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BioMAP®
Phenotypic Profiling ServicesIn Vivo Insights with the Speed and Ease of an In Vitro Assay
What’s Inside:
� x
� x
� x
� x
What’s Inside:
� Triage drug candidates
� Test compounds for safety & efficacy
� Inform preclinical design
� Reposition existing drugs
Post-marketingPreclinicalLead OptimizationLead IdentificationHTSTarget Validation
Chemical Biology Target Validation
Indication Selection Indication Selection
Clinical Biomarker Discovery and Validation
Testing Combination Therapy
Competitor Comparison
Compound Repositioning
Clinical
Sentinel Assay Identification
Determine Potency or Selectivity
Identify Secondary Effects
Inform Toxicity
Phenotypic Drug DiscoveryHuman disease biology is complex and we are only now beginning to fully decipher the countless chemical and physical interactions that contribute to the disruption of normal processes to manifest in disease pathology. It is easy to understand why predicting the effects of a novel compound on disease biology remains a significant challenge. Numerous strategies have been developed for identifying the target, mechanism, selectivity, and potency of a compound, however, many approaches are one-dimensional and fail to capture the complexity of patient physiology in vitro. The BioMAP Phenotypic Profiling Platform overcomes these challenges by mirroring complex tissue and disease biology in vitro and has been validated using approved drugs and known test agents to recapitulate reported clinical outcomes. The BioMAP platform is the best available service to:
� Triage candidates prior to in vivo experiments or IND submission
� Test compounds for safety and efficacy
� Inform preclinical design and biomarker selection
� Manage product lifecycle and reposition existing drugs
Applications of BioMAP® Systems in Drug Discovery
Post-marketingPreclinicalLead OptimizationLead IdentificationHTSTarget Validation
Chemical Biology Target Validation
Indication Selection Indication Selection
Clinical Biomarker Discovery and Validation
Testing Combination Therapy
Competitor Comparison
Compound Repositioning
Clinical
Sentinel Assay Identification
Determine Potency or Selectivity
Identify Secondary Effects
Inform Toxicity
BioMAP Advances Drug DevelopmentBioMAP systems come as close to testing on human patients as an in vitro assay can. When considering what method to use for your next screen or lead characterization, consider the following benefits of BioMAP.
What BioMAP Offers Why It Matters in Drug Development
� Human primary cell-based assays � Intact regulatory and feedback mechanisms
� Validated to recapitulate clinical results � Predictive of clinical outcomes
� Automated assay platform � Reproducible within and between assays
� Proprietary database and custom computational analysis � The only platform to be able to predict safety and mechanism of action based on historical data
� Identification of secondary and off target activities � Reveal potential assets or liabilities of test compounds that one-dimensional approaches may miss
Applications of BioMAP® Systems in Drug Discovery
Learn more about BioMAP and request a quote at www.discoverx.com/biomap
Human Primary CellsTissue and Organ
MicroenvironmentsPatient Biology
Modeling Human Biology for Phenotypic Drug Development
Co-Culture and Disease Relevant Stimuli
60+ BioMAP Systems
BioMAP® Phenotypic Profiling PlatformThe broadest, most physiologically relevant method to quickly and robustly determine the efficacy, safety, and mechanism of action (MOA) of candidate drug molecules to support their pipeline progression. BioMAP Systems are composed of:
� Over 60 human primary cell-based models of tissue and disease biology
� Profiling with 100’s of clinically relevant protein biomarkers
� Database of 4,500+ reference compounds and a suite of bioinformatics for in-depth prediction of safety and MOA
BioMAP informs decisions that accelerate drug candidates from testing to therapies.
