. 

ORD’s 

Computational Toxicology 

Research Program 

Implementation Plan 

(FY 2006 – 2008) 

April 2006 
DISCLAIMER 

 This document has been subjected to internal and external review for clearance. 

The Implementation Plan was developed in response to a recommendation of the first 

review of the National Center for Computational Toxicology, by the Computational 

Toxicology Subcommittee of the Office of Research and Development’s Board of 

Scientific Counselors (BOSC), conducted during a two-day meeting on April 25-26, 

2005. In addition to the BOSC Subcommittee this Plan was reviewed by several ORD 

staff, including members of the ORD Science Council. A second BOSC review was held 

on June 19-20, 2006 and comments were documented in a letter report to Dr. George 

Gray, Assistant Administrator for the Office of Research and Development, dated 

December 12, 2006. Information on the BOSC can be found at 

http://www.epa.gov/comptox/bosc_review/2006/index.html. This Plan does not constitute 

an Agency position or policy concerning computational toxicology. Any mention of trade 

names does not constitute Agency endorsement. 
iii

Table of Contents 

Table of Contents............................................................................................................................ ii 

List of Figures................................................................................................................................ iv 

Acronyms........................................................................................................................................ v 

Executive Summary....................................................................................................................... vi 

I. Introduction and Background ...................................................................................................... 1 

II. Office of Research and Development Computational Toxicology Program ............................. 3 

National Center for Computational Toxicology’s (NCCT) Mission ...................... 4 

Science to Achieve Results Centers........................................................................ 5 

Board of Scientific Counselors ............................................................................... 6 

III. Research Projects...................................................................................................................... 6 

IV. Summary of Outcomes from Research..................................................................................... 9 

V. Partnerships................................................................................................................................ 9 

VI. Conclusion .............................................................................................................................. 11 

Table 1 -Identification of Computaional Toxicology Research Program (CTRP) Projects ......... 13 

Table 2 -CTRP Output/Input Table .............................................................................................. 14 

Table 3 –FTE’s from ORD's Supporting Multi Year Plans.......................................................... 22 

Appendix A................................................................................................................................... 23 

Appendix B................................................................................................................................... 25 
. 

List of Figures 

Figure 1 – Component of the Computational Toxicology Research Program within ORD........... 4 

Figure 2 - NCCT Staffing Profile ................................................................................................... 5 

Figure 3 -Alignment of the Five Research Track ........................................................................... 7 

Figure 4 -Interactions between Long Term Goals .......................................................................... 8 

Figure 5 -Computational Toxicology Research Program Design................................................. 12 
. 

Acronyms 

BOSC Board of Scientific Counselors 

CPCP Categorization and Prioritization Community of Practice 

CoP Communities of Practice 

CompTox Computational Toxicology 

CTISC Computational Toxicology Implementation and Steering Committee 

CTRP Computational Toxicology Research Program 

DNA Deoxyribonucleic acid 

DSSTox Distributed Structure-Searchable Toxicity 

EBRC Environmental Bioinformatics Research Center 

EPA Environmental Protection Agency 

FTE Full Time Equivalents 

FY Fiscal Year 

LTG Long Term Goal 

MOA Memorandum of Understanding 

MYP Multi Year Plan 

NCCT National Center for Computational Toxicology 

NCEA National Center for Environmental Assessment 

NCER National Center for Environmental Research 

NERL National Exposure Research Laboratory 

NHEERL National Health and Environmental Effects Research Laboratory 

NIH National Institutes of Health 

NIEHS National Institute of Environmental Health Sciences 

NRMRL National Risk Management Research Laboratory 

NICEATM National Toxicology Program Interagency Center for Evaluation of 

 Alternative Toxicological Methods 

OPPTS Office of Pesticides, Prevention, and Toxic Substances 

ORD Office of Research and Development 

QSAR Quantitative Structure Activity Relationship 

RFA Request for Application 

RNA Ribonucleic acid 

SAB Science Advisory Board 

STAR Science to Achieve Results 

ToxCast Chemical Prioritization Research Program 
Intentionally Left Blank 
vi 

Executive Summary 

 This document presents the implementation plan for the Framework for the 

Computational Toxicology Research Program (CTRP), a strategy document developed by the 

Office of Research and Development (ORD) in 2003. Computational toxicology is defined as the 

integration of modern computing and information technology, with molecular biology and 

chemistry to improve risk assessment and prioritization of data requirements of chemicals by the 

Agency. The program is intended to provide innovative solutions to a number of persistent and 

pervasive issues facing The Environmental Protection Agency (EPA) regulatory programs. The 

three objectives of the Framework have been translated into long term goals (LTGs) for the 

CTRP and the subsequent research has been aligned into five supporting tracks. The LTGs for 

the program are: (I) EPA risk assessors use improved methods and tools to better understand and 

describe linkages across the source to outcome paradigm; (II) EPA Program Offices use 

advanced hazard characterization tools to prioritize and screen chemicals for toxicological 

evaluation; and (III) EPA risk assessors and regulators use new models based on the latest 

science to reduce uncertainties in dose-response assessment, cross-species extrapolation, and 

quantitative risk assessment. The supporting research tracks are: (A) Development of Data for 

Advanced Biological Models; (B) Information Technologies Development and Application; (C) 

Prioritization Method Development and Application; (D) Providing Tools and System Models 

for Extrapolation across Dose, Life Stage, and Species; and (E) Advanced Computational 

Toxicology Approaches to Improve Cumulative Risk Predictions. A standing subcommittee of 

ORD’s Board of Scientific Counselors has been established to provide guidance to the CTRP as 

it develops its research agenda. 

 The research supporting the CTRP flows from three areas. The first component is 

composed of the efforts of the National Center for Computational Toxicology (NCCT), an 

organizational entity created within ORD in 2005 to accelerate the development and use of 

computational tools in the EPA’s regulatory operations. The staffing plan for the NCCT includes 

computational chemists, computational biologists, and bioinformaticians who will work both on 

internal projects and through partnerships with scientists in other ORD Labs and Centers as well 

as, external scientists to provide scientific expertise and leadership related to the application of 

mathematical and computational tools and models to high priority Agency needs. The projects 

located within the NCCT include those developing information databases for chemical toxicity 

(DSSTox), building toolboxes (ToxCast) for prioritizing chemicals for toxicology evaluation, 

providing computation models that extrapolate exposure and effects across life stages, 

developing multi-scale computational models of organ systems that will help provide 

understanding of how mechanisms of toxicity determine dose and time-responses, evaluating 

issues related to estimation of model parameters in computational models, and integrating 

diverse types of information needed to understand the impact of the environment in a broad 

sense on the health of individuals. 

 The second component is a suite of seven projects distributed across ORD that were 

funded in FY05 by a competitive process overseen by the Computational Toxicology 

Implementation and Steering Committee (CTISC), a cross EPA group formed to initiate the 

ORD program. These projects include efforts that bring systems level approaches to the study of 

chemical toxicity in small fish models and in amphibian metamorphosis, to the effects of diesel 
vii 

particles on lung cells, and to factors associated with development of asthma in children. Other 

projects are designed to predict metabolites of chemicals, use metagenomic approaches to assess 

microbial pollution sources, and evaluate the use of toxicogenomic data in risk assessment of 

reproductive toxicants. 

 The final component of the CTRP is derived from the Science to Achieve Results 

(STAR) program of ORD’s National Center for Environmental Research (NCER). The STAR 

program supported a grant solicitation for investigator directed research in this area of systems 

level understanding of the hypothalamic-pituitary-gonadal axis in FY04 that funded two projects 

involving small fish models and one on the female rat. In FY05, the STAR program supported 

the establishment of two Centers for Environmental Bioinformatics, one at the University of 

North Carolina in Chapel Hill and the other at the University of Medicine and Dentistry of New 

Jersey. These Centers were funded as cooperative agreements, which allows for close 

interactions between scientists within the University-based Centers and EPA scientists. With 

project periods of five years, they are intended to provide considerable momentum in the 

development and application of bioinformatics tools in the protection of human health and the 

environment. 

 Within this plan, the research issue and relevance, experimental approach, progress to 

date and milestones over the next three years are articulated for each of the individual projects. 

