Providing the Missing Link:
The Exposure Ontology ExO
RASS
December 11, 2013
Elaine Cohen Hubal
Chemical Safety for Sustainability Research Program
Office of Research and Development
National Center for Computational Toxicology
Disclaimer. Although this work was reviewed by EPA and
approved for presentation, it may not necessarily reflect official
Agency policy.
May 18, 2011
What Is Exposure Science?
• The bridge between the sources of chemical, physical and
biological agents and human health
– Provides crucial information to estimate real-life risks to health
and to identify the most effective ways to prevent and reduce
these risks.
www.isesweb.org
Office of Research and Development
National Center for Computational Toxicology
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Exposure for Risk Evaluation: Approaches
•
•
•
•
•
Questionnaire based metrics (epidemiology)
Surrogate exposure metrics (ambient measures)
Exposure measurement (direct or point-of-contact)
Biomonitoring (NHANES)
Modeled estimates (indirect or scenario evaluation)
TRANSPORT, TRANSFORMATION, and
FATE PROCESS MODELS
SOURCE / STRESSOR
FORMATION
ADVERSE
OUTCOME
EXPOSURE
MODELS
TRANSPORT/
TRANSFORMATION
DOSE
PBPK
MODELS
ENVIRONMENTAL
CHARACTERIZATION
EXPOSURE
ACTIVITY
PATTERN
•Individual
•Community
•Population
Office of Research and Development
National Center for Computational Toxicology
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2
Systems Biology: Exposure at All Levels of Biological Organization
Stressor
Perturbation
Biological
Receptor
Perturbation
Outcome
Environmental
Source
Ambient
Exposure
Environmental
Source
Personal
Exposure
Population
Individual
Internal Exposure
(Tissue Dose)
Tissue
Dose to Cell
Cell
Dose of Stressor
Molecules
Office of Research and Development
National Center for Computational Toxicology
Biological
Molecules
Disease
Incidence/Prevalence
Disease State
(Changes to Health Status)
Dynamic Tissue Changes
(Tissue Injury)
Dynamic Cell Changes
(Alteration in Cell Division,
Cell Death)
Dynamic Changes in
Intracellular Processes
Cohen Hubal, JESEE, 2008
3
Exposure for Translation
Susceptibility
(Genetic Variants / Epigentic Modifications)
Biological Insight
(Toxicity Pathways)
Environmental Factors
(Exposure)
Improved Measures of Individual Etiological
Processes and Individual Exposures
Key Perturbations
Key Targets
Biomarkers
Indicators Metrics
Personal Risk Profile
Information
Extrapolation for
Risk Assessment
Education
Personal Risk
Management
Office of Research and Development Public Health Policy
National Center for Computational Toxicology Prevention
Cohen Hubal, et al. JTEH, 2010
Knowledge Systems – Enabling Hypothesis Development
• Computational Techniques – Two Branches
A combination of discovery and engineering (mechanistic)-based
modeling approaches required for hypothesis development and testing
• Knowledge-discovery
– Data-collection, mining, and analysis
– Required to extract information from extant data on critical
exposure determinants, link exposure information with toxicity
data, and identify limitations and gaps in exposure data.
• Mechanistic (dynamic) simulation
– Mathematical modeling at various levels of detail
– Required to model the human-environment system and to test our
understanding of this system.
Office of Research and Development
National Center for Computational Toxicology
5
Exposure-Hazard Knowledge System
• Translation of HTP hazard information requires holistic risk
assessment knowledge system
– Include ontologies, databases, linkages
– Facilitate computerized collection, organization, and retrieval of
exposure, hazard, and susceptibility information
– Define relationships, allow automated reasoning, facilitate meta
analyses
• Standardized exposure ontologies required to
– Develop biologically-relevant exposure metrics
– Design and interpret in vitro toxicity tests
– Incorporate information on susceptibility and background
exposures to assess individual and population-level risks
Office of Research and Development
National Center for Computational Toxicology
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Schematic of ontologies, databases and ontology/database
linkages needed for the efficient development of a
Foods-for-Health Knowledge System
MC Lange, et al. (2007) A multi-ontology framework to
Office of Research and Development
guide National
agriculture
food Toxicology
towards diet and health.
Center for and
Computational
J Sci Food and Ag 87(8)1427-34.