Modeling Human Biology for Phenotypic Drug Development
Profile of Clinical Protein Biomarkers
CC
L2/M
CP−
1
CD
106/
VCA
M−1
CD
141/
Thro
mbo
mod
ulin
CD
142/
Tiss
ue F
acto
r
CD
40
CD
62E/
E−Se
lect
in
CD
69
CXC
L8/IL
−8
IL−1
alph
a
M−C
SF
sPG
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SRB
sTN
F−al
pha
B c
ell P
rolif
erat
ion
PBM
C C
ytot
oxic
ity
Secr
eted
IgG
sIL−
17A
sIL−
2
sIL−
6
sTN
F−al
pha
CC
L2/M
CP−
1
CD
54/IC
AM
−1
CXC
L10/
IP−1
0
CXC
L8/IL
−8
CXC
L9/M
IG
IL−1
alph
a
MM
P−9
PAI−
I
SRB
TIM
P−2
uPA
−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
LPS BT KF3CT
CC
L2/M
CP−
1
CD
106/
VCA
M−1
CD
141/
Thro
mbo
mod
ulin
CD
142/
Tiss
ue F
acto
r
CD
40
CD
62E/
E−Se
lect
in
CD
69
CXC
L8/IL
−8
IL−1
alph
a
M−C
SF
sPG
E2
SRB
sTN
F−al
pha
B c
ell P
rolif
erat
ion
PBM
C C
ytot
oxic
ity
Secr
eted
IgG
sIL−
17A
sIL−
2
sIL−
6
sTN
F−al
pha
CC
L2/M
CP−
1
CD
54/IC
AM
−1
CXC
L10/
IP−1
0
CXC
L8/IL
−8
CXC
L9/M
IG
IL−1
alph
a
MM
P−9
PAI−
I
SRB
TIM
P−2
uPA
−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
LPS BT KF3CT
IL1a
sTNFa
IL1a
ProfilesTrametinib, 370 nMTrametinib, 120 nMTrametinib, 4.6 nMTrametinib, 1.5 nM
The BioMAP® Service Offering
Diversity PLUS™ Panel
The Diversity PLUS panel contains 12 BioMAP Systems that allow unbiased characterization of test agents across a broad set of systems modeling various therapeutically relevant disease states. A profile of biomarker activity of each test agent is generated and compared against the BioMAP reference database of more than 4,500 BioMAP profiles of bioactive agents (biologics, approved drugs, chemicals, and experimental agents), clustered with other project compounds and compared against 19 consensus mechanism class profiles of well-characterized drugs.
Use Diversity PLUS to: Inform on the potency, selectivity, safety, mechanism of action, and disease indication of a test agent.
Oncology Panels
The BioMAP Oncology Systems model the complexity of different tumor microenvironments (TME) by combining primary human stromal or vascular cells with immune cells and human tumor cell lines to recapitulate the complex interactions and signaling pathways that occur during tumorigenesis.
Use Oncology Panels to: Gain insight into TME-specific mechanism of action, efficacy, and safety-related effects of test agents.
T Cell/Autoimmune Panel
The T Cell/Autoimmune Panel models the adaptive immune cell microenvironment, as well as the individual T and B cell responses during different types of inflammation.
Use the T Cell Panel to: Gain insight into inflammation-related mechanism of action effects, indication guidance, and combination feasibility for a diverse set of target classes.
Fibrosis Panel
The BioMAP Fibrosis Panel contains systems modeling the tissue environments of the lung and kidney during the complex inflammatory and pro-fibrotic conditions that occur in fibrotic disease, wound healing, and extracellular matrix remodeling.
Use the Fibrosis Panel to: Gain insight into fibrosis-related mechanism of action effects, compound ranking, indication guidance, and combination feasibility for a diverse set of target classes.
Combo ELECT
The BioMAP Combo ELECT service allows statistical evaluation of drug combinations to determine additive or antagonistic differences between drug agents in a nominated system of interest. Each agent is analyzed individually and then statistically compared against a combination array of the two serially diluted agents.
Use Combo ELECT to: Inform on the optimal dosing, potential synergy, or adverse effects of different drug pairings.
Age
nt X
3 7 7 7
2 4 5 4
0 4 3 4
0 0 0 2
Agent Y
Results that Drive DecisionsBelow are the types of data readouts you receive at the conclusion of a BioMAP project to assist in your drug development decision making.
Profile Analysis
Concentration dependent response of biomarker activities of individual test agents.
Benchmark Analysis
Concentration dependent response of biomarker activities of individual test agents.
Similarity Analysis
Identifies agents with similar BioMAP profiles using an unbiased mathematical approach against a proprietary reference database of over 4,500 BioMAP profiles of bioactive agents (Diversity PLUS™ only).
HeatMAP Analysis
Biomarker readouts of agents are compared against consensus mechanism profiles of well characterized drugs (Diversity PLUS) or selected reference benchmarks that are current standards of care (T Cell/Autoimmune and Fibrosis).
Cluster Analysis
Graphical representation of functional similarities between agents tested in BioMAP Systems using pairwise correlation analysis.
Combination Analysis
Comparative annotated overlays of biomarker readouts of the combinations that are specifically different than the individual test agents (Combo ELECT).
Checkerboard Analysis
Summary of the number of biomarker activities in the combination for each concentration pair that are significantly different than individual test agents (Combo ELECT).
