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Society For Risk Analysis Annual Meeting 2008

Risk Analysis: the Science and the Art

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

M4-C
ToxCast: Steps Towards the 'New Technology'

Room: Grand Ballroom E   4:00-5:30 PM

Chair(s): Richard Judson, Harvey Clewell



M4-C.1  16:00  An Introduction to ToxCast(tm). Setzer RW*; US Environmental Protection Agency   setzer.woodrow@epa.gov

Abstract: ToxCast™ is a chemical prioritization research program to develop the ability to forecast toxicity using bioactivity profiling. The point is to use results in a variety of in vitro and rapid non-mammalian in vivo assays to explore effects at different toxicity targets. The design of the study (from receptor-specific assays through cellular assays to evaluations of toxicity in zebrafish) allows data analysis to proceed in both unsupervised and supervised modes. In unsupervised modes, assay results are used to explore and define common toxicity pathways. Supervised analyses are used to develop predictors of animal toxicity as determined from a database of the results of toxicity testing using guideline registration studies. In Phase I of ToxCast™, just over 300 chemicals, largely registered pesticide active ingredients, have been tested in over 400 assays. Phase II will evaluate predictors derived in Phase I by testing toxicity predictions in an additional set of chemicals. While the initial phases of ToxCast™ have focused on hazard identification and prioritization, many assays have been run at multiple concentration levels, so concentration-response analysis is feasible. Combined with work developing PBPK models described in Dr. Clewell’s talk in this session, such analysis could foster the development of purely in vitro-based dose-response analysis, as envisioned in the National Academy’s recent document “Toxicity Testing in the 21st Century”. All data collected in Phase I are being made available to the public, and investigators are encouraged to explore this rich dataset with their own methodologies. This work was reviewed by EPA and approved for publication but does not necessarily reflect official Agency policy.

M4-C.2  16:20  Classification and Dose-Response Characterization of Environmental Chemicals based on Structured Toxicity Information from ToxRefDB. Martin MT*; U.S. Environmental Protection Agency   martin.matt@epa.gov

Abstract: Thirty years and over a billion of today’s dollars worth of pesticide registration toxicity studies, historically stored as hardcopy and scanned documents, have been digitized into highly standardized and structured toxicity data, within the U.S. Environmental Protection Agency’s (EPA) Toxicity Reference Database (ToxRefDB). The source toxicity data in ToxRefDB covers multiple study types, including subchronic, developmental, reproductive, chronic, and cancer studies, resulting in a diverse set of endpoints and toxicities. Novel approaches to chemical classification are performed as a model application of ToxRefDB and as an essential need for highly detailed chemical classifications within the EPA’s ToxCast™ research program. In order to develop predictive models and biological signatures utilizing high-throughput screening (HTS) and in vitro genomic data, endpoints and toxicities must first be identified and globally characterized for ToxCast Phase I chemicals. Secondarily, dose-response characterization within and across toxicity endpoints provide insight into key precursor toxicity events and overall endpoint relevance. Toxicity-based chemical classification and dose-response characterization utilizing ToxRefDB prioritized toxicity endpoints and differentiated toxicity outcomes across a large chemical set. This work was reviewed by EPA and approved for publication but does not necessarily reflect official Agency policy.

M4-C.3  16:40  Predictive Modeling of Apical Toxicity Endpoints using Data from the EPA ToxCast Program. Judson R*, Dix D, Houck K, Martin M, Kavlock R, Shah I, Knudsen T; USE EPA / ORD / NCCT   judson.richard@epa.gov

Abstract: The US EPA and other regulatory agencies face a daunting challenge of evaluating potential toxicity for tens of thousands of environmental chemicals about which little is currently known. The EPA’s ToxCast™ program is testing a novel approach to this problem by screening compounds using a variety of in vitro assays and using the results to prioritize chemicals for further, more detailed testing. Phase I of ToxCast is testing 320 chemicals (mainly pesticide active ingredients) against ~400 cell-based and biochemical assays. In order to anchor these studies, we are using in vivo guideline study data for subchronic, chronic, cancer, reproductive and developmental endpoints. This data is compiled in the EPA toxicity reference database, ToxRefDB. The main goal of ToxCast is the discovery and validation of “signatures” linking in vitro assay data to in vivo toxicity endpoints. These signatures will be collections of assays that are correlated with particular endpoints. These assay collections should also help define molecular-and cellular-level mechanisms of toxicity. This talk will discuss our strategy to use a combination of statistical and machine learning methods, coupled with biochemical network or systems biology approaches. Our initial examples will focus signatures for endpoints from 2 year rodent cancer bioassays. Most of the data we have analyzed is in dose or concentration response series, so to effectively use this data we have developed novel approaches to combine many kinds of dose-response data together with standard machine learning methods. A key issue to be discussed is the validation of ToxCast predictive signatures, an issue involving statistics, as well as data coverage in both biological and chemical space. This work has been reviewed by EPA and approved for presentation but does not necessarily reflect official Agency policy

M4-C.4  17:00  Assessing the Exposure-Dose-Toxicity Relationship Within the EPA’s ToxCast Program. Clewell HJ*, Tsai LC, Dix DJ, Tan YM, Andersen ME, Thomas RS; The Hamner Institutes for Health Sciences, 6 Davis Drive, RTP, NC 27709   hclewell@thehamner.org

Abstract: The EPA’s National Center for Computational Toxicology has initiated a research program called ToxCast with the intent of improving EPA’s chemical toxicity evaluations by developing methods to evaluate a large number of chemicals for potential toxicity and using the information to help prioritize testing of those chemicals that pose the greatest risk. As an adjunct to the ToxCast program, a project was initiated to provide refined exposure-dose-toxicity evaluations that will aid in interpretation of the high-throughput in vitro testing results. Such context will be essential for identifying appropriate priorities for follow-up testing and risk evaluation exercises. In the first part of the project, organ slice cultures have been established for rat liver, lung, and kidney. The cultures were exposed to a subset of the ToxCast Phase I chemicals in a 5-point dose response, with cytotoxicity measured as the endpoint. The data from these studies were used to calculate EC50 values and provide an estimate of organ-specific toxicity in the rat. The second part of the project used computational and in vitro methods to predict the pharmacokinetic properties of the ToxCast chemicals. The pharmacokinetic properties were used to predict what exposure conditions (i.e., route and dose) would be needed to produce target tissue doses equivalent to the EC50 values measured in the cytotoxicity experiments.



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