInhibitor
HMG-CoAReductaseInhibitor
Hsp90Inhibitor
IKK2Inhibitor
IL-17AAgonist
JAKInhibitor
MEKInhibitor
MicrotubuleDisruptor
MicrotubuleStabilizer
MitochondrialInhibitor
mTORInhibitor
p38MAPKInhibitor
PDEIVInhibitor
PI3KInhibitor
PKC(c+n)Inhibitor
ProteasomeInhibitor
RAR/RXRAgonist
SRCa++ATPaseInhibitor
SrcFamilyInhibitor
TNF-alphaAntagonist
VitaminDReceptorAgonist
Patterns reflect “mechanism class” or target biology
Reproducible patterns permit building of classifiers for automated mechanism assignment
Me
chan
ism
Cla
sse
s
BioMAP Assay / Endpoints
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
E/E−
Se
lec
tin
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
Vis
ua
l
CC
L2
/MC
P−
1
CC
L2
6/E
ota
xin−
3
CD
10
6/V
CA
M−
1
CD
62
P/P−
se
lecti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Se
lec
tin
CX
CL
8/I
L−
8
IL−
1a
lph
a
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2
/MC
P−
1
CD
38
CD
40
CD
62
E/E−
Se
lec
tin
CD
69
CX
CL
8/I
L−
8
CX
CL
9/M
IG
PB
MC
Cy
toto
xic
ity
Pro
life
rati
on
SR
B
CD
87
/uP
AR
CX
CL
10
/IP−
10
CX
CL
9/M
IG
HL
A−
DR
IL−
1a
lph
a
MM
P−
1
PA
I−I
SR
B
TG
F−
be
taI
tPA
uP
A
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/I
L−
8
CX
CL
9/M
IG
HL
A−
DR
IL−
6
LD
LR
M−
CS
F
Pro
life
rati
on
Se
rum
Am
ylo
id A
SR
B
CD
10
6/V
CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/I
L−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_7
2h
r
SR
B
TIM
P−
1
CC
L2
/MC
P−
1
CD
54
/IC
AM−
1
CX
CL
10
/IP−
10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
CA
M−
1
CD
14
1/T
hro
mb
om
od
uli
n
CD
14
2/T
iss
ue
Fa
cto
r
CD
54
/IC
AM−
1
CD
62
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54
/IC
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SR
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taI
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−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
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1
CD
10
6/V
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on
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Fa
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CD
62
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CD
40
CD
62
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69
CX
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Pro
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Pro
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Am
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SR
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10
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M−
1
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llag
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10
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8
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54
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10
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9
SR
B
TG
F−
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taI
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P−
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−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2
/MC
P−
1
CD
10
6/V
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1
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CD
40
CD
62
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CD
69
CX
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8
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MC
Cy
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ity
Pro
life
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on
SR
B
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87
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CL
10
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HL
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1
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8
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6
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Pro
life
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on
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id A
SR
B
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10
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life
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on
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2h
r
SR
B
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P−
1
CC
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P−
1
CD
54
/IC
AM−
1
CX
CL
10
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10
IL−
1a
lph
a
MM
P−
9
SR
B
TG
F−
be
taI
TIM
P−
2
uP
A
−1.5
−1.4
−1.3
−1.2
−1.1
−1.0
−0.9
−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
g R
atio
Profiles
Cyclopamine 40 uM
Cyclopamine 13.333 u...
Cyclopamine 4.444 uM
Cyclopamine 1.482 uM
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
35
TF VCAM
Building Support Vector Machine Classifiers
• 88 Compounds
• 28 Target/Pathway
mechanisms
• 1-8 concentrations
• 327 Profiles
• 84 endpoints (8 BioMAP
Systems)
• Support Vector Machine
• 2-class models
• Mechanism class versus “Null”
set
• Result = Decision Value (DV)
• PPV – positive predictive value
(fraction of profiles that are correctly
classified)• PPV = TP / (TP + FP))
• Sensitivity (fraction of profiles that
are assigned to the class)• Sensitivity = TP / (TP + FN))
MitochondrialInhibitor
Microtubule Stabilizer Hsp90 Inhibitor
Classifier Performance: Examples
PDE IVInhibitor
Generate Data Set
Build Classifiers
Test Performance of Classifiers
Berg, Yang & Polokoff, 2013, J. Biomol Screen. 18:1260.36
• AhR agonist (Aryl Hydrocarbon)
• Calcineurin
• EGFR (Epidermal Growth Factor R)
• SERCA (SR Ca++ ATPase)
• EP agonist
• Estrogen R agonist
• Glucocorticoid R agonist
• H1R Antagonist (Histamine)
• HDAC
• HMG-CoA-Reductase
• Hsp90 Inhibitor
• IKK2
• IL-17 R agonist
• JAK
Confidential37
List of Classifiers (SVM Mechanism Models)
• MEK
• Microtubule Disruptor
• Microtubule Stabilizer
• Mitochondrial Inhibitor
• mTOR
• p38 MAPK
• PDE IV (Phosphodiesterase
• PI3K
• PKC (c+n)
• Proteasome
• RAR-RXR agonist
• Src family
• TNF (Tumor Necrosis Factor)
• VDR agonist (Vitamin D R)
Berg, Yang & Polokoff, 2013, J. Biomol Screen. 18:1260.