Log
10 S
cale
(T
est a
gent
rela
tive
to v
ehic
le c
ontr
ol)
-1 -0.5
Increasedexpression
Decreasedexpression
Cal
cine
urin
Inhi
bito
r
EG
FR In
hibi
tor
GR
Ago
nist
H1
Ant
agon
ist
HD
AC In
hibi
tor
HM
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educ
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bito
r
IKK
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JAK
Inhi
bito
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ME
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tor
Mic
rotu
bule
Sta
biliz
er
mTO
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hibi
tor
p38
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PK
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PD
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Inhi
bito
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PI3
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tor
PK
C (c
+n) I
nhib
itor
RA
R/R
XR
Ago
nist
Src
Fam
ily In
hibi
tor
TNF−
alph
a A
ntag
onis
t
Vita
min
D R
ecep
tor A
goni
st
Com
poun
d X
, 100
00 n
M
Com
poun
d X
, 330
0 nM
Com
poun
d X
, 110
0 nM
Com
poun
d X
, 100
nM
lMphg: SRB−MphglMphg: SRBlMphg: sIL−10lMphg: M−CSFlMphg: IL−1alMphg: IL−8lMphg: CD69lMphg: E−SelectinlMphg: CD40lMphg: VCAM−1lMphg: MIP−1alMphg: MCP−1MyoF: TIMP−1MyoF: SRBMyoF: PAI−IMyoF: MMP−1MyoF: DecorinMyoF: IL−8MyoF: Collagen IVMyoF: Collagen IIIMyoF: Collagen IMyoF: VCAM−1MyoF: bFGFMyoF: a−SMAKF3CT: uPAKF3CT: TIMP−2KF3CT: SRBKF3CT: PAI−IKF3CT: MMP−9KF3CT: IL−1aKF3CT: MIGKF3CT: IL−8KF3CT: IP−10KF3CT: ICAM−1KF3CT: MCP−1HDF3CGF: TIMP−2HDF3CGF: TIMP−1HDF3CGF: SRBHDF3CGF: ProliferationHDF3CGF: PAI−IHDF3CGF: MMP−1HDF3CGF: M−CSFHDF3CGF: EGFRHDF3CGF: MIGHDF3CGF: IL−8HDF3CGF: I−TACHDF3CGF: IP−10HDF3CGF: Collagen IIIHDF3CGF: Collagen IHDF3CGF: ICAM−1HDF3CGF: VCAM−1HDF3CGF: MCP−1CASM3C: SRBCASM3C: Serum Amyloid ACASM3C: ProliferationCASM3C: PAI−ICASM3C: M−CSFCASM3C: LDLRCASM3C: IL−6CASM3C: HLA−DRCASM3C: MIGCASM3C: IL−8CASM3C: uPARCASM3C: Tissue FactorCASM3C: ThrombomodulinCASM3C: VCAM−1CASM3C: MCP−1BE3C: uPABE3C: tPABE3C: SRBBE3C: PAI−IBE3C: MMP−9BE3C: MMP−1BE3C: Keratin 8/18BE3C: IL−1aBE3C: HLA−DRBE3C: EGFRBE3C: MIGBE3C: IL−8BE3C: I−TACBE3C: IP−10BE3C: uPARBE3C: ICAM−1BF4T: uPABF4T: tPABF4T: SRBBF4T: PAI−IBF4T: MMP−9BF4T: MMP−3BF4T: MMP−1BF4T: Keratin 8/18BF4T: IL−1aBF4T: IL−8BF4T: CD90BF4T: ICAM−1BF4T: VCAM−1BF4T: Eotaxin−3BF4T: MCP−1BT: sTNF−aBT: sIL−6BT: sIL−2BT: sIL−17FBT: sIL−17ABT: Secreted IgGBT: PBMC CytotoxicityBT: B−cell ProliferationSAg: SRBSAg: ProliferationSAg: PBMC CytotoxicitySAg: MIGSAg: IL−8SAg: CD69SAg: E−SelectinSAg: CD40SAg: CD38SAg: MCP−1LPS: sTNF−aLPS: SRBLPS: sPGE2LPS: M−CSFLPS: IL−1aLPS: IL−8LPS: CD69LPS: E−SelectinLPS: CD40LPS: Tissue FactorLPS: ThrombomodulinLPS: VCAM−1LPS: MCP−14H: VEGFR24H: SRB4H: uPAR4H: P−Selectin4H: VCAM−14H: Eotaxin−34H: MCP−13C: SRB3C: Proliferation3C: HLA−DR3C: MIG3C: IL−83C: uPAR3C: E−Selectin3C: ICAM−13C: Tissue Factor3C: Thrombomodulin3C: VCAM−13C: MCP−1
Biomark
erA
Biomark
erB
Biomark
erC
Biomark
erD
Biomark
erE
Biomark
erF
Biomark
erG
Biomark
erH
Biomark
erI
Biomark
erJ
Biomark
erK
Biomark
erL
Biomark
erM
-1.0
-0.5
0.0
0.5
1.0
Example of Combo Effects
Log
Rat
io
Agent XAgent YCombo X+Y
Age
nt X
3 7 7 7
2 4 5 4
0 4 3 4
0 0 0 2
Agent Y
Human Disease Modeled by the BioMAP® PlatformP
anel System Cell Types Diseases/
Tissues ModeledDescription Protein
Biomarker Readout
3C
Th1 Vasculature Venular endothelial cells
Cardiovascular Disease, Chronic Inflammation
The Th1 Endothelium (3C) system models vascular inflammation of the Th1 type, an environment that promotes monocyte and T cell adhesion and recruitment and is anti-angiogenic. This system is relevant for chronic inflammatory diseases, vascular inflammation and restenosis.
