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Using Machine Intelligence To Perform
Predictive Toxicology
Lyle D. Burgoon, Ph.D.
Team Leader: Bioinformatics and Computational
Toxicology (Data Ninja Corps)
Environmental Laboratory
The views and opinions expressed are
those of the author and not those of the
US Army or any other federal agency.
Innovative solutions for a safer, better worldBUILDING STRONG®
Challenge
Data-to-
Decisions Chasm
TOX21/ToxCast/
Toxicogenomics
Test Data
Modeling
AOPs
Decisions
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Multi-Year Army Investment In
Engineering Predictive Toxicology
2012-2016: Rapid Hazard Assessment Focus Area► AOP Ontology: ontology to predict AOP outcomes using assay data
► AOPXplorer: R and Cytoscape software to facilitate data visualization within the AOP context
2017-2021: Next Generation Risk Assessment Focus Area► Development of High Throughput Zebrafish Embryo Toxicity Assays
► Predicting Molecular Initiating Events through Deep Learning of Molecular Interactions
► Predicting Assay Responses through Deep Learning
► Toolkit that integrates predictive toxicology tools
► Further development of content for AOPO and AOPXplorer
Innovative solutions for a safer, better worldBUILDING STRONG®
AOP Ontology (AOPO)
Innovative solutions for a safer, better worldBUILDING STRONG®
Ontologies Are Models
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Ontology
Organize concepts
Relationships
Vocabulary
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Computer + Ontology = Classify
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Bachelor : is an unmarried : Male
Bachelor : is a : Human
Homo sapiens : is a : mammal
Human : owl:sameAs : Homo sapiens
Is a
bachelor a
mammal?
Computer + Ontology = Classify via Deduction
Subject : Object : Predicate
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LOGICAL TERMINOLOGY
Necessary; Sufficient; Necessary and Sufficient
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Sufficient: To get an A in this course it is sufficient to get an A on all
work turned in
Necessary: To get an A in this course, you must turn in a report
Necessary: states the criteria required to achieve
something
Sufficient: if you meet these criteria you are guaranteed
to achieve something
Necessary and Sufficient: to be guaranteed to achieve
something, you must meet these criteria
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Bachelor : is an unmarried : Male
Bachelor : is a : HumanHomo sapiens : is a : mammal
Human : owl:sameAs : Homo sapiens
Given:
Bob : is a : Bachelor
Sufficient: Bob must be a human, unmarried male
Necessary: To be a bachelor, one must be an unmarried male
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AOP Ontology
Like modern software, it’s a constantly evolving work in progress
Model► Assay classes
► Assay result classes
► Biological pathway classes
► AOP classes
Predict toxicity
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DHB4 HTS Data: B[k]F inhibits activity (Red)
Predict: Steatosis (blue)
Predict: ALT and AST levels increased (green)
ALT
AST
Benzo[k]Fluoranthene effects on Steatosis AOP network
Burgoon, et al (2016). Risk Analysis. doi/10.1111/risa.12613
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Application to Developing Screening Level Risk Assessments
► Identify all available data for a chemical or mixture
► Use AOPs to identify potential adverse outcomes (hazard ID)
► Use concentration-response or dose-response data to calculate a POD for an AOP
• Use sufficient key event – key event sufficient to infer adversity based on network theory
► Reverse dosimetry on POD (if in vitro data) to estimate adult POD
► Determine a safe margin from the POD (divide by 100 if a 100x safe margin is desired)
Burgoon, et al (2016). Risk Analysis. doi/10.1111/risa.12613
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Fish Fecundity AOPs
AOPwiki
AOPXplorer
Visualization of AOPs
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Fish Fecundity AOP network
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AOPXplorer
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AOPXplorer Demo
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Tutorials and Help Documents
A series of examples are included with AOPXplorer
A vignette is being developed to provide written documentation of how to use the AOPXplorer
► Will include omics examples, HTS, etc…
► For instance, examples will allow users to go from raw microarray data, analyse and find genes in the AOPN, add that data to the AOPN graph object, and send it to Cytoscape
Video tutorials► To walk users through step-by-step how to analyze and visualize
their data
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C. Elegans RDX
Cuticle Molting
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AOPXplorer Changed Our TxGen Workflow
Before:► Fishing expedition for what changed
► Non-model org gene annotation is poor :(
► Massive penalties for multiple testing for probes with little annotation
Today► Hypothesis-based analysis focused on AOPs
► We use a fully Bayesian analysis approach (takes longer, but better)
• Focused on probes connected to AOPs
► Data visualization is an intimate part of our analysis workflow
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AOPXplorer + AOPO
What We Can Do Now:► AOPO as an artificial intelligence engine
• Ask: Given the data, is there sufficient evidence to predict that Chemical X causes this AO?
• Ask: Given this AO, what is the minimum set of KEs that need to be measured to make a prediction?
Assay battery design
• Ask: What is the likelihood, given the data, that chemical X causes this AO? How would additional data change this likelihood?
► Near Future:• Exploit these capabilities within the AOPXplorer itself (and thus, within
R)
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Long-Term Strategy
Open Source Toxicological Data Store
Susceptible populations data and AOPs
Data Integration Using AOPO
Allow our benefactors to ask questions in plain English (or near-plain English)
► What are the hazards posed by Chemical X?
► At what external doses will Chemical X cause cancer of any type?
► Is an exposure of X mg/kg-day okay for this population?
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Acknowledgements
US Army ERDC Bioinformatics and Computational Toxicology Group► Gabriel Weinreb (Bennett Aerospace)
► Larry Wu (Bennett Aerospace)
US Army ERDC► Ed Perkins
► Natalia Vinas
Integrated Laboratory Systems, Inc (supporting NIEHS/NICEATM)► Shannon Bell
Oak Ridge Institute for Science and Education► Ingrid Druwe
► Kyle Painter
► Erin Yost
US Environmental Protection Agency► Steve Edwards
► Ila Cote