• Compound characterization
- Broad biological fingerprint
- Cell types, pathways, possible clinical indications
• Mechanism of action
- Triage hits from phenotypic drug discovery programs
- Unexpected off-targets (toxicity)
• Support therapeutic hypotheses
- Compare to competitor molecules, clinical standards of care
- Identify translational biomarkers
Applications
38
EPA ToxCastTM Program – BioSeek
Goal is identification of in vitro assays that can help
forecast in vivo toxicity of environmental and other
agents (including pharmaceuticals)
40
Task Order Compound Number Compound Type
TO1 320 Environmental compounds
TO2 500 Environmental compounds
TO3 200 Environmental and Failed Pharma Compounds
TO4 39 Nanomaterials
TO5 31 Nanomaterials
TO6 100 Failed Pharma Compounds, etc.
TO7 39 Nanomaterials
Total 1229
40
Houck, K.A., J. Biomolecular Screening, 2009, 14:1054-66; Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.
Chemical Groups & Classes in ToxCast
Most active
Least active
41
Overall: 73% Active
(33 – 83%)
Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.
Results of Supervised AnalysisPerformance of SVM Mechanism Classifiers
Mechanism Class1 Number of Compounds
Correctly Assigned
% Comment
p38 MAPK Inhibitor 2 2 100%
Estrogen R Agonist 10 6 60%Not classified: meso-Hexestrol, 4-nonylphenol and diethystilbestrol
HMG-CoA Reductase Inhibitor 3 3 100%
Histamine R1 Antagonist 1 1 100%
Microtubule Inhibitor 2 1 50% Herbicides
GR Agonist 3 3 100%
Mitochondrial Inhibitor 2 2 100% Fungicides
PDE IV Inhibitor 8 6 75%
RAR/RXR Agonist 2 2 100%
Total 33 26 79%
• 1Mechanisms for which classifiers were available and mechanisms were known
- Dataset: Kleinstreuer, Nature Biotechnology, 2014, 32:583
- Classifiers: Berg, Yang and Polokoff, JBS, 2013, 18:1260
42
Unsupervised Analysis (Self Organizing Maps)AhR Phenotypic Signature
• Phenotypic signature of compounds in SOM cluster #57
- Box and whisker plot for cluster 57 representing a signature for AhR activation
• Confirmation of AhR activity
- 85% of members of clusters 57, 67 (adjacent in the 10X10 SOM) were active in an AhR reporter gene assay (examples shown here).
Tissue Factor
Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.43
Unsupervised Analysis (Self Organizing Maps)Estrogen R Actives: Phenotypic Signatures
• Two clusters of chemicals defined by their BioMAP signatures- Blue = Estradiol, Estrogen Receptor Agonists
- Red = Estrogen Receptor Antagonists, “Selective Estrogen R Modulators”
• Increased levels of Tissue Factor by SERMs and ER antagonists
Kleinstreuer, 2014, Nature Biotechnology, 32:583-91.