MCP-1, VCAM-1, TM, TF, ICAM-1, E-selectin, uPAR, IL-8, MIG, HLA-DR, Proliferation, SRB
4H
Th2 Vasculature Venular endothelial cells
Asthma, Allergy, Autoimmunity
The Th2 Endothelium (4H) system models vascular inflammation of the Th2 type, an environment that promotes mast cell, basophil, eosinophil, T and B cell recruitment and is pro-angiogenic. This system is relevant for diseases where Th2-type inflammatory conditions play a role such as allergy, asthma, and ulcerative colitis.
MCP-1, Eotaxin-3, VCAM-1, P-selectin, uPAR, SRB, VEGFRII
LPS
Monocyte Activation
PBMC/Venular endothelial cells
Cardiovascular Disease, Chronic Inflammation
The Monocyte Activation (LPS) system models chronic inflammation of the Th1 type and monocyte activation responses. This system is relevant to inflammatory conditions where monocytes play a key role including atherosclerosis, restenosis, rheumatoid arthritis, and other chronic inflammatory conditions, as well as metabolic diseases.
MCP-1, VCAM-1, TM, TF, CD40, E-selectin, CD69, IL-8, IL1-α, M-CSF, sPGE2, SRB, sTNFα
Div
ersi
ty P
LUS
SA
g
T Cell Activation PBMC/Venular endothelial cells
Autoimmune Disease, Chronic Inflammation
The T Cell Activation (SAg) system models chronic inflammation of the Th1 type and T cell effector responses to TCR signaling with costimulation. This system is relevant to inflammatory conditions where T cells play a key role including organ transplantation, rheumatoid arthritis, psoriasis, Crohn's disease and multiple sclerosis.
MCP-1, CD38, CD40, E-selectin, CD69, IL-8, MIG, PBMC Cytotoxicity, Proliferation, SRB
BT
B and T Cell Autoimmunity
B cells/PBMC Asthma, Allergy, Oncology, Autoimmunity
The B and T Cell Autoimmunity (BT) system models T cell dependent B cell activation and class switching as would occur in a germinal center. This system is relevant for diseases and conditions where B cell activation and antibody production are relevant. These include autoimmune disease, oncology, asthma and allergy.
B cell Proliferation, PBMC Cytotoxicity, Secreted IgG, sIL-17A, sIL-17F, sIL-2, sIL-6, sTNFα
BF4
T
Lung Disease Bronchial epithelial cells/Dermal fibroblasts
Asthma, Allergy, Fibrosis, Lung Inflammation
The Lung Disease (BF4T) system models lung inflammation of the Th2 type, an environment that promotes the recruitment of eosinophils, mast cells and basophils as well as effector memory T cells. This system is relevant for allergy and asthma, pulmonary fibrosis, as well as COPD exacerbations.
MCP-1, Eotaxin-3, VCAM-1, ICAM-1, CD90, IL-8, IL1-α, Keratin 8/18, MMP-1, MMP-3, MMP-9, PAI-1, SRB, tPA, uPA
BE
3C
Lung Inflammation
Bronchial epithelial cells
Lung Inflammation, COPD
The Lung Inflammation (BE3C) system models lung inflammation of the Th1 type, an environment that promotes monocyte and T cell adhesion and recruitment. This system is relevant for sarcoidosis and pulmonary responses to respiratory infections.