Collectively the elements of the plan have been developed to provide a sequential series of short 

to medium term projects that will advance the utilization of computational tools in hazard and 

risk assessment. Particular progress is expected over the next few years in the area of 

prioritization tools, as the ToxCast program begins developing fingerprints of chemical activity 

based on the collection of broad spectrum, high throughput data for a large number of well 

characterized chemicals. Projects developing systems level understanding of biological functions 

will require longer investments, but the results in terms of improving the linkages in the sourceto-outcome paradigm and subsequently in the extrapolation of information across dose, time, 

chemical and species will reduce some of the inherent uncertainties present in risk assessment as 

currently practiced.
1 

I. Introduction and Background 

This document lays out the rationale and short to medium term objectives (up to 3 years) 

of the ORD new research program in computational toxicology. The emerging field of 

computational toxicology applies mathematical and computer models and molecular biological 

and chemical approaches to explore both qualitative and quantitative relationships between 

sources of environmental pollutant exposure and adverse health outcomes. Recent technological 

advances make it possible to develop molecular profiles using 

genomic, proteomic, and metabolomic (the “omics”) methods 

to identify the impacts chemicals may have on living 

organisms or the environment. With these tools, scientists can 

produce a more-detailed understanding of the hazards and 

risks of a much larger number of chemicals. The integration 

of modern computing with molecular biology and chemistry 

will allow scientists to better prioritize data, inform decision makers on chemical risk 

assessments, and understand a chemical’s progression from the environment to the target tissue 

within an organism and ultimately to the key steps that trigger an adverse health effect. 

 Currently, risk estimates are most often based on gross outcomes of disease such as 

occurrence of cancer, a neurological disorder, or a visible birth defect. It has long been assumed 

that these disease outcomes were the result of numerous and crucial alterations at the molecular 

level inside living cells. Molecules of DNA, RNA, or endogenous proteins all have crucial 

chemical and physical arrangements and interactions. Alterations in these arrangements and/or 

interactions might be the first step in a cascade that can then lead to disease, morbidity, and 

mortality. Research in genomics, proteomics, metabolomics, and computational toxicology are 

expected to result in risk assessments based on specific changes at the molecular level, rather 

than just the number of tumors, deaths, overt clinical changes observed in test animals. In the 

future, assessments will be based on changes in molecular markers, such as the number of DNA 

molecules altered at a crucial site, the change in an allosteric membrane protein that acts as a 

receptor, or the change in a regulating protein inside the cell. The key is we will better 

understand how those changes lead directly to the types of adverse health effects that have been 

the traditional basis of EPA risk assessments and to use this understanding to reduce the 

uncertainties in the extrapolation of effects across dose, species and chemicals. 

 Over the past 15 years the explosive growth of information regarding the structure of 

many of the “molecules of life” has occurred. The sequencing of the human genome for 

example, has given us a wealth of information only dreamed of in the previous decades. It will 

soon be possible to accurately ascertain exactly where a xenobiotic chemical interacts with 

regions of the genome and with other endogenous molecules. Binding of a toxic chemical with a 

crucial portion of membrane proteins for example, may alter the structure and porosity of the cell 

membrane. Such a change might lead to a change in the membrane potential of the cell. If that 

cell is a neuron or cardiac cell critical physiologic changes might occur that could lead to overt 

disease. 

 Advancements in the "omics" technologies coupled with the advances in analytic tools 

such as microarray techniques will enable us to predict changes and evaluate which changes can 

initiate, promote, or adjoin the cascade to disease. Computational systems biology techniques 

“Computational toxicology: 

integration of modern 

computing and information 

technology with molecular 

biology”
2 

will help us integrate the necessary quantitative information related to those changes. The 

concepts of computational chemistry and relating structure to activity are not new. For years 

chemists and physicists have been relating key chemical and physical properties to molecular 

and atomic structures. However, with advances in high performance computing and the 

application of high throughput biological screening assays, there is unprecedented opportunity to 

conduct this research on a much larger scale of operation. 

 From the sciences of “omics" technologies arise two possibilities: the first is the broad 

spectrum (or high content) interrogation of multiple molecular events within a cell or tissue (e.g. 

microarrays) and the second is the application of one or a few molecular targets in a high 

throughput screening mode against large numbers of chemicals. Both of these possibilities 

present opportunities and challenges. The opportunity is to use the ensuing outputs of these 

technologies to shift toxicology from a descriptive to a predictive science and therefore improve 

the ability of the EPA to assess hazard and characterize risk. The challenge lies in reducing vast 

amounts of data generated by these approaches into useful information and in determining their 

biological significance. The EPA’s ORD has formulated its research program with this in mind 

and will approach the issues through several targeted research tracks. The main thrust of the 

research program will be to develop generic approaches which are adaptable to the more specific 

types of risk assessment activities that are undertaken in the other Laboratories, Centers, or 

Program Offices. 

 This challenge was given urgency in 2002, when Congress ordered a redirection of $4 

million from available EPA funds, “for the research, development and validation of non-animal 

alternative chemical screening and prioritization methods, such as rapid, non-animal screens 

and Quantitative Structure Activity Relationships (QSAR), for potential inclusion in EPA’s 

current and future relevant chemical evaluation programs”. To fulfill this directive, the EPA 

embarked on development of a research program that: (1) was consistent with the Congressional 

mandate; (2) complemented and leveraged related on-going Agency sponsored efforts to 

consider alternative test methods; (3) further advanced the research to support the Agency’s 

mission; and (4) would not duplicate the mission and programs in this area conducted by other 

agencies. An innovative program, entitled Computational Toxicology (CompTox), was initiated 

to target these goals and, in the process, significantly advance toxicology and risk assessment as 

currently practiced by the Agency and the broader environmental sciences community. As 

recommended by Congress, the proposed approach was developed in consultation with the 

Office of Pesticides, Prevention, and Toxic Substances (OPPTS). The research projects funded in 

FY02 and FY03 were largely devoted to proof-of-concept demonstrations that the approaches of 

computational toxicology could be adapted to the study of endocrine disruptors (chemicals which 

perturb the functioning of an endocrine system and which subsequently lead to adverse health 

effects in an individual or a population). Early successes of these efforts included refinement of 

estrogen receptor ligand binding data for use in development of quantitative structure-activity 

models, evaluation of two EPA-developed cells lines for the detection of activity of estrogens 

and androgens, and the development of an alternative test method for evaluating effects on 

steroidogenesis that avoids the use of animal tissues. As the current document is intended to be 

primarily forward-looking, these early projects will not be discussed further in this document 

(see the 2004 Activity Report for more details). 
3 

 With increasing attention to and expectations for the CompTox program over the next 

several years, ORD developed A Framework for a Computational Toxicology Research Program

in 2003, which provided strategic direction for the program. This 

document was the product of a cross functional ORD team of scientists 

and was endorsed by the Science Advisory Board (SAB). The Framework 

identified three objectives for computational toxicology in the EPA and 

these have been translated into three Long Term Goals (LTGs) for the 

research program described herein: 

• I. Risk assessors use improved methods and tools to better 

understand and describe the linkages of the source-tooutcome paradigm, 

• II. EPA Program Offices use advanced hazard characterization tools to prioritize 

and screen chemicals for toxicological evaluation and 

• III. EPA assessors and regulators use new and improved methods and models 

based on the latest science for enhanced dose-response assessment and 

quantitative risk assessment. 

II. ORD Computational Toxicology Program 

 With issuance of the Framework in FY04, ORD began the process of implementing a 

research program by establishing the Computational Toxicology Implementation and Steering 

Committee (CTISC) to help guide the program and communicate progress across the Agency. 

Membership on the committee consists of the chair, the Director of NCCT, two representatives 

from each of ORD’s Laboratories and Centers, which are nominated by their Directors, 

appropriate ORD management representatives (e.g., Associate Laboratory Directors or National 

Program Managers from aligned programs), an ORD Regional Scientist, and representatives of 

the main client offices of the CTRP Program. Membership consists of a three year term, 

renewable once, (link to current membership). Recognition of the potential impact of 

computational approaches to toxicology has continued to grow over the past several years, and 

now research contributions flow from three distinct areas within ORD. The first component is 

composed of the efforts of the NCCT, an organizational entity created within ORD in 2005, to 

accelerate the development and use of computational tools in the EPA’s regulatory operations. 

The second component consists of projects funded in FY05 by a competitive process overseen by 

the CTISC. The final component of the CTRP is derived from the STAR program of ORD’s 

NCER. Within the CTRP, the STAR program supports a small number of investigator lead 

grants and two larger Centers. The relationship of these components is depicted in Figure 1. The 

innermost component (grey oval) represents the efforts taking place within the NCCT. The 

location of the inner circle indicates the central role NCCT in providing leadership to the overall 

program. In the next outer circle (green oval) are the CTISC funded projects, which represent 

research being performed in other Laboratories and Centers. These efforts are largely within the 

National Health and Environmental Effects Research Laboratory (NHEERL) and the National 

Exposure Research Laboratory (NERL). Smaller efforts are contained within the National Risk 

Management Research Laboratory (NRMRL) and the National Center for Environmental 

Assessment (NCEA). The third and final component of the CTRP is research supported by 
4 

NCER through the STAR program, funded through Request for Applications (RFAs). A 

particularly important effort of the STAR program was the funding of two academic centers to 

support the advancement of bioinformatics in environment health (see below). Greater detail on 

the projects in each of the three components is provided in Section III. It is important to 

recognize that considerable efforts in computational research and in “omic” research reside 

outside those directly or indirectly associated with the NCCT and CTISC. These efforts, 

represented by the yellow oval in Figure 1, are part of ORD’s overall research in health, 

ecological, and risk assessment, and are contained within a variety of Multi-Year Plans (MYPs). 

