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ToxCast™ Data Analysis Summit
Transforming Toxicity Testing From In Vivo to In Vitro:
A Computational Toxicology Challenge
The first ToxCast Data Analysis Summit was held May 14-15, 2009 at the EPA campus in Research Triangle Park, NC. The Data Analysis Summit gathered ToxCast partners to present their findings on the best analysis strategies for finding robust methods to predict whole animal or human chemical toxicity from in vitro data.
The EPA ToxCast™ Program develops novel approaches to predict chemical toxicity using high-throughput and high content in vitro assays. ToxCast™ has produced data on 320 chemicals from over 500 in vitro assays, and about 75 in vivo endpoints, providing a powerful dataset for evaluating analysis approaches.
The Data Summit's agenda, along with downloadable presentations from each session, are provided below. Please note many of the files below are PDF documents.
(All times are Eastern Daylight Savings Time (EDT))
Below are presentations from the conference that can be downloaded. Please note many of the files below are PDF documents.
|Day 1: Thursday, May 14
EPA RTP campus building C auditorium (Room C111)
|8:30am-8:40||Robert Kavlock||Welcoming Remarks and Goals of the Summit (EPA/ORD/NCCT)|
|8:40am-9:00||Jim Jones||Using ToxCast™ for Chemical Screening and Prioritization in the Real World (EPA/OPPTS)|
Introduction to ToxCast- Phase I
|9:00-9:15||David Dix||Overview of the ToxCast™ Research Program: Applications to Predictive Toxicology and Chemical Prioritization (EPA/ORD/NCCT)|
|9:15-9:45||Keith Houck||Characteristics of the ToxCast™ In Vitro Datasets from Biochemical and Cellular Assays (EPA/ORD/NCCT)|
|9:45-10:00||Matt Martin||Characteristics of the ToxRefDB In Vivo Datasets from Chronic, Reproductive and Developmental Assays (EPA/ORD/NCCT)|
|10:00-10:30||BREAK--LIGHT REFRESHMENTS PROVIDED|
Predicting In Vivo Toxicity from ToxCast™ In Vitro Data
|10:30-11:00||Richard Judson||High Throughput Screening of Toxicity Pathways Perturbed by Environmental Chemicals|
|11:00-11:30||Barry Hardy||Initial OpenTox Evaluation of ToxCast Phase 1 Datasets(OpenTox)|
LUNCH--ON YOUR OWN (there is a café onsite)
Mining ToxCast™ Data
|12:30-1:00||Alex Tropsha (U of North Carolina)||Prediction of animal toxicity endpoints of ToxCast Phase I compounds using a combination of chemical and biological in vitro descriptors|
|1:00-1:30||Lyle Burgoon (Michigan State U)||Biomarker Identification using Graph Theoretic and Particle Swarm Optimization-based Support Vector Machine Analysis of the Phase I ToxCast™ Dataset|
|1:30-2:00||William Welsh (U Med Dentistry NJ)||Biological Spectra Analysis of the Toxcast chemicals: Linkng Bioactivity Profiles to Molecular Structure|
|2:00-2:30||Rusty Thomas (Hamner Institutes)||An Integrated In Vitro and Computational Approach to Define the Exposure-Dose-Toxicity Relationships In High-Throughput Screens|
BREAK-- LIGHT REFRESHMENTS PROVIDED
|3:00-3:30||Weida Tong (FDA/NCTR)||Prediction of liver toxicity in the animal study using the mechanistically relevant in vitro screening assay data|
|3:30-4:00||Fred Wright (U North Carolina)||Prediction of in vivo toxicity endpoints from ToxCast Phase I data using a variety of machine learning approaches|
|4:00-4:30||Alison Motsinger (NC State U)||Predictive Modeling of Toxicity Outcomes with Grammatical Evolution Neural Networks|
Open Discussion on ToxCast™ Predictive Modeling, Introduction to Poster Session
Poster Session from submitted abstracts on ToxCast™ Data Analysis
Atrium of Building B
Light refreshments and beverages provided for meeting registrants.