ICAM-1, uPAR, IP-10, I-TAC, IL-8, MIG, EGFR, HLA-DR, IL1-α, Keratin 8/18, MMP-1, MMP-9, PAI-1, SRB, tPA, uPA
CA
SM
3C
Cardiovascular Disease
Coronary artery smooth muscle cells
Cardiovascular Inflammation, Restenosis
The Cardiovascular Disease (CASM3C) system models vascular inflammation of the Th1 type, an environment that promotes monocyte and T cell recruitment. This system is relevant for chronic inflammatory diseases, vascular inflammation and restenosis.
MCP-1, VCAM-1, TM, TF, uPAR, IL-8, MIG, HLA-DR, IL-6, LDLR, M-CSF, PAI-1, Proliferation, SAA, SRB
Pan
el System Cell Types Diseases/Tissues Modeled
Description Protein Biomarker Readout
HD
F3C
GF
Fibrosis and Inflammation
Dermal fibroblasts Fibrosis, Chronic Inflammation
The Fibrosis and Inflammation (HDF3CGF) system models wound healing and matrix/tissue remodeling in the context of Th1-type inflammation. This system is relevant for various diseases including fibrosis, rheumatoid arthritis, psoriasis, as well as stromal biology in tumors.
MCP-1, VCAM-1, ICAM-1, Collagen I, Collagen III, IP-10, I-TAC, IL-8, MIG, EGFR, M-CSF, MMP-1, PAI-1, Proliferation_72hr, SRB, TIMP-1, TIMP-2
Div
ersi
ty P
LUS K
F3C
T
Psoriasis and Dermatitis
Keratinocytes/ Dermal fibroblasts
Psoriasis, Dermatitis, Skin Biology
The Psoriasis and Dermatitis (KF3CT) system models cutaneous inflammation of the Th1 type, an environment that promotes monocyte and T cell adhesion and recruitment. This system is relevant for cutaneous responses to tissue damage caused by mechanical, chemical, or infectious agents, as well as certain states of psoriasis and dermatitis.
MCP-1, ICAM-1, IP-10, IL-8, MIG, IL-1α, MMP-9, PAI-1, SRB, TIMP-2, uPA
Myo
F
Fibrosis Lung fibroblasts Fibrosis, Chronic Inflammation, Wound Healing, Matrix Remodeling
The Fibrosis (MyoF) system models the development of pulmonary myofibroblasts, and are relevant to respiratory disease settings as well as other chronic inflammatory settings where fibrosis occurs such as rheumatoid arthritis.
a-SM Actin, bFGF, VCAM-1, Collagen-I, Collagen-III, Collagen-IV, IL-8, Decorin, MMP-1, PAI-1, TIMP-1, SRB
IMp
hg
Macrophage Activation
Venular endothelial cells/ Macrophages
Cardiovascular Inflammation, Restenosis, Chronic Inflammation
The Macrophage Activation (lMphg) system models chronic inflammation of the Th1 type and macrophage activation responses. This system is relevant to inflammatory conditions where monocytes play a key role including atherosclerosis, restenosis, rheumatoid arthritis, and other chronic inflammatory conditions.
MCP-1, MIP-1α, VCAM-1, CD40, E-selectin, CD69, IL-8, IL1-α, M-CSF, sIL-10, SRB, SRB-Mphg
Str
oH
T29
Colorectal Cancer - Stro
HT-29 colon adenocarcinoma cell line/Primary human fibroblasts/ PBMC
CRC Oncology/Immune Oncology: Host Stromal-Tumor Microenvironment Biology, Tissue-Remodeling, Wound Healing, Inflammation
The Colorectal Cancer - Stro (StroHT29) system models the host stromal-tumor microenvironment by capturing the complex interactions between tumor cells, the host stromal network, and infiltrating immune cells recruited into the tumor mass.
VCAM-1, uPAR, Collagen I, Collagen III, IP-10, MMP-9, PAI-1, PBMC Cytotoxicity, sGranzyme B, sIFNγ, sIL-10, sIL-17A, sIL-2, sIL-6, SRB, sTNFα, sVEGF, TIMP2, tPA, uPA, CEACAM5, Keratin 20
Onc
olo
gy
Vas
cHT
29
Colorectal Cancer - Vasc
HT-29 colon adenocarcinoma cell line/Primary human endothelial cells/PBMC
CRC Oncology/Immune Oncology: Host Vascular-Tumor Microenvironment Biology, Inflammation
The Colorectal Cancer - Vasc (VascHT29) system models host vascular-tumor microenvironment by capturing the complex interactions between tumor cells, the host vascular network, and infiltrating immune cells associated with angiogenesis.