CompTox

Research 

Program

CompTox

Center

Computational/Omic

Research

ORD

Research

Figure 1 – Component of the Computational Toxicology Research Program within ORD 

The National Center for Computational Toxicology: In October 2004, the EPA Science 

Advisor and Assistant Administrator for ORD, Dr. Paul Gilman announced the formation of the 

NCCT, which began official functions in February 2005. The announcement states: 

 “The Center will advance the science needed to more quickly and efficiently evaluate the potential risk of 

chemicals to human health and the environment. The Center will coordinate and implement EPA’s research on 

computational toxicology to provide tools to conduct more rapid risk assessments and improve the identification of 

chemicals for testing that may be of greatest risk.” 

NCCT's Mission 

The Center’s mission is to achieve the goals set forth by ORD and the Framework 

document by performing research that integrates modern computing and information technology 

with molecular biology to improve Agency prioritization of data requirements and risk 

assessment of chemicals. The Center’s staffing profile is designed to provide expertise in 

systems biology, computational chemistry, and bioinformatics, as outlined in Figure 2. 

Recruitments are currently underway for two senior level investigators, one in the area of 

computational systems biology and the other in bioinformatics. These positions are expected to 

be filled by the end of FY06 and with supporting positions will complete staffing of the NCCT, 

as currently envisioned. 
National Center for Computational Toxicology

(NCCT)

Director: Robert Kavlock

Deputy Director: Jerry Blancato

Hugh Barton

Michael Breen (Post-doc)

Elaine Cohen Hubal

Rory Conolly

David Dix

Keith Houck

Matthew Martin (EPA Intern)

Chester Rodriquez (Post doc)

Rhyne Woodrow Setzer

To Be Named (2)

Steve Little

Melissa Pasquinelli (Post-doc)

Jim Rabinowitz

Ann Richard

Eric Weber (Detailed to NERL) 

To Be Named (2)

Program & Management Analyst- Karen Dean

Executive Secretary- Sandra Roberts -

Admin Assistant (SEE)- Dorothy Goodson

SYSTEMS MODELING COMPUTATIONAL CHEMISTRY BIOINFORMATICS

ADMINISTRATIVE SUPPORT

Figure 1 - NCCT Staffing Profile 

The NCCT scientists will advance the field of computational toxicology by working 

across ORD to develop predictive models for screening and testing chemicals and by reducing 

the uncertainties associated with extrapolating predicted effects across chemicals, levels of 

biological organization and species. They will provide scientific expertise and leadership related 

to the application of mathematical and computational tools and models, conduct research to 

improve the predictive capabilities of the methods, models and measurements that constitute the 

input materials to the computational approaches. In addition, they will conduct and sponsor 

research to provide models for fate and transport of chemicals, environmental exposures to 

humans and wildlife, delivery of the chemical to the target site of toxicity, molecular and cellular 

pathways of toxicity, and ultimately systems-level understanding of biological processes and 

their perturbation. A key part of the process of advancing the science will involve developing 

partnerships with other government and private organizations so as to best leverage resources 

committed to the effort. (Link to current NCCT staffing: http://epa.gov/ncct/organization.html ) 

STAR Centers 

 STAR Environmental Bioinformatics Centers: One area of research need, implicit in 

many of the research projects contained with the CTRP, is bioinformatics. This rapidly emerging 

technology is crucial to the computational toxicology program, and whereas the NCCT will be 

adding a senior level bioinformaticist in FY06, there remains a large gap in ORD relative to the 

ability to analyze the high volumes of molecular data and to predict potential toxicity, modes of 

action, and ultimately risk. To help bridge this gap, NCER has supported the establishment of 

two STAR Environmental Bioinformatics Research Centers (EBRC). The Research Center for 

Environmental Bioinformatics and Computational Toxicology at the University of Medicine & 

Dentistry of New Jersey (UMUDJ), Piscataway, NJ and The Carolina Environmental
6 

Bioinformatics Research Center at the University of North Carolina, Chapel Hill, NC will 

operate as cooperative agreements and help facilitate the application of bioinformatics tools and 

approaches to environmental health issues supported by the CTRP. It is anticipated that over the 

next five years the EBRCs will conduct research to improve the science of bioinformatics and 

assist in the analysis and interpretation of data relevant to the protection of human health and the 

environment. Descriptions and web links of the two Centers are provided in Appendix A. 

Bioinformatics staff of the NCCT will serve as technical liaisons with the EBRCs and help guide 

interactions with other ORD investigators. 

The STAR program is planning another Request for Applications in the area of 

computational toxicology in FY07, and the topic for that call is currently under discussion. 

BOSC 

Board of Scientific Counselors: To help guide the program, ORD established a standing 

panel of Board of Scientific Counselors (BOSC) to provide review and advice to NCCT and 

CTRP. The panel first met in April 2005 to review the organization of NCCT, initial plans for 

implementation, and progress of the early CTRP work. The panel commented very favorably on 

the Center’s early progress and the means outlined to achieve its goals (BOSC CompTox 

Review). The composition of staff, plans for future hiring, establishment of working 

partnerships, and the Center’s strategic plan were especially highlighted. Several main 

recommendations were made for consideration, two of which are addressed here. The first was to 

develop a formal implementation plan for the future. The second was to develop Communities of 

Practices (CoPs) within the EPA which can serve as a networking function for interested 

scientists. Three such CoPs have been organized and are discussed later in this document. A few 

other minor suggestions were also made, which were addressed in the formal ORD response to 

the review (ORD BOSC Response). A second site visit of the BOSC is being planned for June 

19–20, 2006, which will entail an in-depth assessment of the progress NCCT has made in 

executing this implementation plan. 

III. Research Projects 

 As noted above, research projects within the CTRP are composed of projects developed 

internally within the NCCT, efforts funded across ORD by the CTISC, and research supported 

by ORD’s STAR program (in the form of both individual research grants as well as larger Center 

grants). Research efforts supporting the three LTGs identified in the CompTox Framework have 

been grouped into five research tracks: (A) Development of Data for Advanced Biological 

Models; (B) Information Technologies Development and Application; (C) Prioritization Method 

Development and Application; (D) Providing Tools and System Models for Extrapolation across 

Dose, Life Stage, and Species; and (E) Advanced Computational Toxicology Approaches to 

Improve Cumulative Risk Predictions. The alignments of the five research tracks with the LTGs 

of the Framework are depicted in Figure 3. The component research projects are identified in 

Table 1, with hyperlinks to both summary information on their individual outputs and impacts 

(Table 2), as well to more descriptive information on the research issue and relevance, approach, 

impact and partnerships in Appendix B. For those projects housed outside the NCCT, Table 3 

provides a summary of the supporting information from other MYPs. 
7 

LTG I

LTG II

LTG III

A. Development of Data 

for Advanced Biological 

Models

B. Information Technologies 

Development and Application

C. Prioritization Method 

Development and 

Application

D. Providing Tools and System 

Models for Extrapolation across 

Dose, Life Stage, and Species

E. Advanced Computational 

Toxicology Approaches to Improve 

Cumulative Risk Predictions

Figure 3 -Alignment of the Five Research Track 

 Track A contains seven projects that are targeted at collecting data to improve various 

aspects of the linkages within the source-to-outcome paradigm. Included is a project 

characterizing thyroid toxicity in an amphibian model, three projects applying “omic” 

technologies to the study of endocrine disruptors in small fish, one project examining gene 

expression networks in the rodent uterus exposed to estrogens, one project characterizing the 

properties of diesel particles that are toxic to human pulmonary cells, and one project seeking 

environmental influences on childhood asthma. These projects are distinguished in that they 

have a large data collection effort combined with a computational modeling component. Track B 

contains efforts to improve the management of information, both from the chemo-informatics 

and bio-informatics prospective, to provide the basis for development of better structure-activity 

models and to support interpretation of high throughput data being derived from Track C. 

