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−

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CD

106/

VCA

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141/

Thro

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CC

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L10/

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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

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VCA

M−1

CD

141/

Thro

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142/

Tiss

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CD

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E−Se

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CD

69

CXC

L8/IL

−8

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B c

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PBM

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ity

Secr

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sIL−

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sIL−

2

sIL−

6

sTN

F−al

pha

CC

L2/M

CP−

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CD

54/IC

AM

−1

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L10/

IP−1

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L8/IL

−8

CXC

L9/M

IG

IL−1

alph

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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).

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Increasedexpression

Decreasedexpression

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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

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Biomark

erB

Biomark

erC

Biomark

erD

Biomark

erE

Biomark

erF

Biomark

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Biomark

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Biomark

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Biomark

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Biomark

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Biomark

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Biomark

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-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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