MCP-1, VCAM-1, CD40, CD69, uPAR, Collagen IV, IP-10, MIG, PBMC Cytotoxicity, sGranzyme B, sIFNγ, sIL-10, sIL-17A, sIL-2, sIL-6, SRB, sTNFα, CEACAM5, Keratin 20
Str
oN
SC
LC
Lung Cancer - Stro
NCI-H1299 NSCLC cell line/Primary human fibroblasts/ PBMC
NSCLC Oncology/Immune Oncology: Host Stromal-Tumor Microenvironment Biology, Tissue-Remodeling, Wound Healing, Inflammation
The Lung Cancer - Stro (StroNSCLC) host-NSCLC tumor microenvironment model system consists of human primary fibroblasts co-cultured with a NSCLC cell line, NCI-H1299, and human peripheral blood mononuclear cells. These conditions model the host stromal-tumor microenvironment by capturing the complex interactions between tumor cells, the host stromal network, and infiltrating immune cells recruited into the tumor mass.
VCAM-1, uPAR, Col-III, IP-10, EGFR, HGF, PAI-1, PBMC Cytotoxicity, SRB, tPA, uPA, sGranzymeB, sPGE2, sVEGF, sIFNγ, sIL-10, sIL-13, sIL-17A, sIL-2, sIL-4, sIL-6, sMDC, sTNFα
Human Disease Modeled by the BioMAP® PlatformP
anel System Cell Types Diseases/
Tissues ModeledDescription Protein
Biomarker Readout
Onc
olo
gy
Vas
cNS
CLC
Lung Cancer - Vasc
NCI-H1299 NSCLC cell line/Primary human endothelial cells/PBMC
NSCLC Oncology/Immune Oncology: Host Vascular-Tumor Microenvironment Biology, Inflammation
The Lung Cancer - Vasc (VascNSCLC) host-NSCLC tumor microenvironment model system consists of human primary vascular endothelial cells co-cultured with a NSCLC cell line, NCI-H1299, and human peripheral blood mononuclear cells. These conditions model the host vascular-tumor microenvironment by capturing the complex interactions between tumor cells, the host vascular network, and infiltrating immune cells associated with angiogenesis.
MCP-1, VCAM-1, CD40, CD69, uPAR,IP-10, PAI-1, PBMC Cytotoxicity, SRB, sGranzymeB, sIFNγ, sIL-10, sIL-13, sIL-17A, sIL-2, sIL-4, sIL-6, sMDC, sTNFα
SA
EM
yoF
Pulmonary Fibrosis
Small airway epithelial cells/Lung fibroblasts
Pulmonary Fibrosis, Chronic Inflammation, Wound Healing, Matrix Remodeling
The Pulmonary Fibrosis (SAEMyoF) system models the biology of fibrotic lung diseases such as idiopathic pulmonary fibrosis. This co-culture of pulmonary epithelial cells and myofibroblasts is relevant for evaluating wound healing and inflammation-related responses in the lung.
α-SMA, MCP-1, VCAM-1, Collagen-I, Collagen-III, IP-10, I-TAC, E-Cadherin, EGFR, M-CSF, MMP-1, MMP-9, N-Cadherin, PAI-I, sIL-6, sIL-8, SRB, sVEGF, TIMP-1, uPA
Fib
rosi
sM
yoF
Fibrosis Lung fibroblasts Fibrosis, Chronic Inflammation, Wound Healing, Matrix Remodeling
The Fibrosis (MyoF) system models the development of pulmonary myofibroblasts, and is relevant to respiratory disease settings as well as other chronic inflammatory settings where fibrosis occurs such as rheumatoid arthritis.
a-SM Actin, bFGF, VCAM-1, Collagen-I, Collagen-III, Collagen-IV, IL-8, Decorin, MMP-1, PAI-1, TIMP-1, SRB
RE
Myo
F
Renal Fibrosis Renal proximal tubule epithelial cells/Lung fibroblasts
Renal Fibrosis, Chronic Inflammation, Wound Healing, Matrix Remodeling
The Renal Fibrosis (REMyoF) system models the development of kidney fibrosis. This system is relevant for inflammatory kidney diseases, nephritis, and fibrosis.