Estrogen
Receptor
Antagonists
Estrogen
Receptor
Agonists
Tissue Factor
44
Tissue FactorPrimary Cellular Initiator of Blood Coagulation
RW Colman 2006 J. Exp. Med
Blood
Coagulation
45
• Pathologic setting – aberrant coagulation thrombosis
- The formation of a blood clot (coagulation) within a vein
- Deep vein thrombosis (DVT), stroke
- Pulmonary embolism thrombi break off and get lodged in the lung
Thrombosis
SMC
Endothelial cells
Vessel Lumenplatelets in fibrin clot
46
• Associated with:
- Exposure to Smoking & Pollution• Polycyclic aromatic hydrocarbons (“Aryl Hydrocarbons”)
- Contraceptives, hormonal replacement therapy
- Various other drugs• mTOR inhibitors (everolimus)
• 2nd generation anti-psychotics
Thrombosis-Related Side Effects
48
• Aryl Hydrocarbon receptor agonists- PAHs, Benz(a)anthracene
- Smoking (Cigarette smoke extract)
• mTOR inhibitors- Everolimus (Baas, 2013, Thromb Res 132:307)
• Anti-Estrogens / SERMS, oral contraceptives- Tamoxifen, Clomiphene, Cyproterone
• Second generation anti-psychotics- Clozapine
• Others- Crizotinib
Mechanisms / Drugs Associated with Thrombosis-Related Side Effects
All show increased Tissue Factor levels in 3C and LPS Systems
49
• Search our reference database for all compounds / test agents that increase TF in the 3C system
- What are the mechanisms represented?
- Do they share any common biology?
• Issues
- Large chemical-biology datasets will have errors• Inactive concentrations, toxic concentrations, variability
- How do we increase our confidence?• Require compound effects at more than one concentration
• Effect size >20% (4 SD)
• Multiple compounds with same target mechanism
Is There A Connection?
50
Reference Compounds that Increase TF
Compound Name Mechanism Compound Name Mechanism
2-Mercaptobenzothiazole AhR agonist 3,5,3-Triiodothyronine Thyroid H R agonist
3-Hydroxyfluorene AhR agonist Concanamycin A Vacuolar ATPase Inhibitor
Benzo(b)fluoranthene AhR agonist MK-2206 AKT Inhibitor
C.I Solvent yellow 14 AhR agonist Crizotinib ALK, c-met Inhibitor
FICZ AhR agonist N-Ethylmaleimide Alkylating agent
Abiraterone CYP17A Inhibitor Terconazole Anti-fungal
Ketoconazole CYP17A Inhibitor GDC-0879 B-Raf Inhibitor
Clomiphene citrate Estrogen R Antagonist KN93 CAMKII Inhibitor
Histamine H1R agonist 8-Hydroxyquinoline Chelating agent
Histamine Phosphate H1R agonist Linoleic Acid Ethyl Ester Fatty Acid
Cobalt(II) Chloride Hexahydrate HIF-1α Inducer Tris(1,3-dichloro-2-propyl) phosphate Flame retardant
Tin(II) Chloride HIF-1α Inducer Fenaminosulf Fungicide
Chloroquine Phosphate Lysosome Inhibitor Mancozeb Fungicide
Primaquine Diphosphate Lysosome Inhibitor Primidone GABA R agonist
Temsirolimus mTOR Inhibitor Mometasone furoate GR agonist
Torin-1 mTOR Inhibitor Desloratadine H1R antagonist
Torin-2 mTOR Inhibitor A 205804 ICAM, E-selectin inhibitor
Bryolog PKC activator Dodecylbenzene Industrial chemical
Bryostatin 1 PKC activator UO126 MEK Inhibitor
Bryostatin 2 PKC activator Imatinib PDGFR, c-Kit, Bcr-Abl Inhibitor
Phorbol 12-myristate 13-acetate PKC activator ZK-108 PI-3K Inhibitor (βγ-selective)
Phorbol 12,13-didecanoate PKC activator GW9662 PPARγ agonist
Picolog PKC activator PP3 SRC Kinase Inhibitor
Z-FA-FMK Cysteine protease Inhibitor TX006146 Unknown
Mifamurtide NOD2 agonist TX006237 Unknown
Ethanol Organic Solvent TX011661 Unknown
Oncostatin M OSM R agonist U-73343 Unknown
PAz-PC Oxidized phospholipid
55/3187 = 1.7%51
Mechanisms that Increase TF
Test Agents Mechanism Confidence in
Mechanism 2-Mercaptobenzothiazole AhR agonist High
