Berg Ellen univ_pugetsound_12feb2015

Chemical biology approaches for predicting
human drug and chemical safety
Ellen L. Berg, PhD
Scientific Director, BioSeek, a division of DiscoveRx
University of Puget Sound
12 February 2015

BIG DATA and In Vitro Testing
are Transforming
Drug Discovery
and Chemical Safety

BIG
DATA
What is ?
Very large datasets
100+ terabytes
Integration of
diverse data

• Advances in high throughput technologies
BIG
DATA
Why
Now ?

Data Driven Research
OLD or
NEW
Hypothesis 1
Hypothesis 2
Hypothesis 3
Hypothesis 4 . . .

Solution
Incorporate “domain” expertise upfront
Issues
Many hypotheses are generated
Each hypothesis requires validation
Validation requires both computational
and “domain” expertise
Data Driven Research

In Vitro In Vivo
High Thoughput Low Throughput
Fast Slow
Simple Complex

In Vitro In Vivo
Simple
Complex
Too Simple? Too

Biological Systems Are Complex
Scale (meters)
molecules pathways cells tissues humans
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
Human exposureMolecular targets
13
What are the organizing principles?

Biological Systems have a Modular Design
• Great diversity from few components
 A given component can contribute to
“many” functions
• Function depends on “context”
Scale (meters) (Time)
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
10-6 sec 102 sec 104 sec 105 sec 108 sec

. . . and Networked Architecture
components
interactions
Provides:
• Rapid responses to environment
 Efficient information flow
• Framework for control systems
 Feedback mechanisms, etc.
• Tolerance to perturbations (robustness)
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M

Consequences:
• Many potential outcomes
 System “wiring” determines outcome
• “Hidden nature” of feedback mechanisms
 Unexpected fragility
• Hard to make predictions
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
components
interactions
. . . and Networked Architecture

Modular, Networked Architecture
Consequences for Drug Discovery & Chemical Safety
• Drug targets function in multiple biological processes
- Unexpected side effects don’t show up before testing in people
• Drugs and chemicals have effects far downstream of the target
- Hard to predict outcomes
• Problems are amplified as most drugs have multiple targets
- Targets may interact in unexpected ways

Primary Human Cell In Vitro Systems
Bridging the Gap
Scale (meters)
10-9 M 10-8 M 10-7 M 10-6 M 10-5 M 10-4 M 10-3 M 10-2 M 10-1 M 1 M
Human exposureMolecular targets
18
BioMAP Systems

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
19

BioMAP® Systems – Key Features
Primary human cell types
Physiologically relevant “context”
Complex activation settings
Co-cultures
Translational biomarker endpoints
20

Feature Mouse Man
Lifespan 2 Years 70 Years
Size 60 g 60 kg
Environment
Animal facility,
cage-mates
Outside world, people,
animals, etc.
Why Human?
Key differences:
DNA repair mechanisms
Control of blood flow, hemostasis
Immune system status
21

Why Primary Human Cells?
22
• Primary human cells
- Freshly isolated from tissues
- Not adapted to life on plastic
- Remember “where they
came from”
0.4
0.5
0.6
0.7
0.8
0.9
1.0
3C
0.4
0.5
0.6
0.7
0.8
0.9
1.0
3C

0.4
0.5
0.6
0.7
0.8
0.9
1.0
3C
0.4
0.5
0.6
0.7
0.8
0.9
1.0
3C
Why Primary Human Cells?
23
• Primary human cells
- Freshly isolated from tissues
- Not adapted to life on plastic
- Remember “where they
came from”
- Retain regulatory pathways
and “interconnections”

Primary Cells Versus Cell Lines
24
• Cell lines
- Long lived – immortal
- Adapted to plastic
- Aberrant pathways are
turned on
- Chromosome duplications
& deletions

Primary Cells Versus Cell Lines
25
• Cell lines
- Long lived – immortal
- Adapted to plastic
- Aberrant pathways are
turned on
- Chromosome duplications &
deletions
- Many regulatory mechanisms
lost
- Pathways become “isolated”

26
Why Complex Activation Conditions?

TNF-
VCAM
One Pathway Active
Expression Level
27

TNF-
VCAM
One Pathway Active
X
XX
Expression Level
Activation of a single pathway
provides limited information
28

TNF-
VCAM
One Pathway ActiveMultiple Pathways Active
IFN
Synergy
IL-1
Feedback
Expression Level
29

TNF-
VCAM
IFN
IL-1
X
XXActivation of multiple pathways
provides more information
Expression Level
More physiologically relevant
30

TNF-
VCAM
IFN
IL-1
X
Expression Level
31
PATTERNMechanism

TNF-
VCAM
IFN
IL-1
X
Expression Level
32
Is there something special
about VCAM?