|Day 2: Friday, May 15
EPA RTP campus building C auditorium (Rooms C111)
Mining ToxCast™ Data for Predictive Toxicity Signatures (cont.)
|8:30-8:50||Chihae Young (US FDA/CFSAN)||Navigating through the domains of biology and chemistry|
|8:50-9:10||Christodoulos Floudas (Princeton U)||Predicting in vivo toxicities using optimal methods for re-ordering and machine learning|
|9:10-9:30||Nina Jeliazkova (IdeaConsult Ltd.)||Hierarchical Multi-label Classification of ToxCast Datasets|
|9:30-9:50||Jun Huan (U of Kansas)||Kernel Based Learning and Data Integration for Chemical Toxicity Prediction|
BREAK—LIGHT REFRESHMENTS PROVIDED
|10:10-10:30||Jessie Xia (Nat'l Inst Statistical Sciences)||Hierarchical Predictive Analysis of Chemical Toxicity By Recursive Partitioning Using the Phase-I ToxCast Dataset|
|10:30-10:50||Johann Gasteiger (Molecular Networks GmbH)||Chemical Reactivity in Metabolism Reactions for the Risk Assessment Workflow|
Future Directions for ToxCast™
|10:50-11:10||Matt Martin (EPA/ORD/NCCT)||Biotransformation and ToxCast™|
|11:10-11:30||Holly Mortensen (EPA/ORD/NCCT)||Mapping Human Toxicity and Disease Pathways in ToxCast™|
LUNCH-- ON YOUR OWN (there is a café onsite)
Moving Forward with Research and Applications of ToxCast™
|12:30-1:00||David Reif (EPA/ORD/NCCT)||Summary of Approaches and Predictions|
Issues Raised by Participants: single slides from volunteers
|1:45-2:45||Robert Kavlock (Moderator)||Facilitated Discussion on ToxCast™ Predictions and Applications
|2:45-3:00||Richard Judson (EPA/ORD/NCCT)||Action Items and Closing Remarks|
Thursday, May 14, 2009
|Poster 21— Technologies of Physics of Systems will help to realize ToxCast™ Mission.
Authors: Ageev V., Fomin B., Fomin O., Kachanova T., Shirshov S., Turalchuk K., Kopylev L., and Chen C.
|Poster 22— Quantitative Nuclease Protection Assays (qNPA™) as Windows into Chemical- Induced Adaptive Response in Cultures of Primary Human Hepatocytes (Concentration- & Time-Response).
Authors: Andrew Beam, Daniel Rotroff, Kimberly Freeman, Adam Farmer, David Reif, Keith Houck, Richard Judson, David Dix, Edward L. LeCluyse, and Stephen Ferguson
|Poster 23-- Preliminary Evaluation of ToxCast™ Phase 1: Sketching the Landscape.
Authors: Romualdo Benigni ,Cecilia Bossa, and Alessandro Giuliani
|Poster 24-- Methods and approaches for defining mechanism signatures from human primary cell-based disease models.
Authors: Ellen L. Berg and Jian Yang
|Poster 25-- Exposure reconstruction from biomonitoring data using screening level approaches: application to chemicals in the ToxCast™ database.
Authors: C.J. Brinkerhoff, J.A. Alexander, A.F. Sasso, S.S. Isukapalli, and P.G. Georgopoulos
|Poster 26-- Data needs for progressing from hazard characterization to risk assessment: Analysis focusing on chemicals from the ToxCast™ database.
Authors: C.J. Brinkerhoff, J.A. Alexander, C.T. Yung, S.S. Isukapalli, and P.G. Georgopoulos
|Poster 27-- Exploratory Data Mining and Data Requirements to Support ToxCast™ Goals.
Authors: Dexter Cahoy, Sumeet Dua, and Kenny Crump
|Poster 28--Relating toxicity profiles to molecular structures: A preliminary analysis of the transcription factor assays data from Attagene Inc.
Authors: Dmitriy Chekmarev, Vladyslav Kholodovych, N Ai, Panos Georgopoulos, Weida Tong and William J. Welsh
|Poster 29-- Dimension reduction and functional relevance in ToxCast™ chemical toxicity data.