3-Hydroxyfluorene AhR agonist High
Benzo(b)fluoranthene AhR agonist High
C.I Solvent yellow 14 AhR agonist High
FICZ AhR agonist High
Abiraterone CYP17A Inhibitor High
Ketoconazole CYP17A Inhibitor High
Clomiphene citrate Estrogen R Antagonist High
Histamine H1R agonist High
Histamine Phosphate H1R agonist High
Cobalt(II) Chloride Hexahydrate HIF-1α Inducer High
Tin(II) Chloride HIF-1α Inducer High
Chloroquine Phosphate Lysosome Inhibitor High
Primaquine Diphosphate Lysosome Inhibitor High
Temsirolimus mTOR Inhibitor High
Torin-1 mTOR Inhibitor High
Torin-2 mTOR Inhibitor High
Bryolog PKC activator High
Bryostatin PKC activator High
Bryostatin 1 PKC activator High
Phorbol 12-myristate 13-acetate PKC activator High
Phorbol 12,13-didecanoate PKC activator High
Picolog PKC activator High
3,5,3-Triiodothyronine Thyroid H R agonist Good
Concanamycin A Vacuolar ATPase Inhibitor Good
Mifamurtide NOD2 agonist Good
Oncostatin M OSM R agonist Good
Ethanol Organic Solvent Good
PAz-PC Oxidized phospholipid Good
Z-FA-FMK Cysteine protease Inhibitor Good
8-Hydroxyquinoline Chelating agent Unknown
A 205804 ICAM, E-selectin inhibitor Unknown
AZD-4547 FGFR Inhibitor Unknown
Crizotinib ALK, c-met Inhibitor Unknown
Desloratadine H1R antagonist Unknown
Dodecylbenzene Industrial chemical Unknown
Fenaminosulf Fungicide Unknown
GDC-0879 B-Raf Inhibitor Unknown
GW9662 PPARγ agonist Unknown
Imatinib PDGFR, c-Kit, Bcr-Abl Inhibitor Unknown
KN93 CaMKII Inhibitor Unknown
Linoleic Acid Ethyl Ester Fatty Acid Unknown
Mancozeb Fungicide Unknown
MK-2206 AKT Inhibitor Unknown
Mometasone furoate GR agonist Unknown
N-Ethylmaleimide Alkylating agent Unknown
PP3 SRC Kinase Inhibitor Unknown
Primidone GABA R agonist Unknown
Sulindac Sulfide NSAID Unknown
Terconazole Anti-fungal Unknown
Tris(1,3-dichloro-2-propyl) phosphate Flame retardant Unknown
TX006146 Unknown Unknown
TX006237 Unknown Unknown
TX011661 Unknown Unknown
U-73343 Unknown Unknown
UO126 MEK Inhibitor Unknown
ZK-108 PI-3K Inhibitor (βγ-selective) Unknown
Mechanisms that Increase TF
AhR Agonist
CYP17A Inhibitor
Estrogen R Antagonist
H1R Agonist
HIF-1α Inducer
Lysosomal Inhibitor
mTOR Inhibitor
PKC Activator
Thyroid H R Agonist
Vacuolar ATPase Inhibitor
NOD2 Agonist
OSM R Agonist
52
Mechanisms that Increase TF
Mechanisms that Increase TF
AhR Agonist
CYP17A Inhibitor
Estrogen R Antagonist
H1R Agonist
HIF-1α Inducer
Lysosomal Inhibitor
mTOR Inhibitor
PKC Activator
Thyroid H R Agonist
Vacuolar ATPase Inhibitor
NOD2 Agonist
OSM R Agonist
Implicate Autophagy
53
Autophagy
• Cellular response to nutrient deprivation
• Also contributes to recycling of dysfunctional organelles, handling of protein aggregates, bacteria and viruses54
mTOR
Concanamycin AVATPase
Chloroquine
Caspase
Z-FA-FMK
Increased Tissue Factor
The Autophagy Connection
Autophagy
Lysosomal Function
55
mTOR
Temsirolimus
PI3Kβ
AKT
ZK-108
MK-2206
Concanamycin AVATPase
Chloroquine
Caspase
Z-FA-FMK
PAz-PC
Nutrient Sensing
Increased Tissue Factor
The Autophagy Connection
Autophagy
56
mTOR
Temsirolimus
Oxygen Sensing
CoCl2
TnCl2
HIF-1α
PI3Kβ
AKT
ZK-108
MK-2206
Concanamycin AVATPase
Chloroquine
Caspase
Z-FA-FMK
PAz-PC
REDD1
Ethanol
Nutrient Sensing
Increased Tissue Factor
The Autophagy Connection
Autophagy
57
mTOR
Temsirolimus
Oxygen Sensing
CoCl2
TnCl2
HIF-1α
PI3Kβ
AKT
ZK-108
MK-2206
Concanamycin AVATPase
Chloroquine
Benzo(b)fluoranthene
ER Clomiphene
Caspase
Z-FA-FMK
NPC1
AhR
PAz-PC
Estrogen
AbirateroneCYP17A1
REDD1
Ethanol
Lipid Sensing
Nutrient Sensing
Increased Tissue Factor