TNF-
VCAM
IFN
IL-1
X
Expression Level
33
Yes.
VCAM belongs to a special
class of proteins:
translational biomarkers

Closer to the disease process
Downstream of multiple pathways and integrate information
“Decision-making”
Measured by clinicians to guide therapy
Predictive
Benefits of Translational Biomarkers
mRNA,
epigenome
Phospho-sites,
intracellular proteins,
metabolome
Cell surface,
secreted molecules
34

Primary Human Cell Systems Panels
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
Endothelial
Cells
Endothelial
Cells
PBMC +
Endothelial
Cells
PBMC +
Endothelial
Cells
Bronchial
epithelial cells
Coronary
artery SMC
Fibroblasts
Keratinocytes
+ 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-CSF
CD38, 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
EndpointTypes
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
35

Panel of Primary Human Cell Systems
BioMAP® Predictive Tox Panel
3C 4H LPS SAg BE3C CASM3C HDF3CGF KF3CT
Endothelial
Cells
Endothelial
Cells
PBMC +
Endothelial
Cells
PBMC +
Endothelial
Cells
Bronchial
epithelial cells
Coronary
artery SMC
Fibroblasts
Keratinocytes
+ 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-CSF
CD38, 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
EndpointTypes
Disease / Tissue
Relevance
BioMAP System
Primary Human Cell
Types
Stimuli
! ! ! ! !
36

• Applying in vitro human cell systems in order to
understanding chemical toxicity mechanisms
- Analyzing a large chemical biology data set
- Connecting chemicals to biological activities
- Integrating this data to connect pathways and build
knowledge frameworks
Case Study
37

Case Study:
Understanding Chemical
Toxicity Mechanisms
38

• GOAL: To develop a cost-effective approach for efficiently
prioritizing the toxicity testing of thousands of chemicals
• APPROACH:
- Collect a large set of known chemicals
• Large and diverse collection, well characterized
• In vivo toxicology studies, exposure information, human data
- Test in a large number of in vitro assays
• Diverse (biochemical, target-based, phenotypic, novel technologies)
• QUESTIONS:
- Can we identify in vitro assays that predict in vivo effects?
•  “Replace, reduce and refine” animal testing
- Can this data help us understand toxicity mechanisms?
- How can we use this data in risk assessment?
EPA ToxCastTM Program

> 1100 Chemicals Profiled in BioMAP Systems
>300,000 Datapoint Chemical Biology Dataset for ToxCast
BioMAP Assays
Chemicals
Grouped by Biological Similarity
GroupedbyChemicalClass
Houck, JBS, 2009
Kleinstreuer, NBT, 2014

Unsupervised Data Analysis
Self Organizing Maps (SOMs)
Clustering
• Looking for patterns in the data  Insights

SOM Analysis Identified a Cluster of Chemicals
Key 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, 32:583-91.43
SOM Analysis Identified a Cluster of Chemicals
Aryl Hydrocarbon Receptor Agonists

Tissue Factor (TF)
Primary Cellular Initiator of Blood Coagulation
RW Colman 2006 J. Exp. Med
Blood
Coagulation
44
Thrombosis

Thrombosis is Required for Normal Wound Healing
45

• Pathologic setting – aberrant coagulation  thrombosis
- The formation of a blood clot (coagulation) within a vein
- Deep vein thrombosis (DVT), stroke, pulmonary embolism 
thrombi break off and get lodged in the lung
- Ebola infection produces a consumptive coagulopathy
Thrombosis Can Also Be Pathologic
SMC
Endothelial cells
Vessel Lumenplatelets in fibrin clot
46