Importantly, Track B is designed to migrate the maximum amount of information into the public 

domain so it can be used by others for a variety of purposes. Track C contains four projects 

intended to speed the development of data for hazard characterization, and includes components 

on predicting xenobiotic metabolism, molecular docking models for in-silico predictions of 

ligand-receptor interactions, high throughput data acquisition to fingerprint chemicals for 

potential hazard, and development of technologies to identify sources of fecal contaminants in 

aquatic environments. Track D contains seven projects that are primarily computational in 

nature, with efforts ranging from statistical considerations for model parameter fitting, to 

providing generic tools for cross species and cross life stage extrapolations, exploring model 

portability and linkage issues via use of software standards, modeling particular cell and organ 

processes, applying “omic” data in risk assessment, and developing tools and platforms for use 

of “omics” in regulatory decision processes. Finally, Track D contains two projects that are 

exploring computational approaches to real-world type situations where exposure is to more than 

one form of stressor. 
8 

 The outputs of the various projects within each LTG will also provide support for future 

activities in other LTGs through relationships depicted in Figure 4. 

Interactions Between LTGs

LTG I: Linkages-Risk assessors will 

use improved methods and tools to 

better understand and describe the 

linkages of the source-to-outcome 

paradigm.

LTG III: QRA-Assessors and regulators will use 

improved methods and models based on the latest 

science for enhanced quantitative risk assessment.

LTG II: Prioritization-Regulators and 

risk assessors will use advanced hazard 

characterization tools to prioritize and 

screen chemicals for toxicological 

evaluation.

Figure 4 -Interactions between LTGs 

 For example, in projects under LTG II, involving the use of advanced hazard 

characterization tools by EPA Program Offices, early efforts are being directed towards 

providing new high throughput techniques for prioritization and for identifying those 

environmental stressors that should undergo further testing (see project IIC-3). However, 

information gleaned from such high throughput technologies are expected to prove informative 

for advancing modeling efforts (e.g. projects IIID-5). Preliminary information about mode-ofaction can be extracted from these studies and the models can be exercised using a variety of 

scenarios derived from these studies. The models can help identify mode-of-action scenarios that 

would contribute most to potential hazard, and risk models could then target on those pathways. 

Additional molecular studies could then be conducted, concentrating on those scenarios 

identified as having the greatest potential impact. This next phase of information would be used 

to further refine the models and reduce uncertainty. Such iterative approaches between controlled 

experiments and advanced modeling techniques, while very useful, have proven to be very 

expensive and time consuming. The cost and required time could be reduced using some of the 

approaches developed under this LTG. The systems biology project (IIID-5) will serve as a 

culmination project whereby information from a variety of sources (high throughput, in-vitro, invivo, and in-silico) is used to formulate a risk assessment. In this project, models can be used to 

estimate the probability of adverse effects and the variability and uncertainty associated with 

those estimates from available knowledge and data. The models can also be used as testing tools 

to estimate how much uncertainty could be reduced with more experimental data and also to 

identify the type of needed data. Such linkage between LTGs will help advance the overall 

program. As noted, LTG II will make significant contributions to LTG III and will also feed 

information to LTG I. Similarly, as toxicity pathways are characterized at multiple levels of 

biological organization in LTG I, screening tools can be added to projects in LTG II that will 

enable larger numbers of chemicals to be characterized. Finally, furthering understanding of 

toxicity pathways from projects in LTG I will advance computational work in LTG III, by 

examining how various toxicity pathways interact with one another to modulate activity. 
9 

IV. Summary of Outcomes from Research 

 The implementation plan described herein was developed to provide a sequential series of 

short to mid term projects that will advance utilization of computational tools in hazard and risk 

assessment. In particular, over the next 1 to 3 years we expect to make significant progress in 

developing and applying prioritization and screening tools. For example, there will be targeted 

application of our current understanding of selected toxic endpoints so predictive models can be 

developed, leading to more efficient testing paradigms and reduction in uncertainties in interspecies extrapolation. These models will be populated with data at the molecular and 

biochemical molecular levels and will provide a basis to interpret interspecies homology and 

comparative toxicity. Proof-of-concept of ToxCast, in particular, will provide a number of EPA 

Program Offices with an extremely useful tool to improve the efficiency and effectiveness of 

hazard identification and risk assessment methodologies. New and innovative ways will be 

developed to assimilate, evaluate, and use the myriad of data assorted with molecular and 

chemical information. Further development and application of DSSTox will be a key component 

of these efforts. Computational chemistry will provide in-silico models for predicting complex 

interactions of environmental chemicals with important biochemical receptors, which can then 

lead to adverse effects. Early examples of modeled target interactions include estrogenic, 

androgenic receptors, and acetyl cholinesterase. 

 Progress will be made at developing computational models and modeling systems that 

represent comprehensive descriptions of the underlying biology of adverse impacts caused by 

exposure to environmental agents. Computational models describing the relationship between 

diesel exhaust particle composition and its genotoxic and inflammogenic properties are 

examples. Other projects will develop methods to measure and then describe the mechanisms of 

childhood asthma resulting from environmental exposures. The whole systems biology modeling 

approach will develop a range of models, from those describing pharmacodynamic connections 

between exposure and effects, to those describing complex endogenous pathways, and the 

perturbations in such pathways resulting from environmental exposures. Also, ways to 

incorporate and use “omics” information in these models will be explored. Finally, attempts will 

be made at formulating models of common, but complex, disease processes which are then 

exacerbated by exposures to exogenous substances and stressors. A detailed summary of outputs 

and impacts resulting from the projects listed in the previous Table 1 can be found in Table 2. 

V. Partnerships 

 Given the broad nature of the challenges facing computational toxicology, the CTRP 

must engage collaborative partners both across ORD and outside organizations in order to be 

successful. Internally, development of these linkages began at the management level with the 

initiation of a Memorandum of Understanding (MOA) with the NHEERL and the NERL. This 

MOA provides the NCCT with access to administrative support functions such as oversight of 

extramural actions, formal quality assurance procedures, and planning for information 

management. Access to these and other functions allow for more efficient and effective use of 

FTEs within the Center. Management staff of the NCCT has a regularly scheduled monthly 

meeting with counterparts in NERL and NHEERL to ensure smooth operations and to strategize 

on common scientific directions. 
10 

 With the successful initiation of a cross functional ORD research program supporting the 

Framework and the creation of the NCCT in February 2005, the charter of the CTISC was 

redefined to include the following functions: 

• Serving in an oversight function over the computational toxicology projects 

funded by the activities of the committee and coordinate, as appropriate, with 

relevant MYPs associated with the projects. 

• Establishing partnerships external to the EPA to help support the Framework as 

implemented in the various Laboratories and Centers of ORD. 

• Advising NCER on the formulation of ideas for new RFAs, providing suggestions 

for scientific peer reviewers, serving on relevancy reviews as appropriate, and 

keeping informed on the research progress of the STAR grants program. 

• Acting in a consultancy role for activities of the NCCT. 

• Providing a forum for communication of computational toxicology activities 

across ORD and the Agency. 

 The ORD's multi-year planning process provides another opportunity for linkage 

between the CTRP and related research efforts. Each of the MYPs is led by a National Program 

Director with support from staff of the relevant Laboratories and Centers. The NCCT participates 

actively in four of the MYP teams. Three contain similar research activities for screening and 

prioritizing chemicals, i.e. Endocrine Disrupting Chemicals (EDCs, LTG III), Safe 

Pesticides/Safe Products (SP2, LTG I), and Drinking Water Research Program (DW, LTG II) 

whereas the fourth has a major focus on the incorporation of biologically based mode-of-action 

information into quantitative risk assessment (the Human Health Research Strategy and the 

Human Health MYP, LTG II). The Director of the NCCT meets at least quarterly with the 

National Program Directors for these MYPs, and the NCCT is attempting to allocate at least 10% 

of its available extramural resources to computational toxicology-related projects in other 

Laboratories and Centers. Preference in deciding which projects to support will be given to 

those efforts that have the active participation of a member of the NCCT. 

 

 On the scientific level, the NCCT has initiated three Communities of Practice (CoP) in 

the areas of Chemoinformatics, Biological Modeling, and Categorization and Prioritization that 

are intended to unite practitioners in the designated fields. Prior to implementation of these CoPs, 

the concept of ‘adjunct’ appointments with researchers outside the Center to create a 

collaborative environment was considered, but there was no clear idea of how such appointments 

would function. The concept of the CoPs was suggested by the BOSC in April 2005, and has 

since been adopted as a primary means of communication and integration of activities across 

ORD, the EPA, and outside entities. These efforts will serve to enhance communication and 

coordination, develop common standards, promote consistency, evaluate and provide guidance 

on best practices, recommend research priorities, and provide training to interested parties. 

Copies of the charters for these groups are provided here (Chemo; Modeling; and CPCP). 