The Autophagy Connection
Autophagy
58
mTOR
Temsirolimus
Oxygen Sensing
NOD2Mifamurtide
CoCl2
TnCl2
HIF-1α
PI3Kβ
AKT
ZK-108
MK-2206
PKC
PMA Concanamycin AVATPase
Chloroquine
Benzo(b)fluoranthene
ER Clomiphene
Caspase
Z-FA-FMK
NPC1
AhR
PAz-PC
Estrogen
AbirateroneCYP17A1
REDD1
Ethanol
Lipid Sensing
Nutrient Sensing
Bacterial Sensing
Increased Tissue Factor
The Autophagy Connection
Autophagy
Berg, Polokoff, O’Mahony, Nguyen & Li, submitted, 201459
• Summary
- Compounds that increase TF are associated with thrombosis related side effects
- Compounds that increase TF also increase autophagic vacuoles (increase formation or decrease breakdown)
- Mechanistic Hypothesis: thrombosis-related side effects are associated with alterations in the process of autophagy that increase TF cell surface levels
• Take home message:
- This case study illustrates how chemical biology datasets, combined with external knowledge, can give rise to higher level mechanistic understanding of toxicity mechanisms
Tissue Factor, Autophagy & Thrombosis
60
Adverse Outcome Pathway Framework
MIEKey
EventAdverse
OutcomeKey
EventKey
Event
Molecular
Initiating EventClinical Effect
• Framework for integrating mode of action hypotheses to outcomes for chemical risk assessment (OECD)- http://www.oecd.org/chemicalsafety/testing/adverse-outcome-pathways-
molecular-screening-and-toxicogenomics.htm
• Focused on the clinical outcome- Anchored at both ends
61
AOP for DVT
MIEKey
EventAdverse
Outcome
Inhibition of
mTOR
Upregulation
of Tissue
Factor
Deep Vein
Thrombosis
Initiation of
Coagulation
Key Event
Key Event
Molecular
Initiating EventClinical Effect
Increase in
Autophagic
Vacuolization
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AOP for DVT
MIEKey
EventAdverse
Outcome
Inhibition of
mTOR
Upregulation
of Tissue
Factor
Deep Vein
Thrombosis
Initiation of
Coagulation
Key Event
Key Event
Molecular
Initiating EventClinical Effect
MIE
Activation of
AhR
Increase in
Autophagic
Vacuolization
Key Event
Inhibition of
NPC1
Key Event
HDF3CGF
In vitro
disease model
3C
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
Endothelial Cells
Endothelial Cells
PBMC + Endothelial
Cells
PBMC + Endothelial
Cells
Bronchial epithelial cells
Coronary artery SMC
FibroblastsKeratinocytes + Fibroblasts
Th1 Th2 TLR4 TCR Th1 Th1 Th1 + GF Th1 + TGF
Acute Inflammation E-selectin, IL-8
E-selectin, IL-1a, IL-8, TNF-
a, PGE2 IL-8 IL-1a
IL-8, IL-6, SAA
IL-8 IL-1α
Chronic Inflammation
VCAM-1, ICAM-1, MCP-1, MIG
VCAM-1, Eotaxin-3,
MCP-1
VCAM-1, MCP-1
MCP-1, E-selectin, MIG
IP-10, MIG, HLA-DR
MCP-1, VCAM-1,MIG, HLA-
DR
VCAM-1, IP-10, MIG
MCP-1, ICAM-1, IP-10
Immune Response HLA-DR CD40, M-CSFCD38, CD40, CD69, T cell
Prolif., Cytotox.HLA-DR M-CSF M-CSF
Tissue Remodeling uPAR, MMP-1, PAI-1, TGFb1, SRB, tPA, uPA
uPAR,
Collagen III, EGFR, MMP-1, PAI-1, Fibroblast
Prolif., SRB, TIMP-1
MMP-9, SRB, TIMP-2, uPA,
TGFβ1
Vascular Biology
TM, TF, uPAR, EC
Proliferation, SRB, Vis
VEGFRII, uPAR, P-
selectin, SRB
Tissue Factor, SRB
SRB
TM, TF, LDLR, SMC
Proliferation, SRB
Vascular Biology,
Cardiovascular
Disease, Chronic
Inflammation
Asthma, Allergy,
Oncology,
Vascular Biology
Cardiovascular
Disease, Chronic
Inflammation,
Infectious Disease
Autoimmune
Disease, Chronic
Inflammation,
Immune Biology
COPD,
Respiratory,
Epithelial Biology
Vascular Biology,
Cardiovascular
Inflammation,
Restenosis
Tissue Remodeling,
Fibrosis, Wound
Healing
Skin
Biology,Psoriasis,
Dermatitis
En
dp
oin
t Ty
pe
s
Disease / Tissue Relevance
BioMAP System
Primary Human Cell Types
Stimuli
! ! ! ! !
