Using Machine Intelligence to Perform Predictive Toxicology

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


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


AOP Ontology (AOPO)


Ontologies Are Models


Ontology

Organize concepts

Relationships

Vocabulary


Computer + Ontology = Classify


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


LOGICAL TERMINOLOGY

Necessary; Sufficient; Necessary and Sufficient


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


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


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


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


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


Fish Fecundity AOPs

AOPwiki

AOPXplorer

Visualization of AOPs


Fish Fecundity AOP network


AOPXplorer


AOPXplorer Demo


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


C. Elegans RDX

Cuticle Molting


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


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)


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?


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

Using Machine Intelligence to Perform Predictive Toxicology

Science

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