 Extending further outside the confines of ORD and the EPA, the NCCT is also 

developing strong linkages with the National Toxicology Program of the NIEHS. The 

commonalities between A Framework for Computational Toxicology and the A National 

Toxicology Program for the 21st Century: A Roadmap for the Future and the co-location on the 
11 

RTP federal research campus provided a strong impetus for these interactions. Beginning in mid 

2005, the two groups began an exchange of work-in-progress seminars that 

led to establishment of a number of specific events, including efforts that 

draw on the complementary strengths of the two organizations. These 

include a joint seminar program, collaborating on statistical analysis of 

complex dose-response patterns, sharing of workgroup and CoP 

memberships, developing a mathematical construct for model-building 

based on information from varying levels of biological information, meta 

analysis of PBPK model development across chemicals, participating in a class study on 

perfluornated compounds, and the offering of technical training courses to parties outside the 

organizations. Working relationships are also being developed with the National Toxicology 

Programs Interagency Center for the Evaluation of Alternative Toxicological Methods 

(NICEATM), which is currently exploring high throughput hazard identification tools. These 

efforts extend to opportunities for collaboration and linkages with NIH through the NIH 

Roadmap, Accelerating Medical Discoveries to Improve Health are also being sought. This 

especially concerns the theme, “New Pathways to Discovery: Molecular Libraries and Molecular 

Imaging and Building Blocks Biological Pathways and Networks.” Discussions with Dr. Chris 

Austin, Senior Advisor to Director of Translational Research of the National Human Genome 

Research Institute, have led to consideration for inclusion of the EPA-relevant chemicals and 

biological assays within the Molecular Libraries Initiative. 

VI. Conclusion 

 Ultimately the CTRP and NCCT will provide outcomes which will help reduce or 

prevent the risk to humans and the environment from environmental stressors. As such, the 

program is aligned with overarching goals of the Agency. Figure 5 depicts the design of the 

program for achieving these goals. Shown is the progression of the research program over the 

next several years. We list the resources and inputs that will help define the program and the 

activities that are discussed in this plan. Next, we highlight expected research outputs, means for 

their transfer, and example clients and beneficiaries of these outputs. In summary, this plan will 

communicate the intent or expected outcomes and how those outcomes will improve risk 

assessment and management procedures and policies. This will clearly take several years to 

come to fruition. Hence this plan, while it outlines a multifaceted process, has concentrated 

mostly on the first years where the activities are commenced and specific outputs are produced. 

The desire is that the outputs will help achieve short-term outcomes and will greatly contribute to 

the results expected in later years. 

 This plan should be considered a “living document” to be updated and modified on a 

yearly basis. These annual updates will reflect and document progress, any necessary change in 

direction, and added objectives as the program matures.
12 

Research Program

Research

Outputs

Clients 

Short-Term

Outcomes

Short-Term 

Outcomes

Externalities that May Impact the CompTox Program 

Congressional appropriations and Administration budget decisions, changes in EPA science priorities, changes in EPA regulatory requirements, av

consent agreements

Research Topics &

Activities

Inputs and ResourcesStrategic guidance•Research Plans:client needs, research questions, research priorities, outcomes, multi-year plans (EDC, SP2, DW, HH), Research Strategies•Partnerships•ORD Intramural Research Resources•ORD Extramural (STAR) Research Resources•Program-level funding & FTE

ORD conducts research to:•Develop improved methods and tools to better understand and describe the linkages of the source to outcome paradigm•Develop advanced hazard characterization tools to prioritize and screen chemicals for toxicological evaluation•Develop new and improved methods and models based on the latest science for enhanced quantitative risk assessment

ORD research provides:•Prioritization and screening tools with abbreviated testing protocols to improve inter-species extrapolation and identify potential toxicities •New and innovative ways to assimilate, evaluate, and use molecular and chemical information •In-silico means to predict complex interations of pollutants with important endogenous proteins•Modeling systems that represent comprehensive descriptions of the underlying biology of adverse impacts caused by environmental pollutants

•Scientific conferences and workshops•Peerreviewed publications•Technical and decision support •Publicly available data bases and models

•EPA (OPPTS, 

OW, OCHP, OEI, OAR, Regions•Risk assessors (IRIS, RAF)•Other Federal agencies (FDA, ATSDR, NIH, NTP, DOE)•States•Tribes•International health organizations and universities (WHO)•Regulated community •Academic community •Targeted communities•American Public

Ou

Independent experts, risk assessors, or others in the field:•Use ORD’sScreening and prioritization tools to identify the greatest potential toxicity for large number of compounds or compound classes:•Use ORD’sAdvanced data base systems to study the relationships between exposure, chemical properties, molecular interactions and toxicity•Use ORD’smethods and models to better characterize the mechanistic basis for adverse effects at molecular level for risk Assessment purposes• Use ORD’smethods and models to characterizeeffectiveness of risk management decisions

•Laof ccomclaspesothposand furare ide•Reuse of a•Excomexpprorisk •Domolatesciein mmadecimpeffecosof rmadec

In

O

Research Outputs

Outreach 

& 

Transfer

Figure 5 -Computational Toxicology Research Program Design
13 

Table 1 -Identification of CTRP Projects

Table 1- Organization of Projects in the Computational Toxicology Program (The Roman numeral indicates the Long Term Goal (LTG), the A-E letter the Arabic numeral identifies the project number. Projects shown in blue are projects largely external to the NCCT; projects in red are primarily conduc

in green are part of the STAR Program. Note: the STAR Environmental Bioinformatics Centers are not listed here, as they are cross cutting activities). 

Long Term 

Goal Research Track Project Title 

I. EPA risk assessors use improved methods and tools to better understand and describe linkages across the source-tooutcome paradigm 

A. Development of 

Data for Advanced 

Biological Models 

IA-1 Linkage of Exposure and Effects Using Genomics, Proteomics, and Metabonomics in Small Fish Models

IA-2 Systems Biology Modeling of Fathead Minnow Response to Endocrine Disruptors

IA-3 Chemically Induced Changes in Gene Expression Patterns along the HPG Axis at Different Organization Levels Using a 

IA-4 A Systems Approach to Characterizing and Predicting Thyroid Toxicity Using an Amphibian Model

IA-5 Estrogen Elicited Gene Expression Network Elucidation in the Rat Uterus

IA-6 Risk Assessment of the inflammogenic and mutagenic effects of diesel exhaust particles: A systems biology approachIA-7 Mechanistic Indicators of Childhood Asthma (MICA)

B. Information 

Technologies 

Development and 

Application 

IIB-1 Integrated Chemical Information Technologies Applied to Toxicology II. EPA Program 

Offices use advanced hazard characterization tools to prioritize and screen chemicals for toxicological evaluation 

C. Prioritization 

Method Development 

and Application 

IIC-1 Simulating Metabolism of Xenobiotic Chemicals as a Predictor of Toxicity

IIC-2 Modeling Molecular Targets for Toxicity, a Computational Approach to Understanding Key Steps in the Mechanisms f

Bioassay Requirements

IIC-3 ToxCast, a Tool for Categorization and Prioritization of Chemical Hazard Based on Multi-Dimensional Information Do

IIC-4 Development of microbial metagenomic markers for environmental monitoring and risk assessment

D. Providing Tools 

and System Models 

for Extrapolation 

Across Dose, Life 

Stage and Species 

IIID-1 Statistical Methodology for Estimating Parameters in PBPK/PD Models

IIID-2 Modeling Toxicokinetics for Cross-Species Extrapolation of Developmental Effects

IIID-3 Development of a portable software language for physiologically-based pharmacokinetic (PBPK) models

IIID-4 Systems Modeling of Prostate Regulation and Response to Antiandrogen

IIID-5 Systems Biology Model Development and Application.

IIID-6 Use of Toxicogenomics Data in Risk Assessment: Case Study for a Chemical in the Androgen-Mediated Male Reprodu

IIID-7 Developing Computational Tools for Application of Toxicogenomics to Environmental Regulations and Risk Assessm

III. EPA assessors and regulators use new and improved methods and models based on the latest science for enhanced dose-response assessment and quantitative risk assessment. E. Advanced 

Computational 

Toxicology 

Approaches to 

Improve Cumulative 

Risk Predictions 

IIIE-1 Dose-Time-Response Modeling for Evaluating Cumulative Risk of N-Methyl Carbamate Pesticides

IIIE-2 Application of Visual Analytic Tools to Evaluate Complex Relationships between Environmental Factors and Health O
14 

Table 2 -CTRP Output/Input Table

I. EPA risk assessors use improved methods and tools to better understand and describe linkages across the source to 

A. Development of Data for Advanced Biological Models 

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 E

IA-1 Linkage of Exposure and Effects Using Genomics, Proteomics, and Metabonomics in Small Fish Models 

Development of a conceptual model for the HPG axis in small fish models as a basis for focused hypothesis testing of potential endocrine disrupting chemicals. 

Preliminary results on the effects of model chemicals on the fecundity of fathead minnows and single gene and protein expression. 

