A Chemical Biology Approach Using Primary Human Cell Systems and Co-cultures for Understanding Target Biology Ellen L. Berg, PhD Scientific Director, BioSeek, a division of DiscoveRx Physiologically Relevant Target Strategies SLAS 2015, Washington DC 11 February 2015
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A Chemical Biology Approach Using Primary Human Cell Systems and Co-cultures
for Understanding Target Biology
Ellen L. Berg, PhDScientific Director, BioSeek, a division of DiscoveRx
Physiologically Relevant Target StrategiesSLAS 2015, Washington DC
11 February 2015
• Problem:
- Pharmaceutical productivity is too 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 better data, not more data
2
Target-Based Drug Discovery - Challenges
• Target validation
- Biology has a modular architecture
- Function depends on “context”
• Target selectivity (poly-pharmacy)
- Most drugs interact with more than one target
- Targets interact with one another3
Solution: Primary Human Cell Systems
• BioMAP® Profiling Platform:
- In Vitro testing in primary human cell-
based tissue and disease models
• Chemical biology approach
- Data-driven research methodology
- Large scale chemical biology datasets
• Applications in drug discovery
- Compound /target validation
- Translational biology
- Drug mechanisms of action – in context of disease
4
BioMAP® Technology Platform
BioMAP®
Assay Systems
Reference
Profile Database
Predictive
Informatics Tools
Human primary cells
Disease-models
> 50 systems
Biomarker responses to drugs
are stored in the database
> 3000 drugs and agents
Custom informatics tools are
used to predict clinical outcomes
High Throughput Human Biology
5
BioMAP® Systems – Key Features
Primary human cell types
Physiologically relevant “context”
Complex activation settings
Co-cultures
Translational biomarker endpoints
6
Feature Mouse Man
Lifespan 2 Years 70 Years
Size 60 g 60 kg
EnvironmentAnimal facility,
cage-matesOutside world, people,
animals, etc.
Why Human?
Key differences:DNA repair mechanisms
Control of blood flow, hemostasis
Immune system status
7
Closer to the disease process
Downstream of multiple pathways and integrate information
“Decision-making”
Used by clinicians to guide therapy - Provide clinical “line of sight”
Why Translational Biomarkers?
mRNA,epigenome
Phospho-sites, intracellular proteins,
metabolome
Cell surface,secreted molecules
8
Primary Human Cell Systems Panels3C 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
- Standardized methods, automated and run at scale – strict QA
- Methods to manage variation • Donor qualification, donor pools
• Plate based normalization
- Singlicate endpoint measurements, but• Multiple concentrations (4+) per compound
• Multiple endpoints per assay system
• Multiple assay systems per compound
Experimental Design
11
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
12
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, 14:1054-66.13
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
Diversity of BioMAP Profile PatternsProfile Shapes & Concentration-Response Patterns
InactiveActive – Sharp dose-response
Active – Dose resistantActive – Selectively
14
Rapamycin (mTOR) Genistein (multi-target)
Dose ResistanceA Compound “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
15
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3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
Control (no drug)
95%
significance
envelope
BioMAP Systems
Biomarker Endpoints
Concentration
Response
Cytotoxicity Readouts
16
• Trametinib is approved for the treatment of metastatic melanoma
• Common side effects are rash, diarrhea, peripheral edema, fatigue, and dermatitis• Skin toxicity requiring hospitalization occurred in 6% of patients treated with trametinib, most
commonly for secondary infections of the skin or severe skin toxicity (METRIC Phase III study)
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−0.8
−0.7
−0.6
−0.5
−0.4
−0.3
−0.2
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0.2
0.3
0.4
0.5
0.6
0.7
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BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
BioMAP Systems
17
• Activities relevant to the role of MEK kinase in the cell cycle- MEK kinase (MAP2K) is a member of the MAPK signaling cascade
- Key activities include: inhibition of endothelial cell, T cell, smooth muscle cell and fibroblast proliferation
EC
Prolif. T cell
Prolif.
SMC
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CA
M−
1
Co
llag
en
III
CX
CL
10
/IP−
10
CX
CL
8/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
54/I
CA
M−
1
CD
62
E/E−
Sele
cti
n
CD
87
/uP
AR
CX
CL
8/IL−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
CC
L2/M
CP−
1
CC
L26
/Eo
tax
in−
3
CD
10
6/V
CA
M−
1
CD
62P
/P−
sele
cti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Sele
cti
n
CX
CL
8/IL−
8
IL−
1alp
ha
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2/M
CP−
1
CD
38
CD
40
CD
62
E/E−
Sele
cti
n
CD
69
CX
CL
8/IL−
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−
1alp
ha
MM
P−
1
PA
I−I
SR
B
tPA
uP
A
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/IL−
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/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
BioMAP Systems
18
• Activities relevant to the role of MEK kinase in cancer- Angiogenesis (PMID: 10209122)
BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
BioMAP Systems
19
• Activities relevant to side effects – risk of infections- Immunosuppression
• Inhibition of T cell proliferation and T cell activation
T Cell
Proliferation
T cell
activation
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
54/I
CA
M−
1
CD
62
