Providing the Missing Link The Exposure Ontology Ex

Providing the Missing Link: The Exposure Ontology Ex. O 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 1

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 2 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 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 6

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 Center for and food Toxicology diet and health. agriculture Computational towards J Sci Food and Ag 87(8)1427 -34. 7

Exposure Data Sources Peter Egeghy, NERL Office of Research and Development National Center for Computational Toxicology http: //actor. epa. gov 8

Exposure Data Landscape Office of Research and Development National Center for Computational Toxicology 9 Network of exposure taxonomy used in ACTo. R; Egeghy et al, 2011

Exposure Data Landscape Office of Research and Development National Center for Computational Toxicology 10 Number of unique chemicals by data type in ACTo. R; Egeghy et al, 2011

Exposure Data Collection and Access: ACTo. R, Aggregated Computational Toxicology Resource http: //actor. epa. gov/ ACTo. R API Chemical ID, Structure Chemical ACTo. R Core Tox. Ref. DB Tabular Data, Links to Web Resources In Vivo Study Data - OPP Internet Office of. Searches Development Research and DSSTox Tox. Cast. DB Tox. Cast Data – NCCT, ORD, Collaborators Chemical ID & Structure QC, Inventory Tracking Expo. Cast. DB Exposure Data – NERL, NCCT National Center for Computational Toxicology 11

Exposure Data Collection and Access : Expo. Cast. DB 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 ACTo. R • 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 ACTo. R Exposure Data Collection and Access: Expo. Cast. DB 1 1 N Laboratory method N Measure (Chemical) N Technique / sampling method N Location_CV (cont vocab. ) N 1 N 1 N Sample N • Descriptive statistics capabilities N N Location N N N • Full raw data sets available for download • Browse data capability Medium (serum, air, soil, etc. ) N • Four initial studies from National Exposure Research Laboratory N N 1 Subject N N N N Sources Study N Exposure Taxonomy (Assay_ category_ CV table in ACTo. R) N N Study_CV cont. vocab 1 Office of Research and Development National Center for Computational Toxicology N N 13

Ex. O: An Ontology for Exposure Science Office of Research and Development National Center for Computational Toxicology 14

Exposure Data Collection and Access: Design and Evaluation of the Exposure Ontology, Ex. O 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 RO 1 Office of Research and Development National Center for Computational Toxicology 15

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: Ex. O • 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 17

Exposure Ontology Working Group Member Institution Role/expertise Carolyn Mattingly, Ph. D Judith Blake, Ph. D. Mount Desert Island Biological Laboratory The Jackson Laboratory Michael Callahan, Ph. M. MDB, Inc. Robin Dodson, Sc. D. Silent Spring Institute (SSI) Facilitator/curated database development Facilitator/ontology development Core Member/ exposure assessment Core Member/ exposure research Member/exposure research Peter Egeghy, Ph. D. US EPA, NERL Member/exposure research Jane Hoppin, Sc. D. NIEHS Member/epidemiology Thomas Mc. Kone, Ph. D. Lawrence Berkeley National Laboratory (LBNL) Core Member/ exposure research Ruthann Rudel, MS. Silent Spring Institute (SSI) Member/exposure research Elaine Cohen Hubal, Ph. D US EPA; NCCT Office of Research and Development National Center for Computational Toxicology 18

Phases of Exposure Ontology Development 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 20

Biolog. Agent Relational View of Selected Ex. O 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 of Research and Development • Occupation 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 (Ex. O) integration within a broader biological context. Encode Annotated with Chemical (e. g. , Me. SH) Is a Genes Interacts with (e. g. , CTD) Exposure Receptor (e. g. , Ex. O) Interacts with (e. g. , CTD) Is a Office of Research and Development National Center for Computational Toxicology Interact via Occur within Biological System (e. g. , Functional model of anatomy) Assessed by Via an Exposure Event (e. g. , Ex. O, Expo. Cast. DB) Gene Products Pathways, Networks Reactions (e. g. , KEGG, Reactome) Exposure Stressor (e. g. , Ex. O) Interacts with Biological Process Molecular Function Cellular Component (e. g. , Gene Ontology Results in an Exposure Outcome Phenotype (e. g. , OMIM, Me. SH) 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 Me. SH for “Chemical agent” Stressors; DO, OMIM and Me. SH for “Disease” Outcomes). • Cross-referencing will underscore where Ex. O 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 23

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 • Ex. O– • Carolyn Mattingly, • Tom Mc. Kone, • Judy Blake, Expo. Cast. DB -Richard Judson, Peter Egeghy, Sumit Gangwal • Mike Callahan Office of Research and Development National Center for Computational Toxicology 25