7
Exposure Data Sources
Production/
Import
Volumes
Source/Stressor
Formation
EPA IUR
Environmental
Concentration
Exposure
EPA TRI
Chemicals in
Consumer
Products
DOE GHG
EU ESIS
Product
Usage
Information
Transport/Fate
Environmental
Releases
EPA HPVIS
EPA Pesticide
Usage Data
Production/
Process
Information
UK Pharmaceutical Usage
Indoor Air
Monitoring
Data
Human
Exposure
Monitoring
Household
Products DB
Cosmetic
Voluntary
Reg. DB
Environmental
Transformation
EPA HPVIS
ATSDR Tox
Profiles
ECOTOX DB
DEA NFLIS
DOE IndoorAir
NHEXAS
CTEPP
NHEXAS
EUROPA PestDiet
Outdoor Air
Monitoring
Data
Activity
Patterns
Information
Environmental
Fate Simulator
CESAR
EPA NATA
EPA AIRS/AFS
Exposure
Limits
UN IPCC GHG
EPA NHAPS
EPA CHAD
Human
Biological
Monitoring
Data
NIOSH NOES
AIHA WEEL
OSHA PEL
CDC NHANES
NHEXAS
CTEPP
Peter Egeghy, NERL
Office of Research and Development
National Center for Computational Toxicology
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http://actor.epa.gov
Exposure Data Landscape
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National Center for Computational Toxicology
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Network of exposure taxonomy used in ACToR; Egeghy et al, 2011
Exposure Data Landscape
Number of Unique Chemicals
10000
1000
100
10
1
Production
Volume
Use
Category
Food Use
Chemical
Release
Water
Conc.
Soil Conc.
Food Conc.
Air Conc.
Biomarker
Conc.
Data Type
Office of Research and Development
National Center for Computational Toxicology
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Number of unique chemicals by data type in ACToR; Egeghy et al, 2011
Exposure Data Collection and Access: ACToR,
Aggregated Computational Toxicology Resource
http://actor.epa.gov/
ACToR API
Chemical
ID,
Structure
Chemical
ACToR Core
ToxRefDB
Tabular Data,
Links to Web
Resources
In Vivo Study
Data - OPP
Internet
Office ofSearches
Research and Development
DSSTox
ToxCastDB
ToxCast Data –
NCCT, ORD,
Collaborators
Chemical ID &
Structure QC,
Inventory
Tracking
ExpoCastDB
Exposure Data –
NERL, NCCT
National Center for Computational Toxicology
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Exposure Data Collection and Access :
ExpoCastDB Goals
• Consolidate observational human exposure data, improve access
and provide links to health related data
• House measurements from human exposure studies
• Encourage standardized reporting of observational exposure information
• Provide separate interface with inner workings of ACToR
• Facilitate linkages with toxicity data, environmental fate data, chemical
manufacture information
• Provide basic user functions
http://actor.epa.gov/
• Visualization (e.g., scatterplots, probability plots, goodness-of-fit)
• Obtain summary statistics and estimate distributional parameters
• Download customized datasets
Office of Research and Development
National Center for Computational Toxicology
Generic_chemical
table in ACToR
Exposure Data Collection and Access: ExpoCastDB
1
1
N
Laboratory method
N
Measure
(Chemical)
N
Technique /
sampling method
N
Location_CV
(cont vocab.)
• Four initial studies
from National
Exposure Research
Laboratory
• Full raw data sets
available for
download
N
1
N
N
1
N
1
N
Sample
Medium
(serum, air, soil, etc.)
N
• Browse data
capability
N
• Descriptive
statistics capabilities
N
Location
N
N
N
N
N
N
N
N
1
N
Subject
N
N
N
N
N
Exposure
Taxonomy
(Assay_
category_
CV table
in ACToR)
N
N
Study
Sources
N
N
N
Study_CV
cont. vocab
1
Office of Research and Development
National Center for Computational Toxicology
N
N
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ExO: An Ontology for Exposure Science
Office of Research and Development
National Center for Computational Toxicology
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Exposure Data Collection and Access:
Design and Evaluation of the Exposure Ontology, ExO
Background:
• Significant progress has been made in collecting and improving access
to genomic, toxicology, and health data
• These information resources lack exposure data required to
– translate molecular insights
– elucidate environmental contributions to diseases
– assess human health risks at the individual and population levels
Aim:
• Facilitate centralization and integration of exposure data to inform
understanding of environmental health
• Bridge gap between exposure science and other environmental health
disciplines
Vehicle:
• Carolyn Mattingly, Mount Desert Island Biological Laboratory
• LRI seed funding, followed by NIEHS RO1
Office of Research and Development
National Center for Computational Toxicology
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Ontologies
• An ontology is a formal representation of knowledge within a domain and
typically consists of classes, the properties of those classes, and the
relationships between these
(Gruber, Int. J. Human-Computer Studies, 1995)
• Many fields are developing ontologies to
– Organizing and analyzing large amounts of complex information from
multiple scientific disciplines
– Provide unprecedented perspective
– Enable more informed hypothesis development
(http://www.obofoundry.org/)
Office of Research and Development
National Center for Computational Toxicology
16
Design and Evaluation of the Exposure Ontology: ExO
• Develop an exposure ontology consistent with those being used
in toxicology and other health sciences
• Facilitate centralization and integration of exposure data to inform
understanding of environmental health
• Bridge gap between exposure science and other environmental
health disciplines
– Initially focus development on human exposure to chemicals
– Ultimately, provide domains that can be extended to
encompass exposure data for the full range of receptors and
stressors
Office of Research and Development
National Center for Computational Toxicology
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Exposure Ontology Working Group
Working Group Member
Institution
Role/expertise
Carolyn Mattingly, PhD
Judith Blake, PhD.
