Berg ellen slas 2015 11_february2015

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

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

9

Panel of Primary Human Cell SystemsBioMAP® Predictive Tox Panel

3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT

Endothelial Cells

Endothelial Cells

PBMC + Endothelial

Cells

PBMC + Endothelial

Cells


Coronary artery SMC





a, PGE2 IL-8 IL-1a

IL-8, IL-6, SAA

IL-8 IL-1α



VCAM-1, Eotaxin-3,

MCP-1

VCAM-1, MCP-1


IP-10, MIG, HLA-DR


DR

VCAM-1, IP-10, MIG





uPAR,




TGFβ1

Vascular Biology

TM, TF, uPAR, EC


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


BioMAP System


Stimuli

! ! ! ! !

10

• Challenges:

- Cells and assays are expensive

- Primary cells are variable

• Solutions:

- 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

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(Dru

g/D

MS

O c

on

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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

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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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BioMAP Profiling: Example ProfileTrametinib (MekinistTM) MEK Kinase Inhibitor

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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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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

Prolif. Fibroblast

Prolif.

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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


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



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)

• Decreased EC proliferation; increased cell surface KDR (receptor for VEGF)

- Tissue remodeling and metastasis• Decreased proteolysis (uPAR, tPA, PAI-1), matrix metalloprotease (MMP1, TIMPs)

uPARuPAR

MMP1

PAI-1

tPA EGFRMMP1

PAI-1

TIMP-1

TIMP-2

EC

Prolif.

VEGFR2

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


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



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


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



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

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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)

- 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

25

Trametinib + Dabrafenib Combination

Dabrafenib

Trametinib

• Combination approved by the

US FDA June 2014 for the

treatment of BRAF V600E

mutation-positive unresectable

or metastatic melanoma

• Improved patient responses

• Reduced incidence of cutaneous

squamous cell carcinoma

Modeling Tumor-Host Microenvironments

27

Human In Vitro Models of Tumor MicroenvironmentBioMAP CRC 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, CK2028

Dabrafenib (B-raf) Trametinib (MEK) Dabrafenib +Trametinib

29

Combination Study Example: Dabrafenib (B-Raf) + Trametinib (MEK Inhibitor)


• 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

30



• 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) and reduced skin proliferative disorders

31


Case Study: Elucidating Mechanisms of Toxicity

32

• GOAL: To develop a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals

• Profiling in BioMAP Systems since 2007: • > 1100 Chemicals Profiled

• > 300,000 Datapoint Chemical Biology Dataset for ToxCast

EPA ToxCastTM Program

• Patterns in the data Insights

Cluster of Chemicals Identified by SOMKey Feature: Increased Tissue Factor

• Cluster of chemicals defined by their BioMAP signature

- Key feature: Increased Tissue Factor (TF) in BioMAP 3C system

Nicole Kleinstreuer, et al., NBT, 2014

Tissue Factor

• Phenotypic signature of compounds in SOM cluster #57

- Box and whisker plot for cluster 57 representing a signature for AhR activation

• Compounds: AhR Agonists

- 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, 32:583-91.35

Cluster of Chemicals Identified by SOMAryl Hydrocarbon Receptor Agonists

Tissue Factor (TF)Primary Cellular Initiator of Blood Coagulation

RW Colman 2006 J. Exp. Med

Blood

Coagulation

36

Thrombosis

Thrombosis is Required for Normal Wound Healing

37

• Pathologic setting – aberrant coagulation thrombosis

- The formation of a blood clot (coagulation) within a vein

- Clinical manifestations• Deep vein thrombosis (DVT), stroke, and pulmonary embolism thrombi

break off and get lodged in the lung

• Ebola – consumptive coagulopathy

Thrombosis Can Also Be Pathologic

Smooth muscle cells

Endothelial cells

Vessel Lumenplatelets in fibrin clot

38

• 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 (oncology drug)

Mechanisms / Drugs Associated with Thrombosis-Related Side Effects

All show increased Tissue Factor levels in the BioMAP 3C System

39

• Leverage our large chemical biology database of >3800 compounds

• Search the database for all compounds / test agents that increase TF in the 3C system

- How common is this activity?

- What are the mechanisms represented?

- Is there a connection that helps us better understand the regulation of thrombosis?

Are These Mechanisms Connected?

40

Analysis of Reference CompoundsTest 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



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

41

• Increased TF is an uncommon activity

• 55/3187 compounds (1.7%)

Implicate Autophagy

Berg, et al., IJMS, 2015

Autophagy

• Intracellular self-degradation system

• Cellular response to nutrient deprivation

• Also contributes to recycling of dysfunctional organelles, handling of protein aggregates, bacteria and viruses42

The Autophagy Connection


Lysosomal

Function


Lysosomal

Function


Lysosomal

Function


Lysosomal

Function


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

50

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


Endothelial Cells

Endothelial Cells

PBMC + Endothelial

Cells

PBMC + Endothelial

Cells


Coronary artery SMC





a, PGE2 IL-8 IL-1a

IL-8, IL-6, SAA

IL-8 IL-1α



VCAM-1, Eotaxin-3,

MCP-1

VCAM-1, MCP-1


IP-10, MIG, HLA-DR


DR

VCAM-1, IP-10, MIG





uPAR,




TGFβ1

Vascular Biology

TM, TF, uPAR, EC


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


BioMAP System


Stimuli

! ! ! ! !

51

MIE

Inhibition of

Estrogen R

• Profiling across primary human cell systems can be applied in target-based drug discovery for:

- Defining characteristics of good drugs• Confirming expected target activities

• Identifying unexpected activities (target or off-target)

• Concentration-response characteristics (dose resistance)

- Guide in vivo preclinical and clinical studies• Dose selection, safety window

• Identify new biomarkers or pathways to track

• Preview drug combination effects

• Applications at the organizational level:

- Connect target biology across programs and therapeutic areas

- Improve safety prediction early in discovery

- Facilitate opportunities for new indication discovery

In Conclusion

52

• BioSeek

- Mark A. Polokoff

- Dat Nguyen

- Xitong Li

- Antal Berenyi

- Alison O’Mahony

• NIEHS (ILS)

- Nicole Kleinstreuer

Acknowledgements

• EPA

- Keith Houck

- Richard Judson

- David Dix

- Bob Kavlock

- David Reif

- Matt Martin

- Ann Richard

- Tom Knudsen

53

Contact:

Ellen L. Berg, PhD,

Scientific Director

BioSeek, a division of DiscoveRx

310 Utah Avenue, Suite 100

South San Francisco, CA 94080

+1-650-416-7621

[email protected]

54