α-SMA, MCP-1, VCAM-1, Collagen-I, Collagen-III, IP-10, I-TAC, E-Cadherin, EGFR, Ker8/18, M-CSF, MMP-1, MMP-9, N-Cadherin, PAI-I, sIL-6, sIL-8, SRB, sVEGF, TIMP-1, tPA, uPA
T C
ell/A
uto
imm
une
BT
B and T Cell Autoimmunity
B cells/PBMC Asthma, Allergy, Oncology, Autoimmunity
The B and T Cell Autoimmunity (BT) system models T cell dependent B cell activation and class switching as would occur in a germinal center. This system is relevant for diseases and conditions where B cell activation and antibody production are relevant. These include autoimmune disease, oncology, asthma, and allergy.
B cell Proliferation, PBMC Cytotoxicity, Secreted IgG, sIL-17A, sIL-17F, sIL-2, sIL-6, sTNFα
SA
g
Chronic Th1 Inflammation
PBMC/Venular endothelial cells
Autoimmune Disease, Chronic Inflammation
The Chronic Th1 Inflammation (SAg) system models chronic inflammation of the Th1 type and T cell effector responses to TCR signaling with costimulation. This system is relevant to inflammatory conditions where T cells play a key role including organ transplantation, rheumatoid arthritis, psoriasis, Crohn's disease, and multiple sclerosis.
MCP-1, CD38, CD40, E-selectin, CD69, IL-8, MIG, PBMC Cytotoxicity, Proliferation, SRB
ITH
2
Vascular Inflammation
Venular endothelial cells/TH2 blasts
Asthma, Allergy, Oncology
The Vascular Inflammation (ITH2) system models vascular inflammation (mixed Th1 and Th2 types), an environment that promotes mast cell, basophil, eosinophil, T and B cell recruitment, vascular permeability, and is pro-angiogenic. This system is relevant for diseases where Th2 type inflammatory conditions play a role such as allergy, asthma, and ulcerative colitis.
MCP-1, Eotaxin-3, VCAM-1, CD38, CD40, E-selectin, P-selectin, CD69, uPAR, Collagen IV, IL-8, MIG, PBMC Cytotoxicity, sIL-17A, sIL-17F, SRB
HD
FSA
g
Tissue Inflammation
Dermal fibroblasts/ PBMC
Autoimmune Disease, Chronic Inflammation, Rheumatoid Arthritis
The Tissue Inflammation (HDFSAg) system models chronic inflammation of the Th1 type and T cell effector responses to TCR signaling with costimulation. This system is relevant to inflammatory conditions where T cells play a key role including rheumatoid arthritis, psoriasis, Crohn's disease, fibrosis, and wound healing biology.
MCP-1, VCAM-1, Collagen I, IP-10, MMP-1, sIL-10, sIL-17A, sIL-17F, sIL-2, sIL-6, SRB, sTGFb, sTNFα, sVEGF, IL-8, MIG, MCSF
DiscoverX Publications
1 E. L. Berg, et al. “Elucidating mechanisms of toxicity using phenotypic data from primary human cell systems-a chemical biology approach for thrombosis-related side effects,” International Journal of Molecular Sciences, vol. 16, no. 1, pp. 1008–1029, 2015.
2 E. L. Berg, et al. “Consideration of the cellular microenvironment: physiologically relevant co-culture systems in drug discovery,” Advanced Drug Delivery Reviews, vol. 69, pp. 190–204, 2014.
3 E. L. Berg, “Systems biology in drug discovery and development,” Drug Discovery Today, vol. 19, no. 2, pp. 113–125, 2014.
4 E. L. Berg and A. O’Mahony, “Complex primary human cell systems for drug discovery,” in Human-based Systems for Translational Research (R. Coleman, ed.), RSC Drug Discovery, pp. 88–109, The Royal Society of Chemistry, 2014.
5 N. C. Kleinstreuer, et al., “Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms,” Nature Biotechnology, vol. 32, no. 6, pp. 583–591, 2014.
6 E. L. Berg, et al., “Building predictive models for mechanism-of-action classification from phenotypic assay data sets,” Journal of Biomolecular Screening, pp. 1260–9, 2013.
7 J. A. Lee and E. L. Berg, “Neoclassic drug discovery the case for lead generation using phenotypic and functional approaches,” Journal of Biomolecular Screening, p. 1087057113506118, 2013.
8 A. C. Melton, et al., “Regulation of IL-17A production is distinct from IL-17F in a primary human cell co- culture model of T cell-mediated B cell activation,” PLOS ONE, vol. 8, no. 3, p. e58966, 2013.
9 G. Bergamini, et al., “A selective inhibitor reveals PI3Kγ dependence of T(H)17 cell differentiation,” Nature Chemical Biology, vol. 8, no. 6, pp. 576–582, 2012.