• 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
47

• 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?
48

Analysis of Reference Compounds
Test Agents Mechanism
Confidence in
Mechanism
2-Mercaptobenzothiazole AhR agonist High
3-Hydroxyfluorene AhR agonist High
Benzo(b)fluoranthene AhR agonist High
C.I Solvent yellow 14 AhR agonist High
FICZ AhR agonist High
Abiraterone CYP17A Inhibitor High
Ketoconazole CYP17A Inhibitor High
Clomiphene citrate Estrogen R Antagonist High
Histamine H1R agonist High
Histamine Phosphate H1R agonist High
Cobalt(II) Chloride Hexahydrate HIF-1α Inducer High
Tin(II) Chloride HIF-1α Inducer High
Chloroquine Phosphate Lysosome Inhibitor High
Primaquine Diphosphate Lysosome Inhibitor High
Temsirolimus mTOR Inhibitor High
Torin-1 mTOR Inhibitor High
Torin-2 mTOR Inhibitor High
Bryolog PKC activator High
Bryostatin PKC activator High
Bryostatin 1 PKC activator High
Phorbol 12-myristate 13-acetate PKC activator High
Phorbol 12,13-didecanoate PKC activator High
Picolog PKC activator High
3,5,3-Triiodothyronine Thyroid H R agonist Good
Concanamycin A Vacuolar ATPase Inhibitor Good
Mifamurtide NOD2 agonist Good
Oncostatin M OSM R agonist Good
Ethanol Organic Solvent Good
PAz-PC Oxidized phospholipid Good
Z-FA-FMK Cysteine protease Inhibitor Good
8-Hydroxyquinoline Chelating agent Unknown
A 205804 ICAM, E-selectin inhibitor Unknown
AZD-4547 FGFR Inhibitor Unknown
Crizotinib ALK, c-met Inhibitor Unknown
Desloratadine H1R antagonist Unknown
Dodecylbenzene Industrial chemical Unknown
Fenaminosulf Fungicide Unknown
GDC-0879 B-Raf Inhibitor Unknown
GW9662 PPARγ agonist Unknown
Imatinib PDGFR, c-Kit, Bcr-Abl Inhibitor Unknown
KN93 CaMKII Inhibitor Unknown
Linoleic Acid Ethyl Ester Fatty Acid Unknown
Mancozeb Fungicide Unknown
MK-2206 AKT Inhibitor Unknown
Mometasone furoate GR agonist Unknown
N-Ethylmaleimide Alkylating agent Unknown
PP3 SRC Kinase Inhibitor Unknown
Primidone GABA R agonist Unknown
Sulindac Sulfide NSAID Unknown
Terconazole Anti-fungal Unknown
Tris(1,3-dichloro-2-propyl) phosphate Flame retardant Unknown
TX006146 Unknown Unknown
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
49
• 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
50

The Autophagy Connection
Lysosomal
Function

• Data mining large chemical biology data sets can produce
detailed pathway frameworks and mechanistic hypotheses
for important toxicity mechanisms
• Mechanistic Hypothesis: thrombosis-related side effects may
be caused by 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
The Autophagy Connection
Tissue Factor, Autophagy & Thrombosis
57

• GOAL: To develop a cost-effective approach for efficiently
prioritizing the toxicity testing of thousands of chemicals
• Can we identify in vitro assays that predict in vivo effects?
-  “Replace, reduce and refine” animal testing
• Can this data help us understand toxicity mechanisms?
• How can we use this data in risk assessment?
EPA ToxCastTM Program
YES, in
some
cases
YES, in
some
cases
AOPs

Adverse Outcome Pathway Framework
MIE
Key
Event
Adverse
Outcome
Key
Event
Key
Event
Molecular
Initiating Event Clinical Effect
• Framework for integrating mode of action hypotheses to
outcomes for chemical risk assessment (OECD)
- http://www.oecd.org/chemicalsafety/testing/adverse-outcome-pathways-
molecular-screening-and-toxicogenomics.htm
• Focused on the clinical outcome
- Anchored at both ends
59

AOP for DVT
MIE
Key
Event
Adverse
Outcome
Inhibition of
mTOR
Upregulation
of Tissue
Factor
Deep Vein
Thrombosis
Initiation of
Coagulation
Key
Event
Key
Event
Molecular
Increase in
Autophagic
Vacuolization
60

AOP for DVT
MIE
Key
Event
Adverse
Outcome
Inhibition of
mTOR
Upregulation
of Tissue
Factor
Deep Vein
Thrombosis
Initiation of
Coagulation
Key
Event
Key
Event
Molecular
MIE
Activation of
AhR
Increase in
Autophagic
Vacuolization
Key
Event
Inhibition of
NPC1
Key
Event
HDF3CGF
In vitro
disease model
3C
3C 4H LPS
Endothelial
Cells
Endothelial
Cells
PBMC +
Endothelial
Cells
P
En
Th1 Th2 TLR4
BioMAP System
Primary Human Cell
Types
Stimuli
! ! !
61

• Chemical profiling in human cell systems generates
activity profiles that can be used to:
- Group chemicals into bioactivity classes
- Generate MoA hypotheses
- Identify activities that may correlate with in vivo outcomes
• High throughput in vitro data is most informative when
combined with external information
- Known targets
- In vivo bioactivities
Summary
Confidential62

The Future
• BIG DATA and in vitro testing are transforming
drug discovery and chemical safety
BUT
• We need biologists who can do data science
• So learn programming, statistics, R, and data
mining methods

• 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
64

Resources
• EPA’s ToxCast Program
- http://www.epa.gov/ncct/toxcast/
• Tox21
- http://ntp.niehs.nih.gov/go/tox21
• NCATS (National Center for Advancing Translational
Sciences)
- http://www.ncats.nih.gov
• Open FDA
- https://open.fda.gov

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
eberg@bioseekinc.com
66

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