63
• Challenges for studying drug combinations:
- System must include both targets
- Physiologically relevant setting (ideally all human)
- Suitably robust to capture combination effects
• Case Example
- BioMAP Oncology systems that model tumor-host
microenvironments
- Trametinib (MEK kinase inhibitor) + Dabrafenib (Braf inhibitor)
• Combination approved for treatment of melanoma
Drug Combinations
65
BioMAP Oncology Systems
System Primary Human Cell TypesDisease / Tissue
RelevanceBiomarker Readouts
StroHT29
HT-29 colon adenocarcinoma cell
line + Primary Human Fibroblasts
+ PBMC
Oncology: Host Tumor-
Stromal Microenvironment
sVEGF, MMP9, TIMP2, tPA, uPA, uPAR, collagen I,
collagen III, PAI-1, SRB, sIL-2, pCyt, sIL-6, sIL-10,
sIFNγ, sTNFα, sIL-17A, sGranzyme B, Keratin 20,
CEACAM5, IP-10, VCAM-1
VascHT29
HT-29 colon adenocarcinoma cell
line + Primary Human Endothelial
cells + PBMC
Oncology: Host Tumor-
Vascular Microenvironment
CD40, CD69, uPAR, collagen IV, MCP-1, VCAM-1,
pCyt, SRB, sIL-2, sIL-6, sIL-10, sIFNγ, sTNFα, sIL-
17A, sGranzyme B, CEACAM5, Keratin 20, IP-10,
MIG
• Biomarker Endpoints:
• Immunomodulation: IL-2, IL-6, IL-10, IL-4, IFNγ, CD40, CD69, IL-17, Granzyme B
• Inflammation: TNFα, MCP-1, VCAM, CXCL9/MIG,
• Metastasis / Remodeling: MMP9, TIMP2, Collagens I, III, IV, uPA, uPAR, PAI-1
• Angiogenesis / Fibrinolysis: uPA, uPAR, PAI-1, VEGF
• Tumor specific markers: CEACAM5, CK2067
Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib
68
Combination Study Example: B-Raf + MEK Inhibitor
Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib
• Combination effects of Dabrafenib (B-raf) and Trametinib (MEK)- Tumor cell marker (CEACAM5) is reduced only in the combination (green
arrow)
- Consistent with the combination being more efficacious against tumors in vivo
69
Combination Study Example: B-Raf + MEK Inhibitor
Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib
• Combination effects of Dabrafenib (B-raf) and Trametinib (MEK)- Tumor cell marker (CEACAM5) is reduced only in the combination
- Consistent with the combination being more efficacious against tumors in vivo
- Reduced levels of Inflammatory endpoints; collagen III (grey arrows)
- Consistent with reduced Trametinib-related skin side effects (Flaherty, 2012, NEJM 367:1694).
70
Combination Study Example: B-Raf + MEK Inhibitor
• Chemical profiling in human cell systems generates activity profiles that can be used to:
- Group chemicals into bioactivity classes
- Generate MoA hypotheses
- Identify activities that may correlate with in vivo outcomes
• High throughput in vitro data is most informative when combined with external information
- Known targets
- In vivo bioactivities
Summary
Confidential71
• Applications for predicting in vivo effects must also consider:
- Exposure - level and route
- Distribution
- Metabolism
- Human variability
Challenges and Considerations
Confidential72
• BioSeek
- Mark A. Polokoff
- Dat Nguyen
- Xitong Li
- Antal Berenyi
- Alison O’Mahony
- Jian Yang (Oracle)
• UCSF
- Kevan Shokat
Acknowledgements
• EPA
- Keith Houck
- Nicole Kleinstreuer
- Richard Judson
• Support
- NIH/NIAID (SBIR)
- EPA (EP-D-12-047, EP-W-07-039)
73