10 E. L. Berg, et al., “Chemical target and pathway toxicity mechanisms defined in primary human cell systems,” Journal of Pharmacological and Toxicological Methods, vol. 61, no. 1, pp. 3–15, 2010.
11 K. A. Houck, et al., “Profiling bioactivity of the ToxCast chemical library using BioMAP primary human cell systems,” Journal of Biomolecular Screening, 2009.
12 E. L. Berg, et al., “Characterization of compound mechanisms and secondary activities by BioMAP analysis,” Journal of Pharmacological and Toxicological Methods, vol. 53, no. 1, pp. 67–74, 2006.
13 E. L. Berg, et al., “Approaches to the analysis of cell signaling networks and their application in drug discovery.,” Current Opinion in Drug Discovery & Development, vol. 8, no. 1, pp. 107–114, 2005.
14 E. L. Berg, et al., “Biological complexity and drug discovery: a practical systems biology approach,” IEE Proceedings-Systems Biology, vol. 152, no. 4, pp. 201–206, 2005.
15 E. C. Butcher, et al., “Systems biology in drug discovery,” Nature Biotechnology, vol. 22, no. 10, pp. 1253–1259,2004.
16 E. J. Kunkel, et al., “Rapid structure-activity and selectivity analysis of kinase inhibitors by BioMAP analysis in complex human primary cell-based models,” Assay Drug Development Technologies, vol. 2, no. 4, pp. 431–442, 2004.
17 E. J. Kunkel, et al., “An integrative biology approach for analysis of drug action in models of human vascular inflammation,” The FASEB Journal, vol. 18, no. 11, pp. 1279–1281, 2004.
18 I. Plavec, et al., “Method for analyzing signaling networks in complex cellular systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 5, pp. 1223–1228, 2004.
Client-DiscoverX Co-Publications
1 A. Hammitzsch, et al., “CBP30, a selective CBP/p300 bromodomain inhibitor, suppresses human Th17 responses,” Proceedings of the National Academy of Sciences, vol. 112, no. 34, pp. 10768–10773, 2015.
2 P. Haselmayer, et al., “Characterization of novel PI3Kδ inhibitors as potential therapeutics for SLE and lupus nephritis in pre-clinical studies,” Frontiers in Immunology, vol. 5, 2014.
3 P. Ciceri, S. Muller, et al., “Dual kinase-bromodomain inhibitors for rationally designed polypharmacology,” Nature Chemical Biology, vol. 10, no. 4, pp. 305–312, 2014.
4 D. Xu, et al., “RN486, a selective Bruton’s tyrosine kinase inhibitor, abrogates immune hypersensitivity responses and arthritis in rodents,” Journal of Pharmacology and Experimental Therapeutics, vol. 341, no. 1, pp. 90–103, 2012.
5 O. Williams, et al., “Discovery of dual inhibitors of the immune cell PI3Ks p110δ and p110γ: A prototype for new anti- inflammatory drugs,” Chemistry & Biology, vol. 17, no. 2, pp. 123–134, 2010.
6 J. L. Garrison, et al., “A substrate-specific inhibitor of protein translocation into the endoplasmic reticulum,” Nature, vol. 436, no. 7048, pp. 285–289, 2005.
Client Only Publications Featuring BioMAP Profiling
1. A. Y. Zhong, et al., “Targeting interleukin-2-inducible T-cell kinase (ITK) and resting lymphocyte kinase (RLK) using a novel covalent inhibitor PRN694,” Journal of Biological Chemistry, vol. 290, no. 10, pp. 5960–5978, 2015.
2 G. O. Gillard, et al., “DMF, but not other fumarates, inhibits NF-δB activity in vitro in an Nrf2-independent manner,” Journal of Neuroimmunology, vol. 283, pp. 74–85, 2015.
3 F. Vincent, et al., “Developing predictive assays: The phenotypic screening “rule of 3”,” Science Translational Medicine, vol. 7, no. 293, pp. 293ps15–293ps15, 2015.
4 A. Dittmann, et al., “The commonly used PI3-kinase probe LY294002 is an inhibitor of BET bromodomains,” ACS Chemical Biology, vol. 9, no. 2, pp. 495–502, 2013.
5 T. J. Soos, et al., “CDK/GSK-3 inhibitors as a new approach for the treatment of proliferative renal diseases,” Drug News & Perspectives, vol. 19, no. 6, p. 325, 2006.
6 E. C. Butcher, “Can cell systems biology rescue drug discovery?,” Nature Reviews Drug Discovery, vol. 4, no. 6, pp. 461–467, 2005.
Learn More About BioMAP® from Scientific Literature
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