Authors: Kwangbom Choi, Jennifer Staab, Timothy C. Elston, and Shawn M. Gomez
|Poster 30-- Miniaturized Three-Dimensional Cell Culture and Metabolic Enzyme Arrays for High-Throughput Toxicity Assays.
Authors: Jonathan S. Dordick, Jessica D. Ryan, Moo-Yeal Lee, Prashanth Asuri, Michael G. Hogg, and Douglas S. Clark
|Poster 31-- Application of Structure-Activity Relationship (SAR) and Cluster Analysis to ToxCast™.
Authors: Angelina Duggan and Laura McIntosh, Ph.D.
|Poster 32-- A Collaborative Web-Based Architecture for Sharing ToxCast™ Data.
Authors: Sean Ekins, Ellen L. Berg, Keith A. Houck, Moses M. Hohman, and Barry A. Bunin
|Poster 33-- Exposure Considerations for Comparing Potency (and Efficacy) Responses In Cell-Based Assays vs. Cell-Free Assays.
Authors: Stephen Ferguson and Kimberly Freeman
|Poster 34-- Modeling Rat Liver Toxicity Signature Using Machine Learning Techniques.
Authors: Robert Fraczkiewicz, Marvin Waldman, and Walter S. Woltosz
|Poster 35-- Progress in the development of ebTrack, an open environmental bioinformatics system: design considerations related to the support of consistent multi- attribute ToxCast™ data analyses.
Authors: P.G. Georgopoulos, M. Chen, S.S. Isukapalli, W. Welsh, and W. Tong
|Poster 36-- In vitro screening for chemical toxicity in a genetically-diverse human model system.
Authors: Shannon Hatcher, Oksana Kosyk, Pamela Ross, Fred Wright, John Schwartz, David Dix, and Ivan Rusyn
|Poster 37-- High-throughput screening of chemicals for estrogenic and androgenic activity using mass spectrometry.
Authors: Michael J. Hemmer, Kimberly A. Salinas, and Peggy S. Harris
|Poster 38-- Exploring cellular systems biology endpoints in the context of the ToxCast™ Phase I data set.
Authors: Paul Hodor, Nick Radio, Lawrence Vernetti, Ken Giuliano, Kate Johnston, and Albert Gough.
|Poster 39-- Assessment of Environmental and Health Impacts in Munitions Research, Development, Testing and Evaluation: A Phased Approach.
Authors: Mark S. Johnson, William S. Eck, Cheng J. Cao, Lawrence Williams, and Valerie H. Adams
|Poster 40-- Biological Profiling of Endocrine Related Effects of Chemicals in ToxCast™.
Authors: RJ Kavlock, D Dix, K Houck, R Judson, D Reif, T Knudsen and M Martin
|Poster 41-- Evaluation of High-throughput Genotoxicity Screen Assays Used in Profiling the 320 US EPA ToxCast™ Chemicals.
Authors: Andrew W. Knight, Louise Birrell and Richard M. Walmsley
|Poster 42-- Docking ToxCast™ Compounds to Discover New PXR Agonists.
Authors: Sandhya Kortagere, Matthew D. Krasowski, Erica Reschly, and Sean Ekins
|Poster 43-- Consideration of "Dose" in Evaluation of ToxCast™ Data: Use of biomonitoring and pharmacokinetic data.
Authors: Lesa L. Aylward and LLP Sean M. Hays
| Poster 44--An automated physiological screen for endocrine disrupting chemicals.
Authors: G. Lemkine, D. Du Pasquier, and A. Sebillot, and B. A. Demeneix
|Poster 45-- Development of in vitro toxicogenetic models for hepatotoxicity.
Authors: Stephanie Martinez, Blair Bradford, Todd Stewart, Valerie Soldatow, Kirsten Amaral, Stephen Ferguson, Chris Black, Edward LeCluyse and Ivan Rusyn
|Poster 46--Initial OpenTox Evaluation of ToxCast Phase 1 Datasets.