Gene and protein expression as the basis for extrapolation of the effects of endocrine disrupting chemicals across small fish models. 

OPPTS - Develotest methods for humans and wildincorporation of OPP's probabilist

IA-2 Systems Biology Modeling of Fathead Minnow Response to Endocrine Disruptors

To determine and compare gene and protein expression profiles and physiological and reproductive endpoints for adult FHM exposed to a model estrogen 17 alphaethinylestradiol (EE2), androgen (17βtrenbolone), or their antagonists (ZM 

189,154 and flutamide, respectively). 

To predict gene expression patterns of two compounds (zearalenone and EE2) that are environmental estrogens. 

To develop a computational modeling framework that integrates exposure concentration, gene expression, and proteomic profiles with physiological endpoints. 

This STAR Grancomputational mand protein biompredictive of advestrogenic compominnows. This qimprove risk asseand by extrapoladisrupting compo

IA-3 Chemically Induced Changes in Gene Expression Patterns along the HPG axis at Different Organization Levels Using a Small Animal Model (Medaka) 

Determine natural backgrounds and variability of gene expressions, optimize methods based on findings, develop methods to identify gene products and begin exposure studies with model chemicals. 

Finalize exposure studies and identification of genomic expression patterns based on four model compounds and perform statistical comparisons. Quantify gene products and determine their functionality and biological relevance. Compare responses of test chemicals to model chemicals. Submit final report on project. 

A model will be relevance of the oexpression profiltarget sites, and tgene expression 

outcome (e.g., rethresholds at the of effects on the survival, growth fecundity as well be determined. Uindividual chemimixtures to interfconcern will enhbasic mechanismthe potential to dand cost effective assessment. The a wider range of 
15 

I. EPA risk assessors use improved methods and tools to better understand and describe linkages across the source to out

A. Development of Data for Advanced Biological Models (cont.)

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 E

IA-4 A Systems Approach to Characterizing and Predicting Thyroid Toxicity Using an Amphibian Model

Establish data management system; Formulate initial systems model for thyroid function; Develop in-vitro thyroid and pituitary culture systems. 

Characterize molecular changes associated with TH inhibition; Characterize specific molecular responses in thyroid gland culture; Develop initial QSAR for NIS inhibition. 

Refine systems model for relating MOA to outcome; Complete first round QSAR hypothesis for testing for NIS. 

Development of HPT so that predtesting protocols inter-species extrPopulate these mmolecular and biprovide a basis to and comparative 

IA-5 Estrogen Elicited Gene Expression Network Elucidation in the Rat Uterus

Establish estrogenic endocrine elicited doseand time-dependent changes in rat uterine gene expression using four estrogen receptor ligands; investigate role of ER in mediating changes in gene expression. 

Phenotypically anchor changes in gene expression to histopathological outcomes. Develop a computational model that describes the estrogen elicited gene expression network; Submit final project report. 

The models deveexpression changEED elicited histExamination of mdisruptors with vkey regulatory noresponses, which high throughput assays for chemiresulting models algorithms (i.e. Pcomprehensive mpituitary-gonadal 

IA-6 Risk Assessment of the Inflamagenic and Mutagenic Effects of Diesel Exhaust Particles: A systems Biology Approach

Generate the first four DEP samples; Conduct chemical characterization of the DEP samples; Conduct a pilot study assessing the inflammogenicity and signal transduction profile of two DEP samples in human and rodent airway epithelial cells. 

Generate eight DEP samples; Identify overlaps in human and mouse gene expression patterns associated with DEP inflammogenicity or mutagenicity; Perform a bioassaydirected fractionation on the available DEPs and determine the distribution of mass and mutagenicity among the fractions. 

Generate remaining four DEP samples; Define signal transduction pathways involved in inflammogenic and mutagenic responses to DEP exposure based on gene expression patterns; Complete biological models of relevant signaling pathways. 

Predictive modelrelationship betwcomposition and properties: Well based risk assessincluding the use the risk assessmeNOTE: for 4th yeall biological meand the transfer oassessors for the Develop exposurexposure data, geof roadways and metals), and homexposure model eexposure and ear
16 

I. EPA risk assessors use improved methods and tools to better understand and describe linkages across the source to out

A. Development of Date for Advanced Biological Models (cont.)

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 E

1A-7_Mechanistic Indicators of Childhood Asthma (MICA)

Collect bloods and isolate RNA from rodent lung and blood samples and blood; Develop QA plan for gene expression analysis; Develop and seek approval for Intramural Research Protocol including recommended and standard operating protocols for collecting pilot study gene expression analysis data. 

Develop relevant educational modules appropriate for schoolchildren and their parents to enhance participant recruitment and ensure their informed consent; Develop asthma severity score criteria based on asthma diagnosis, medicine and FEV1 in schoolbased questionnaires and lung function measurements for selection of schoolchildren; Develop standard operating procedures for the collection, processing, and analysis of biological samples from children; Complete monitoring of ambient exposure levels indoor/outdoor. 

Develop plan for collection and analysis of biological samples; Collect, prepare process and analyze human biological samples from 200 children; Prepare comprehensive data base NOTE: for 4th year (09): Complete a data base of all biological measurements and analytic results and the transfer of this data to modelers and risk assessors for the preparation of manuscripts; Develop exposure models based on ambient exposure data, geographic information (locations of roadways and major sources of PAHs and metals), and home /school locations; Compare exposure model estimates with biomarkers of exposure and early effects. 

Research wouAnnual Perfo2012, provide with methods exposure and children, inclucharacterizing hazards and rreducing risks from harmful Enhancement assessment, ppredictive moassessment anfor large-scalBetter determexposure-respthe low-exposincorporation into experimedevelopment biologically-bexposure-respresponses outexperimental of regulatory pathways, thalead to adversthe evaluation points with linchanges. 
17 

II. EPA Program Offices use advanced hazard characterization tools to prioritize and screen chemicals for toxicolo

B. Information Technologies Development and Application 

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 E

IIB-1 Integrated Chemical Information Technologies Applied to Toxicants

05: Incorporate DSSTox standard chemical fields into CEBS; Create DSSTox database and documentation files for NTP Immunotox database; create chemical index file for the EPA IRIS programs; Establish Communities of Practice - Chemoinformatics Workgroup to begin to coordinate efforts across the Agency to inventory, retrieve, and explore chemical information data.; Propose plan to link DSSTox effort with the NLM PubChem project. 06: Create DSSTox standard chemical field index files for public genomics databases and NTP legacy toxicity data; coordinate linkages within the CEBS relational search environment; Create and publish DSSTox database and documentation files for additional published toxicity databases and chemical index files for EPA and NTP programs; Propose plan to structure-index and quality review chemical information in EPA data files currently on the web and to provide for an EPA-wide structure browser; Assist with formation of proposed toxicity chemicals subset for high-throughput testing in ToxCast and the NIH Molecular Libraries Screening Initiative collaboration. 

Assist with incorporation chemical structure browser technology into EPA ArrayTrack, and full with DSSTox data files and structure-indexed public genomics data; Establish procedures and protocols for automating the chemical annotation of new data submitted to CEBS or EPA ArrayTrack from the DSSTox Master chemical list; Continue expansion of the DSSTox public toxicity database inventory; Implement plan to uniformly structure-index EPA data files and provide EPA website structuresearchability through curated EPA chemical data files; Create structure-annotated database of test results for toxicity subset chemicals from the NTP/NIH Molecular Libraries Initiative. 

Advise and assist with the implementation of chemical definitions (e.g., assigning a chemical to a class) and structure analog searching capability across chemically indexed data files, integrated with toxicogenomics data and bioinformatics capabilities, to serve as the foundation for chemoinformatics capabilities across EPA and NTP data; Advise and assist with the development of procedures and capabilities for deriving chemical signatures for predicting toxicity outcomes from the complete profile of ToxCast data; Begin to tabulate and explore data from NTP/NIH Molecular Libraries screening collaboration in relation to other DSSTox databases; Implement procedures for expanding the structure-annotation of EPA chemical data records and providing methods for flexible structure or analog searching on the EPA website. 

Improve capaintegrate usefactivity informnew data, botThese efforts impact a wide offices that heinformation rProduction Vthe PremanufProgram in OProgram, and Programs. 
18 

II. EPA Program Offices use advanced hazard characterization tools to prioritize and screen chemicals for toxicologic

C. Prioritization Method Development and Application 

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 

IIC-1 Simulating Metabolism of Xenobiotic Chemicals as a Predictor of Toxicity

For priority chemicals, incorporate available chemical metadata, metabolism data, and metabolic maps into a searchable database for data management and structure/substructure searchable access; Forecast metabolic pathways for selected priority chemicals using existing simulator for liver metabolism; Initiate in-vitro Phase I liver microsome experiments; incorporate new laboratory and literature metabolism data into simulator training sets. 

