defining chemical target and pathway toxicity mechanisms with primary human cell systems
DESCRIPTION
Presentation at eCheminfo, Bryn Mawr, PA, 15 Oct 2009TRANSCRIPT
Defining Chemical Target and Pathway Toxicity Mechanisms with Primary Human Cell Systems
Ellen L. Berg, PhD
BioSeek, Inc.
eChemInfo, Philadelphia PAOctober 2009
BioSeek
BioSeek
Presentation Overview
• Why is predictive toxicology so hard?
Biological complexity
• A role for cell-based assays in predictive toxicology
BioMAP primary human cell systems
Examples & challenges
2
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Biological Systems Are Complex
Scale (meters) (Time)
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
10-6 sec 102 sec 104 sec 105 sec 108 sec
Modular design / Networked architecture
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Biological Systems have a Modular Design
• Great diversity from few components
A given component can contribute to “many”
functions (“Legos”)
• The ability to compartmentalize
Safety feature to limit damage
Scale (meters) (Time)
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 M10-6 sec 102 sec 104 sec 105 sec 108 sec
components
interactions
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Biological Systems have a Networked Architecture
components
interactions
• Rapid responses to environment
Efficient information flow
• Framework for control systems
Feedback mechanisms, etc.
• Many potential outcomes
System “wiring” determines outcome
Scale (meters) (Time)
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 M10-6 sec 102 sec 104 sec 105 sec 108 sec
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Biological Systems are Dominated by Control Systems
• Dominate in networks with demanding
performance requirements
• Important for providing tolerance to
perturbations (robustness)
• “Hidden nature”
Unexpected fragility
Scale (meters) (Time)
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 M10-6 sec 102 sec 104 sec 105 sec 108 sec
components
interactions
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Modular, Networked ArchitectureConsequences for Drug Discovery & Predictive Toxicology
• Drug targets function in multiple biological processes
Unexpected side effects are often only discovered in clinical testing
• Drugs can have effects far downstream of the target
Hard to predict outcomes
• Problems become amplified since most drugs have multiple
targets
Targets may interact in unexpected ways
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BioMAP® Technology Bridging the Gap
Scale (meters) (Time)
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
10-6 sec 102 sec 104 sec 105 sec 108 sec
BioMAP Technology
8
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BioMAP® Technology Platform
Assays
Human primary cells Disease-like culture conditions
LPS
BF4T
SM3C
Profile Database Informatics
Biological responses to drugs and stored in the database
Specialized informatics tools are used to mine and analyze biological data
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• BioMAP Systems are cell-based assays that BioMAP Systems are cell-based assays that model complex human disease biologymodel complex human disease biology
• Human primary cell types
• Co-cultures, multiple stimulation factors
• Disease & tissue-relevant culture conditions
BioMAP® Technology Platform
Assays
LPS
BF4T
SM3C
Human primary cells Disease-like culture conditions
>30 BioMAP Systems
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• Assay endpoints include human clinical Assay endpoints include human clinical biomarkers and risk factors (proteins)biomarkers and risk factors (proteins)
Cytokines, chemokines, mediators
Cell surface receptors
Proteases, enzymes (MMPs, plasminogen activators)
Others (hemostatic factors, matrix components)
Clinically relevant, extracellular biomarkers
BioMAP® Technology Platform
Assays
LPS
BF4T
SM3C
Human primary cells Disease-like culture conditions
>30 BioMAP Systems
BioSeek
AssaysAssays
Human primary cells Disease-like culture conditions
LPS
BF4T
SM3C
Profile DatabaseProfile Database
Biological responses to drugs and stored in the database
BioMAP® Technology Platform
• > 2000 agents> 2000 agents
• Approved drugsApproved drugs
• Clinical stage & Clinical stage &
failed drugsfailed drugs
• Experimental Experimental
compoundscompounds
• BiologicsBiologics
• ToxicantsToxicants
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BioMAP Systems are Validated Corticosteroids (Prednisolone) Are Active in Inflammation Systems
BioMAP Systems
Readout Parameters (Biomarkers)Cytotoxicity Readouts
Lo
g e
xpre
ssio
n r
atio
(Dru
g/D
MS
O c
ontr
ol)
Control (no drug)
99% significance envelope
DoseResponse
Profiles retain shape over multiple concentrationsProfiles retain shape over multiple concentrations
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E-selectin
TNF-
IL-8
BioMAP Systems are Validated Activities of Corticosteroids Match Clinical Effects
