Using Machine Intelligence to Perform Predictive Toxicology
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Toxicology is in the midst of transformation, moving towards a data rich quantitative future. As we move towards a Biological Big Data reality, investigators and risk assessors need computational tools that can help them make decisions. In this slide deck I lay out some of my vision for using ontologies to drive predictive toxicology using machine intelligence.
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Using Machine Intelligence to Perform Predictive Toxicology
- 1. 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.
- 2. Innovative solutions for a safer, better worldBUILDING STRONG® Challenge Data-to- Decisions Chasm TOX21/ToxCast/ Toxicogenomics Test Data Modeling AOPs Decisions
- 3. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 4. Innovative solutions for a safer, better worldBUILDING STRONG® AOP Ontology (AOPO)
- 5. Innovative solutions for a safer, better worldBUILDING STRONG® Ontologies Are Models
- 6. Innovative solutions for a safer, better worldBUILDING STRONG® Ontology Organize concepts Relationships Vocabulary
- 7. Innovative solutions for a safer, better worldBUILDING STRONG® Computer + Ontology = Classify
- 8. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 9. Innovative solutions for a safer, better worldBUILDING STRONG® LOGICAL TERMINOLOGY Necessary; Sufficient; Necessary and Sufficient
- 10. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 11. Innovative solutions for a safer, better worldBUILDING STRONG® Bachelor : is an unmarried : Male Bachelor : is a : Human Homo 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
- 12. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 13. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 14. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 15. Innovative solutions for a safer, better worldBUILDING STRONG® Fish Fecundity AOPs AOPwiki AOPXplorer Visualization of AOPs
- 16. Innovative solutions for a safer, better worldBUILDING STRONG® Fish Fecundity AOP network
- 17. Innovative solutions for a safer, better worldBUILDING STRONG® AOPXplorer
- 18. Innovative solutions for a safer, better worldBUILDING STRONG®
- 19. Innovative solutions for a safer, better worldBUILDING STRONG® AOPXplorer Demo
- 20. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 21. Innovative solutions for a safer, better worldBUILDING STRONG® C. Elegans RDX Cuticle Molting
- 22. Innovative solutions for a safer, better worldBUILDING STRONG® 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
- 23. Innovative solutions for a safer, better worldBUILDING STRONG® 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)
- 24. Innovative solutions for a safer, better worldBUILDING STRONG® 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?
- 25. Innovative solutions for a safer, better worldBUILDING STRONG® 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
Editor's Notes
- The challenge is how do we bridge the divide from all of these data sources to risk management decisions? How do we integrate all of these data together to inform decision making?
- Warfighters are exposed to an unknown chemical agent by contact with native building material Biomarkers obtained non-invasively Predict potential hazard outcomes based on biomarkers Predict potential chemicals and chemical countermeasures based on predictions
- Here ya go. Note that I don't have inhibition arrows turned on yet -- so everything is just regular arrows. The fish fecundity AOPN also illustrates a problem going forward that I'm finding A LOT in the wiki that EAGMST needs to address -- different levels of "acceptable" organization when dealing with the same AO. This is even evident in AOPs from the SAME author. I suspect Dan would say this was on purpose to illustrate the problem, but regardless, it's a problem that the AOP Ontology seeks to solve with or without EAGMST assistance :)
- This goes through a demonstration. We perform our analyses in R, and we visualize in Cytoscape. 1) We query the AOPO for the steatosis network. 2) We read in some expression data. 3) We attach the expression data to the steatosis AOPN. 4) We send this to Cytoscape to visualize the overlay.
- The spaghetti plot shows a random assortment of 40 models from the bootstrap metaregression (1,000 models were fit to the bootstrap data). The other plot is the 95% confidence envelope + median – so take all 1,000 models, and calculate the 95% confidence envelope and the median and then plot those values.
- Detoxification via Txn and Gsr occur at low doses, along with p53-mediated cell death and Stat3-mediated inflammation. At low doses, Stat3 is likely able to overcome bbc3-mediated apoptosis; however, it is unclear if that continues at higher doses (in other words, we may see some cell death at higher doses). As the inflammatory process continues and escalates at higher doses, the extracellular matrix will begin to break down, releasing additional inflammatory cell recruiting molecules and chemoattractants. Glycosaminoglycans (GAGs) are also likely to begin entering the cell and are shuttled to the lysosome. These GAGs are toxic if they accumulate in the lysosome, so the cell will begin to increase the amount of Galns protein available to break the GAGs down. Thus, Galns expression is likely a compensatory mechanism to handle the ECM breakdown and inflammatory response.
- The challenge is how do we bridge the divide from all of these data sources to risk management decisions? How do we integrate all of these data together to inform decision making?