E/E−
Sele
cti
n
CD
87
/uP
AR
CX
CL
8/IL−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
CC
L2/M
CP−
1
CC
L26
/Eo
tax
in−
3
CD
10
6/V
CA
M−
1
CD
62P
/P−
sele
cti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Sele
cti
n
CX
CL
8/IL−
8
IL−
1alp
ha
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2/M
CP−
1
CD
38
CD
40
CD
62
E/E−
Sele
cti
n
CD
69
CX
CL
8/IL−
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−
1alp
ha
MM
P−
1
PA
I−I
SR
B
tPA
uP
A
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/IL−
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/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
54/I
CA
M−
1
CD
62
E/E−
Sele
cti
n
CD
87
/uP
AR
CX
CL
8/IL−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
CC
L2/M
CP−
1
CC
L26
/Eo
tax
in−
3
CD
10
6/V
CA
M−
1
CD
62P
/P−
sele
cti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Sele
cti
n
CX
CL
8/IL−
8
IL−
1alp
ha
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2/M
CP−
1
CD
38
CD
40
CD
62
E/E−
Sele
cti
n
CD
69
CX
CL
8/IL−
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−
1alp
ha
MM
P−
1
PA
I−I
SR
B
tPA
uP
A
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/IL−
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/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
BioMAP Systems
20
• Activities relevant to side effects – risk of infections- Immunosuppression
• Inhibition of T cell proliferation and T cell activation
- Inhibition of acute inflammatory markers IL-1α, IL-8 and TNFα
T Cell
Proliferation
T cell
activation
IL-8 IL-1a
TNF-aIL-8
IL-1aIL-8
IL-8IL-1a
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
54/I
CA
M−
1
CD
62
E/E−
Sele
cti
n
CD
87
/uP
AR
CX
CL
8/IL−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
CC
L2/M
CP−
1
CC
L26
/Eo
tax
in−
3
CD
10
6/V
CA
M−
1
CD
62P
/P−
sele
cti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Sele
cti
n
CX
CL
8/IL−
8
IL−
1alp
ha
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2/M
CP−
1
CD
38
CD
40
CD
62
E/E−
Sele
cti
n
CD
69
CX
CL
8/IL−
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−
1alp
ha
MM
P−
1
PA
I−I
SR
B
tPA
uP
A
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/IL−
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/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
54/I
CA
M−
1
CD
62
E/E−
Sele
cti
n
CD
87
/uP
AR
CX
CL
8/IL−
8
CX
CL
9/M
IG
HL
A−
DR
Pro
life
rati
on
SR
B
CC
L2/M
CP−
1
CC
L26
/Eo
tax
in−
3
CD
10
6/V
CA
M−
1
CD
62P
/P−
sele
cti
n
CD
87
/uP
AR
SR
B
VE
GF
R2
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
14
2/T
iss
ue
Fa
cto
r
CD
40
CD
62
E/E−
Sele
cti
n
CX
CL
8/IL−
8
IL−
1alp
ha
M−
CS
F
sP
GE
2
SR
B
sT
NF−
alp
ha
CC
L2/M
CP−
1
CD
38
CD
40
CD
62
E/E−
Sele
cti
n
CD
69
CX
CL
8/IL−
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−
1alp
ha
MM
P−
1
PA
I−I
SR
B
tPA
uP
A
CC
L2/M
CP−
1
CD
10
6/V
CA
M−
1
CD
141
/Th
rom
bo
mo
du
lin
CD
14
2/T
iss
ue
Fa
cto
r
CD
87
/uP
AR
CX
CL
8/IL−
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/IL−
8
CX
CL
9/M
IG
EG
FR
M−
CS
F
MM
P−
1
PA
I−I
Pro
life
rati
on
_72
hr
SR
B
TIM
P−
1
CC
L2/M
CP−
1
CD
54/I
CA
M−
1
CX
CL
10
/IP−
10
IL−
1alp
ha
MM
P−
9
SR
B
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
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor
Lo
g e
xp
res
sio
n r
ati
o
(Dru
g/V
eh
icle
co
ntr
ol)
BioMAP Systems
21
• Activities relevant to side effects – skin rash- Upregulation of VCAM and IP-10 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.
IP-10VCAMVCAM
• Confirm expected target activities
• Identify unexpected activities
- Target or off-target
• Evaluate suitability for in vivo preclinical studies
- Safety window
- Dose-resistance
• Guide in vivo preclinical studies
- Dose selection
- Identify new biomarkers or pathways to track
How Can These Data Help in TDD?
22
Alert!!
Prioritize
Predict
Confirm
• Testing Drug Combinations
- Preclinical testing of drug combinations to preview in vivo
effects
• Elucidating Mechanisms of Toxicity
- Data mining chemical biology datasets
- Connect target biology in knowledge frameworks built around
clinical outcomes
Case Studies
23
Case Study: Drug Combinations
24
• Challenges for studying drug combinations:
- System must include both targets
- Physiologically relevant setting (ideally all human)
• Also contributes to recycling of dysfunctional organelles, handling of protein aggregates, bacteria and viruses42
The Autophagy Connection
The Autophagy Connection
Lysosomal
Function
The Autophagy Connection
Lysosomal
Function
The Autophagy Connection
Lysosomal
Function
The Autophagy Connection
Lysosomal
Function
The Autophagy Connection
Lysosomal
Function
Berg, et al., IJMS, 2015
• Summary
- Mechanistic Hypothesis: thrombosis-related side effects are associated with alterations in the process of autophagy that increase TF cell surface levels
- In moderation, during nutrient deprivation, an increase in TF leading to the recruitment of nutrient-rich platelets to a tissue sites would be a beneficial response
• Next Step:
- Incorporation of these data in a knowledge framework
Tissue Factor, Autophagy & Thrombosis
49
Adverse Outcome Pathway (AOP)Knowledge 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/chemical safety/testing/adverse-outcome-pathways-
molecular-screening-and-toxicogenomics.htm
• Focused on the clinical outcome- Anchored at both ends
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An AOP for DVT
MIEKey
EventAdverse
Outcome
Activation of
AhR
Upregulation
of Tissue
Factor
Deep Vein
Thrombosis
Initiation of
Coagulation
Key Event
Key Event
Molecular
Initiating EventClinical Effect
Increase in
Autophagic
Vacuolization
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