Authors: Barry Hardya, Christoph Helmab, Nina Jeliazkovac, Romualdo Benignid, Stefan Kramere, Haralambous Sarimveisg, David Gallagherh, Vladimir Poroikovi, and Sylvia Escherk
|Poster 47--Using the ToxMinerTM Database for Identifying Disease-Gene Associations in the ToxCast™ Dataset.
Authors: Holly Mortensen, David Dix, Keith Houck, Robert Kavlock, and Richard Judson
|Poster 48-- Building a Database of Developmental Neurotoxicants: Evidence from Human and Animal Studies.
Authors: W. Mundy, S. Padilla,T. Shafer, M. Gilbert, J. Breier, J. Cowden, K. Crofton, D. Herr, K. Jensen, K. Raffaele, N. Radio, and K. Schumacher
|Poster 49-- Using Leadscope to Analyze the ToxCast™ Data.
Authors: Glenn Myatt, David Bower, Kevin Cross, and Pat Quigley
|Poster 50-- Pathway Modeling of in utero DBP Exposure in Rat Testes.
Authors: M. Ovacik , M.G. Ierapetritou, P. Georgopoulos, W. Welsh, S. Euling, B. Sen, K. Gaido, and I.P. Androulakis Corresponding Author: Ioannis (Yannis) P. Androulakis
|Poster 51--Toxicity Screening of the ToxCast™ Chemical Library Using a Zebrafish Developmental Assay.
Authors: S. Padilla, D. L. Hunter, B. Padnos, D. Corum, D. J. Dix, K. A. Houck, T. B. Knudsen and M. T. Martin
|Poster 52-- EU project CADASTER: Case studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment.
Authors: Willie J.G.M. Peijnenburg, Mojca Durjava, Paola Gramatica, Andreas Woldegiorgis, Tomas Oberg, Nina Jeliazkova, Mark A.J. Huijbregts, Mike Comber,and Igor V. Tetko
|Poster 53--(Q)SAR and (Q)AAR analysis of ToxCast Dataset Using PASS and GUSAR approaches.
Authors: Vladimir Poroikov, Dmitry Filimonov, Alexey Zakharov, Alexey Lagunin, and Sergey Novikov
|Poster 54-- Use of Cellular Systems Biology to Evaluate the ToxCast™ Phase I Data in Two Liver Cell Models.
Authors: Nick Radio, Lawrence Vernetti, Ken Giuliano, Paul Hodor, Albert Gough, and Kate Johnston
|Poster 55--Analysis of Quantitative High Throughput Screening Data for Applications in Toxicology
Authors: K. R. Shockley, G. E. Kissling, M. Xia, R. Huang, C.P. Austin, and R.R. Tice
|Poster 56--Establishing a biological context for ToxCast™ chemical toxicity data.
Authors: Jennifer Staab, Kwangbom Choi, Timothy C. Elston, and Shawn M. Gomez
|Poster 57--Developing a Software Interface for Visualizing and Mining the ToxCast™ Data.
Authors: Ling-Chieh Tsai, David J. Dix, Richard S. Judson, and Russell S. Thomas
|Poster 58--Chemical Selection via in vitro-in vivo Correlation of ToxCast™ and ToxRefDB Data for the Virtual Liver Project.
Authors: J.F. Wambaugh, K. Houck, R.S. Judson, M.T. Martin, D.J. Dix, and I. Shah
*publication but does not necessarily reflect official Agency policy.
|Poster 59--In Vitro Profiling of EPA's Initial ToxCast™ Chemical by Novascreen (Caliper Life Sciences).
Authors: Arthur D. Weissman Ph.D. and Ming Lui, Ph.D.
|Poster 60--Cheminformatics Analysis of EPA ToxCast™ Chemical Libraries to Develop Predictive Toxicity Models and Prioritize Compounds for in vivo Toxicity Testing.