Mount Desert Island
Biological Laboratory
The Jackson Laboratory
Michael Callahan, PhM.
MDB, Inc.
Robin Dodson, ScD.
Silent Spring Institute (SSI)
Facilitator/curated database
development
Facilitator/ontology
development
Core Member/
exposure assessment
Core Member/
exposure research
Member/exposure research
Peter Egeghy, PhD.
US EPA, NERL
Member/exposure research
Jane Hoppin, ScD.
NIEHS
Member/epidemiology
Thomas McKone, PhD.
Lawrence Berkeley National
Laboratory (LBNL)
Core Member/
exposure research
Ruthann Rudel, MS.
Silent Spring Institute (SSI)
Member/exposure research
Elaine Cohen Hubal, PhD US EPA; NCCT
Office of Research and Development
National Center for Computational Toxicology
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Phases of Exposure Ontology Development
Phase II
Phase I
Phase III
Phase IV
Disseminate
exposure
ontology for
public feedback
Initial pilot curation
to identify major
concepts
Model relationships
among data concepts
Full working group
Expand test data set to evaluation of draft
evaluate extensibility of
ontology
conceptual model and cross
-reference existing ontologies
Iterate data model
refinement and curation
Office of Research and Development
National Center for Computational Toxicology
Definitions of Central Concepts
• Exposure Stressor - An agent, stimulus, activity, or event that causes
stress or tension on an organism and interacts with an exposure receptor
during an exposure event.
• Exposure Receptor - An entity (e.g., a human, human population, or a
human organ) that interacts with an exposure stressor during an
exposure event.
• Exposure Event - An interaction between an exposure stressor and an
exposure receptor.
• Exposure Outcome - Entity that results from the interaction between an
exposure receptor and an exposure stressor during an exposure event.
Office of Research and Development
National Center for Computational Toxicology
Mattingly et al, submitted
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Biolog.
Agent
Relational View of Selected ExO Domains
Public
Policy
Chem.
Agent
•Source
•Location
•Process
•Transport Path
Biomech.
Agent
Exposure
Stressor
Individual
Anthrosphere
•Location
•Genetic Background
•Lifestage
•Health Status
•Socioeconomic Status
Office•Occupation
of Research and Development
National Center for Computational Toxicology
Intervention
Biolog.
Response
Phys.
Agent
Psychosoc.
Agent
Human
Pop.
Exposure
Outcome
Exposure
Receptor
Disease
Symptom
Exposure
Event
Molecular
Response
•Location
•Temporal Pattern
•Intensity
•Route
•Assay
•Medium
•Method
•Location
Mattingly et al, submitted
High-level schematic of Exposure Ontology (ExO) integration
within a broader biological context.
Encode
Annotated with
Chemical
(e.g., MeSH)
Is a
Genes
Interacts with
(e.g., CTD)
Exposure Receptor
(e.g., ExO)
Interacts with
(e.g., CTD)
Is a
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Interact via
Occur within
Biological System
(e.g., Functional model of
anatomy)
Assessed by
Via an
Exposure Event
(e.g., ExO,
ExpoCastDB)
Gene
Products
Pathways, Networks
Reactions
(e.g., KEGG, Reactome)
Exposure Stressor
(e.g., ExO)
Interacts with
Biological Process
Molecular Function
Cellular Component
(e.g., Gene Ontology
Results in an
Exposure Outcome
Phenotype
(e.g., OMIM, MeSH)
Mattingly et al, submitted
Next Steps
• Open source approach
• With input from the scientific community, further specify branches
• Leverage existing ontologies (e.g., CHEBI and MeSH for “Chemical
agent” Stressors; DO, OMIM and MeSH for “Disease” Outcomes).
• Cross-referencing will underscore where ExO fits into a broader
knowledge space and where it may add value to existing ontologies.
Office of Research and Development
National Center for Computational Toxicology
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Beyond EPA:
Pilot Curation of Exposure Data into CTD
Chemicals
Exposure
Data (curated
and public
sources)
chemical-gene
interactions
Genes
chemical-disease
relationships
gene-disease
relationships
Diseases
functional annotations
Carolyn Mattingly
Office of Research and Development
National Center for Computational Toxicology
pathway data
24
Acknowledgements
• ExO–
• Carolyn Mattingly,
• Tom McKone,
• Judy Blake,
ExpoCastDB -Richard Judson,
Peter Egeghy,
Sumit Gangwal
• Mike Callahan
Office of Research and Development
National Center for Computational Toxicology
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