Confirm formation of predicted metabolites for priority chemicals and compare observed maps to forecasted maps; Develop and approach to evaluate and enhance simulator performance through improvement of transformation probability estimates and expansion of transformation reaction domain; evaluation of refined simulator and model predictions in context of Program Office prioritization needs. 

Provide effects-b(parent compounProgram Officesto prioritize chemeffects of parent reliability estimaand to rank chemand toxicity testiOPPT for predictsearchable metabuse for identificasubstructures of ilinkage of effects simulation. 

IIC-2 Modeling Molecular Targets for Toxicity: a Computational Approach to Understanding Key Steps in the Mechanisms for Toxicity and a Tool for Prioritizing Bioassay Requirements

Results of the use of the target-toxicant paradigm to screen for estrogenicity/androgenicity; Results of the affect of the two binding site model on the cumulative risk of chemicals acting through the enzyme AChE. 

Results of the importance of protein flexibility in evaluating the interaction of chemicals with macromolecular receptors. 

Evaluation of the use of the targettoxicant method as a tool in a diverse chemical screen; Application of the target-toxicant approach to other targets of toxicity. 

Help fulfill the Ahazard identificaother computatioScreening. 

IIC-3 ToxCast: A Tool for Categorization and Prioritization of Chemical Hazard Base on Multi-Dimensional Information Domains

Develop conceptual framework for ToxCast (started in 05); establish initial battery of assays across the information domains, identify list of chemicals to evaluate proof of concept for framework and begin data acquisition. 

Report on the utility of statistical clustering techniques on assay results from pilot chemicals to group them according to known toxicity patterns; revise framework as dictated by results. 

A biologically anto associate chemwill provide a nuan extremely useimproving the efidentification and employed by the 

IIC-4 Development of Microbial Metagenomic Markers for Environmental Monitoring and Risk

Evaluation of metagenomic databases as a source of molecular markers to assess human and animal fecal contamination in surface waters. 

Report on the evaluation of PCRbased host-specific assays to confirm the presence of animal fecal sources of pollution in waters impacted by fecal contamination; Report on fecal indicator microorganisms and/or genetic markers from fecal material whose densities in recreational waters best correlate with the rates of illnesses in users of recreational waters. 

Development of identification, enassessment and tnonhuman fecal and supplies. 
19 

III. EPA risk assessors and regulators use models based on the best science to reduce uncertainties in the dose-respon

extrapolations. 

D. Providing Tools and System Models for Extrapolation Across Dose, Life Stage, and Species 

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 

IIID-1 Statistical Methodology for Estimating Parameters in PBPK/PD Models

Submission of a journal article outlining outstanding statistical issues in the analysis of PBPK models and providing methods for use of formal statistical methods for more reliable and rational estimates of key parameters and for model evaluation. 

Joint NIEHS/NCCT workshop on Statistical Issues in PBPK/PD modeling to establish expert based consensus on best practices in statistical analysis and evaluation of PBPK/PD models. 

Publication of framework for formal statistical analysis of PBPK/PD models. 

Better characterizparameters and qpharmacokinetic predictions will rtheir more generareplacement of dtransparent mechuncertainty, in tuincreasing the trarisk assessments, including OPP, O

IIID-2 Modeling Toxicokinetics for Cross Species Extrapolation of Developmental Effects

Model rat pup lactational exposure for perfluooctanoate (PFOA) using compartmental approaches. 

Develop initial physiologically-based pharmacokinetic model for PFOA in rat maternal-fetal-pup unit and identify data gaps in relation to rat and human. 

Evaluate modeled dosimetry for rat fetus and pup for a limited number of prototype compounds to inform the uncertainty in use of maternal exposure dose in risk assessments. 

Characterization uncertainties attematernal exposurinformation on thdirectly form the assessment by demetric for extrapcompounds, incluof concept for thicompounds prodsignificant regulaThese compoundthe Agency. 

IIID-3 Development of Portable Software Language for PBPK Models

Report on findings of assessing 

extensiveness required for the XML schema. 

NCCT-sponsored meeting on evaluation of the performance of the completed XML schema and status of the visualization program. 

Report on proposal to have finalized XML schema included in the next release of SBML and evaluation of visualization program. 

Enhance the abilquantitative doseand mechanism oability to create, PBPK models wicompatibility are community. The models to biologinterest, since thiefficient means odose from the PBresponses. 
20 

III. EPA risk assessors and regulators use models based on the best science to reduce uncertainties in the dose-respon

extrapolations. (cont.)

D. Providing Tools and System Models for Extrapolation across Dose, Life Stage, and Species (cont.)

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 

IIID-4 Systems Modeling of Prostate Regulation and Response to Androgens

Prostrate function model following castration. 

Dose-response relationship with testosterone and antiandrogen exposure. 

Biologically based model of prostate androgen dependent gene regulation incorporating genomics data. 

Development and based model of epertubationscausfactors. A true pused by OPPTS arisk assessments. 

IIID_5 Systems Biology Model Development and Application

Journal article on use of biologic models to 

ascertain necessary resolution of exposure measurements; Collaborative groups formed; Formulation of conceptual model and writing the mathematics and code for testing models; Selection and begin model implementation for endogenous biochemical system; Start model development of disease process. 

 Implementation of model and begin application for cases with exposures to known environmental toxicants – abstracts and presentations; Disease model coded, exercised, and evaluated; Determine through literature search and other means for examples of exogenous exposure that impact the disease process. 

 Journal articles illustrating some uses of “omics” information in quantitative models; Summary report for Agency use on earliest best practices on "omics" and system biology models; Journal article for disease model; Enhancement of disease model to incorporate exposures to environmental toxicants. 

The latest mechabetween exposurconditions, and grationally accounis a great need fomolecular informand information studies will be usseveral program OAR. 

IIID-6 Use of Toxicogenomic Data in Risk Assessment: Case Study for a Chemical in the Androgen-Mediated Male Reproductive Development Pathway

Draft of the case study report (includes scoping exercise complete for case studies; i.e., progress on case study and defining approach for integrating TG data into risk assessment); Discussions with the EPA chemical assessment team, Regions, and Program Offices. 

Conduct EPA Colloquium presenting results and lessons learned from the case study; External peer review draft of report; Submit manuscript on project to peer-reviewed journal. 

This project was recommendation Genomics and RSpecifically, it wpractical attempt toxicogenomics dthe methods deveprepare for genomby addressing aredata may be particriteria for inclusassessment; and of toxicogenomic

IID-7 Developing Computational Tools for Application of Toxicogenomics to Environmental Regulations and Risk Assessment

Completion of participation in the 

Microarray Quality Control (MAQC) with FDA and publication of papers describing best practices- includes description of SPC Genomics Technical Framework. Installation of FDA ArrayTrack database for ORD and Agency use. 

 Continued development of ArrayTrack database and analytical tools for toxicogenomics in cooperation across Agency, with FDA, and with the NC and NJ Environmental Bioinformatics Centers. Trial incorporations of toxicogenomic data into risk assessments in collaboration with various Program and Regional Office staff. 

 Publication of examples and principles for integrating toxicogenomic data into risk assessments in peer-reviewed scientific journals and contribution of these principles into Agency science policy. 

Development of tools, and applicadata within Progrprovide EPA stafgenomics and asstoxicogenomics genvironmental schelp EPA develomethods to prope
21 

III. EPA risk assessors and regulators use models based on the best science to reduce uncertainties in the dose

species extrapolations. (cont.)

E. Advanced Computational Toxicology Approaches to Improve Cumulative Risk Predictions 

Project Number & Title Outputs/Outcomes 06 Outputs/Outcomes 07 Outputs/Outcomes 08 

IIIE-1 Dose-TimeResponse Modeling for Evaluating Cumulative Risk of N-methyl carbamates

Science Advisory Panel review (August 05) of preliminary cumulative risk assessment, including dose-response modeling; release and Science Advisory Panel review of revised cumulative risk assessment; submission of methods and dose-response models for publication in the peer-reviewed literature. 

Incorporation of dose-time-response methodology into the Agency’s BMDS software. 

The dose-responby OPP in the dothe Agency’s N-massessment. Metbe used in the futfor agents with e

IIIE-2 Application of visual analytic Tools to Evaluate Complex Relationships between Environmental Factors and Health Outcomes

Demonstration of potential for application of VA to evaluate exposure data and workshop on applying VA to analyze children’s cohort data. 

Generic conceptual model of complex relationships between environmental factors and human health outcomes. 

Demonstration of application of VA to analyze children’s cohort data. 