Authors: Liying Zhang, Hao Zhu, Ivan Rusyn, Richard Judson, David Dix, Keith Houck, Matthew Martin, Ann Richard, Robert Kavlock, and Alexander Tropsha
|Poster 61--Using ToxCast™ cell-viability and gene-expression assays as biological descriptors in QSAR modeling of animal toxicity endpoints.
Authors: Hao Zhu, Alexander Sedykh, Liying Zhang, Ivan Rusyn, and Alexander Tropsha
Overview: The U.S. EPA ToxCast™ Program is developing approaches to predict chemical toxicity using data from high-throughput and high content in vitro assays. Phase I of ToxCast has produced data from 320 chemicals, ~500 in vitro assays and ~100 in vivo endpoints, providing a powerful dataset for evaluating the applicability of various analytic approaches for predicting the potential for an adverse response.
The goal of ToxCast is to develop and verify "toxicity signatures," which are algorithms using in vitro and in silico data to predict in vivo toxicities. These signatures will be used to screen and prioritize chemicals for targeted toxicity testing, and over the next several years EPA would like to screen thousands of compounds. However, successful predictive models will depend on robust and reliable methods that EPA can rely on for making decisions about further testing of environmental chemicals.
This first ToxCast Data Analysis Summit is designed to bring together experts in machine learning, computational chemistry, statistics, high-throughput screening and computational toxicology, with toxicologists and regulatory staff. Plenary talks will describe the ToxCast Program and a series of issues related to toxicity prediction, both from a scientific and regulatory standpoint. Speakers will be selected from abstract submitters to describe algorithmic, computational or systems biology approaches to solving these issues.To further this aim, we invite interested researchers to submit abstracts and present their analyses, using the ToxCast Phase I dataset, at the First ToxCast Data Analysis Summit
Data Overview: : The collection of ToxCast Phase I chemicals were chosen because high quality, guideline-based animal toxicity data were available. These chemicals are mostly pesticide active compounds for which we have rat and mouse 2-year chronic/cancer, 2-generation reproductive, and developmental toxicity data. For analysis, we will provide ~100 toxicity endpoints from these study types whose value is a "LEL" or lowest effective level at which the endpoint was observed- these are the values to predict. In addition, we will provide other aggregated endpoints derived from clustering analyses. Analysis groups (or analysis partners) are also free to develop other endpoints to predict from the data that we will provide.
A total of 9 in vitro datasets have been produced, reviewed and cleared for research use. These include biochemical receptor and enzyme assays; and cell-based assays measuring RNA and protein, cytotoxicity, cell growth and morphology changes in a variety of human and rodent cell types. For each assay, we will provide chemical concentration values at which the assay becomes active, e.g. IC50 or LEC (lowest effective concentration). Additionally, we will provide molecular descriptors and physicochemical properties calculated from chemical structure.
|Name||Description||Number of Assays|
|ACEA||Real-time Cell Electronic Sensing||7|
|Attagene||Transcription factor assays||80|
|BioSeek||Cell-based protein level assays||87|
|Cellumen||Cell imaging assays||10|
|Gentronix||Genetic toxicity assay||1|
|NCGC||Nuclear receptor assays||21|
|Novascreen||Receptor binding and enzyme inhibition assays||239|
|Solidus||Phase I /II metabolic enzyme vs. cytotoxicity assays||4|
|ChemClass||Chemical Class Information||107|
|PhysChem||Calculated physico-chemical properties||3|
|ToxRefDB Endpoints||Animal study data from chronic / cancer, multigenerational and developmental toxicity guideline studies||100|
- Meeting organizers contact information:
|David Dix||(co-chair, EPA/ORD/NCCT)|
|Richard Judson||(co-chair, EPA/ORD/NCCT)|
|Lyle Burgoon||(Michigan State University)|
|Aldert Piersma||(RIVM, The Netherlands)|
|Ivan Rusyn||(University of North Carolina at Chapel Hill)|
|Rusty Thomas||(The Hamner Institutes for Life Sciences)|
|Alex Tropsha||(University of North Carolina at Chapel Hill)|
|William Welsh||(University of Medicine and Dentistry of New Jersey)|
|Fred Wright||(University of North Carolina at Chapel Hill)|