PGE2
IL-8MCP-1
MCP-1, IL-8, E-sel. decreaseLeukocyte recruitment
Many, e.g. Jilma et al., 2000
PGE2 decreasePain, swellingSebaldt et al., 1990
Readouts in BioMAP show the same pattern as has been reported for patients receiving steroid therapy
Readouts in BioMAP show the same pattern as has been reported for patients receiving steroid therapy
Collagen I & III
Collagen I, III decreaseSkin atrophyAutio et al., 1994
MMP-1
Lo
g e
xpre
ssio
n r
atio
(Dru
g/D
MS
O c
ontr
ol)
PAI-1SAA
PAI-1, SAA increaseCV complications
Sartori et al., 1999Fyfe et al., 1997
PAI-1
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Predictive Toxicology
• Characterizing chemicals for mode of action
• Defining toxicity mechanisms
Pathway mechanisms
Unknown targets or target-independent mechanisms
• Chemical prioritization
ToxCastTM Phase I Proof-of-concept study
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MicrotubuleStabilizers
Mitochondrial ET chain
Retinoids
Hsp90
CDK
NFB
MEK
DNAsynthesis
JNK
Proteinsynthesis
MicrotubuleDestabilizers
Estrogen R
PI-3K
Ca++
Mobilization
Classification of Drugs By Mechanism Pairwise Correlation of BioMAP Reveals Functional Similarities
mTOR
PKC Activation
p38 MAPK
HMG-CoAreductase
Calcineurin
Transcription
Mechanism of Action(On-Target)
PathwayRelationships
BioSeek
Defining Toxicity Mechanisms
Sodium Azide Chlorambucil
Cycloheximide A23187 Thapsigargin
Oxidative /Nitrosative Stress
ER Stress Unfolded Protein Response
Rotenone
BioSeek
ToxCast ChemicalsDiversity of Mechanisms
18
Houck, 2009
BioSeek
BioMAP Profiles of Compounds in Example Cluster
• Pyraclostrobin and Tryfloxystrobin are strobilium herbicides
• BioMAP profiles are highly similar to one another
Tryfloxystrobin
Pyraclostrobin
Pyraclostrobin, 13.33 M
Pyraclostrobin, 4.44 M
Pyraclostrobin, 1.48 M
Tryfloxystrobin, 40 M
Tryfloxystrobin, 13.33 M
Tryfloxystrobin, 4.44 M
BioSeek
Trifluoxystrobin
Reference Database Search Identifies MOA
Tryfloxystrobin
Search results:
• Search reference database for compounds with similar profiles Correlation (Pearson-based) metrics
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Trifluoxystrobin
Search results:
• Optimized search algorithms use combination of metrics
• Confirms mechanism of action
• Of >3000 reference profiles searched, only 7
compounds are significantly similar
• All are known inhibitors of mitochondrial
function
Tryfloxystrobin
Reference Database Search Identifies MOA
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cAMP Elevation
DNA Alkylation
NFB
Tubulin Inhibition
Mitochondrial Dysfunction
ToxCast ChemicalsDiversity of Mechanisms
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Challenges for Predictive Toxicology Applications
• Data require specialized analysis approaches
Compound cytotoxicities complicate data interpretation
• Assay readouts reflect activities of multiple targets
Concentration effects are complicated
• Diversity of assay endpoint ranges and dynamics
• Assay readouts can be independent
• Can only predict human toxicity, not animal toxicities
Species differences are amplified in complex systems
• Limited data available for validation
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Cytotoxicity Effects
• Cell type selective toxicity
• Activation state-dependent toxicity
Toxicity
Cell Type
Endothelial Cells Epithelial Cells SMC Fibroblasts EC
Activation State
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Lo
g e
xpre
ssio
n r
atio
(Com
poun
d /D
MS
O c
ontr
ol)
Readout Parameters (Biomarkers)
BioMAP Systems
Readout Parameters (Biomarkers)
BioMAP Systems
Rapamycin Genistein
Concentration Effects
• Diversity of concentration effects
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Concentration Effects
• EC50, Slope, Magnitude of effect
• Plateau?
• Cytotoxicity
Rapamycin Genistein
No effect
MaxInhibition
Increasing Concentration Increasing Concentration
Cytotoxicity
HLA
-DR
Lev
els
Plateau
EC50Magnitude
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Concentration Effects
• Bimodal distribution - Reverse dosing
No effect
MaxInhibition
Increasing Concentration
Rea
dout
Lev
els
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Summary
• Biological complexity is a challenge to predictive toxicology
• Cell-based assays have a role in defining mechanisms of action
Complex assays are effective
Target and pathway mechanisms can be identified
• Specialized approaches to data analysis are required
Data are heterogeneous and multiparameter
Analysis requires managing concentration and cytotoxicity effects
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Acknowledgements
• BioSeek Alison O’Mahony Jennifer Melrose Elen Rosler Sylvie Privat Dat Nguyen Mark Polokoff Stephanie Tong Jian Yang Antal Berenyi
• Stanford Eugene Butcher Rob Tibshirani Trevor Hastie
• EPA David Dix Keith Houck Richard Judson Bob Kavlock
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