Develop multi-fanational-scale reg(program officesscreening and remsupport epidemioenvironment intecharacterize expotracking. The resdevelop concepts Detroit Childrenand the National 
22 

Table 3 -FTE's from ORD's Supporting MYPs 

FTEs From ORD Multi-year Plans Associated with the Computational Toxicology New Start Projects Initially Funded 

 (Note: FTEs distributions as indicated in the funded project proposal). 

ID Title Lead L/C/O FTEs Multi Year Plan 

 PM AT DW WQ ECO SP2 EDC HH CT 

IA-1 

Linkage of Exposure and 

Effects Using Genomics, 

Proteomics and 

Metabonomics in Small 

Fish Models 

NERL 12 5 5 2 

IA-6 

Risk Assessment of the 

inflammogenic and 

mutagenic effects of 

diesel exhaust particles: A 

systems biology approach 

NHEERL 2.6 1.2 0.4 1.0 

IA-7 

Mechanistic Indicators of 

Childhood Asthma 

(MICA) 

NHEERL 5.6 1.9 0.2 1.2 0.2 1.6 0.2 

IIC-1 

Simulating Metabolism of 

Xenobiotic Chemicals as 

a Predictor of Toxicity 

NERL 3.7 1.2 2.5 

IA-4 

A Systems Approach to 

Characterizing and Predicting Thyroid 

Toxicity Using an 

Amphibian Model 

NHEERL 6.6 1.8 4.7 0.1 

IIC-4 

Development of microbial 

metagenomic markers for 

environmental monitoring 

and risk assessment 

NRMRL 4.6 4.6 

IIID6 

Use of Toxicogenomic 

Data in Risk Assessment: 

workshop and Case 

Studies for a Chemical in 

the Androgen-mediated 

Male Reproductive 

Development Toxicity 

Pathway 

NCEA 1.6 0.2 

 Total 36.7 3.1 0.2 1.2 4.6 5 8.6 6.9 2.6 2.8 
24 

Appendix A 

1) The Research Center for Environmental Bioinformatics and Computational Toxicology at the 

University of Medicine & Dentistry of New Jersey (UMDNJ), Piscataway, which will bring 

together a team of computational scientists with diverse backgrounds in bioinformatics, 

chemistry and environmental science, from UMDNJ, Rutgers, and Princeton Universities, and 

the US Food and Drug Administration’s Center for Toxicoinformatics. The team will address 

multiple elements of the source-to-outcome sequence for toxic pollutants as well as develop tools 

for toxicant characterization. The computational tools developed through this effort will be 

extensively evaluated and refined through collaboration between Center scientists as well as with 

colleagues from the three universities and the EPA. Particular emphasis will be on methods that 

enhance current risk assessment practices and reduce uncertainties. Researchers will also develop 

a web-accessible Environmental Bioinformatics Knowledge Base that will provide a useroriented interface to an extensive set of information and modeling resources. 

 

2) The Carolina Environmental Bioinformatics Research Center at the University of North 

Carolina, Chapel Hill, will develop new analytic and computational methods, create efficient 

user-friendly tools to disseminate the methods to the wider community, and apply the 

computational methods to molecular toxicology and other studies. The Center brings together 

multiple investigators and disciplines, combining expertise in biostatistics, computational 

biology, chemistry, and computer science to advance the field of Computational Toxicology. 

Researchers will focus on providing biostatistician support to the Center by performing analyses 

and developing new methods in Computational Biology. The Center will also create a framework 

for merging data from various technologies in a systems-biology approach.
25 

Appendix B 

Left Blank Intentionally 
26 

IA-1 

Title: Linkage of Exposure and Effects Using Genomics, Proteomics, and Metabonomics in 

Small Fish Models. 

Lead: David Bencic, NERL 

Research Issue and Relevance: Over the past decade there has been a focused international 

effort to identify possible adverse effects of EDCs on humans and wildlife. Effects on 

reproduction and development mediated through alterations in the HPG axis have been of 

particular concern (USEPA 1998). There is convincing evidence that fish in the environment 

are being affected by EDCs both at the individual and population levels (World Health 

Organization 2002). Unfortunately, because feral fish populations are simultaneously exposed to 

multiple stressors, it is difficult, if not impossible; to accurately assess the role of EDCs in 

producing adverse impacts (World Health Organization 2002). As a result, many protocols using 

fish have been developed and validated, both nationally and internationally, for regulatory 

programs for EDCs (Ankley and Johnson 2004). In the US, a 1996 congressional mandate 

directed the US Environmental Protection Agency (EPA) to develop a formal screening and 

testing program for EDCs. Among the Tier 1 tests recommended (USEPA 1998) to detect 

endocrine disruption of the HPG axis is a 21-d reproduction assay with adult fathead minnows 

(Ankley et al. 2001). This approach is also being applied to EDC testing with two other small 

fish models (i.e., medaka, zebrafish) in other countries via activities coordinated by the 

Organization for Economic Cooperation and Development (OECD; Ankley and Johnson 2004). 

While of great utility, an important limitation of these tests is that they require significant 

investment in time and resources. Furthermore, many of the effects endpoints measured in the 

assays are not diagnostic of specific endocrine-related MOA. An ideal screening assay for EDCs 

would quickly identify diagnostic endpoints directly indicative of exposure in adult organisms. 

Detection of anomalies at the genomic level would enable screening methods of shorter duration 

to identify effects at the molecular level, soon after exposure, before they are manifested at the 

population level. This research also directly addresses the Computational Toxicology objective 

of providing approaches for prioritizing chemicals for testing. 

Approach: We propose to use a combination of whole organism endpoints, genomic, 

proteomic, and metabonomic approaches, and computational modeling to (a) identify new 

molecular biomarkers of exposure to endocrine disrupting compounds (EDCs) representing 

several modes/mechanisms of action (MOA) and (b) link those biomarkers to effects that are 

relevant for both diagnostic and predictive risk assessments using small fish models. These 

goals will be achieved through a three-phase approach that incorporates expertise across 

EPA/ORD and capitalizes on partnerships with other federal and academic laboratories. During 

Phase 1, effects of a candidate list of nine compounds having different MOA within the 

hypothalamic-pituitary-gonadal (HPG) axis will be characterized using the fathead minnow 

(Pimephales promelas) Phase 2 will take advantage of the well characterized zebrafish (Danio 

rerio) genome, to identify transcriptome and proteome level changes in addition to metabolite 

changes, associated with zebrafish exposure to the same suite of EDCs. Phase 2 data will be 

used to identify relevant molecular changes that could (a) serve as diagnostic markers for various 

types of EDC exposure and (b) begin to inform a systems-level characterization of the responses 
27 

to those exposures. In Phase 3, candidate genes/diagnostic markers identified in zebrafish (Phase 

2) will be validated in fathead minnows through focused toxicological testing aimed at 

examination of changes in specific gene expression and protein levels. In this way, changes at 

the genomic, proteomic, and metabonomic level will be linked to one another, linked across 

multiple teleost species, and ultimately linked to adverse effects at the individual- and, through 

modeling, population-level (i.e. linkage back to Phase 1). This three phase effort will identify 

new, potentially cost-effective, diagnostic exposure markers for EDCs, and developing sourceto-outcome linkages critical for effective use of biomarkers for risk assessments. This is one of 

three objectives of the EPA/ORD Computational Toxicology program (Kavlock et al. 2004). 

Progress to Date: This is New Start in the Computational Toxicology Research Program and 

was initiated in January 2005. 

Impact: The research described in this proposal will directly and indirectly impact several EPA 

Program Offices. The most immediate client for the research will by the Office of Science 

Council and Policy (OSCP) within OPPTS, who are charged with developing and validating 

screening and testing methods for chemicals that impact the HPG axis of humans and wildlife. 

ORD (MED) has been working with OSCP for the past 5 years in developing the fathead 

minnow as a model species for testing endocrine disrupting chemicals, both in the US and, under 

the auspices of the Organization for Economic Cooperation and Development (OECD), through 

the world. Because of this existing relationship with OSCP (and OECD), data from the proposed 

work have a significant and timely impact on international testing programs for endocrinedisrupting chemicals. The Office of Pesticides (also within OPPTS) also will benefit from this 

research, in two fashions. First, at least two of the test chemicals to be used for these studies 

(prochloraz, ketoconazole) are of direct interest to the Program Office with respect to both 

human health and ecological effects. This work also will benefit the Environmental Fate and 

Effects Division (EFED) within the Office of Pesticides. EFED is responsible for pesticide 

registration/risk assessments for fish and wildlife; in doing this, they are moving to probabilistic 

risk assessments that incorporate consideration of mechanism-specific data in the context of 

population-level effects, an approach parallel to this proposal. 

Partnerships/Collaborations: This research is enabled through an extensive network of EPA 

and non-EPA partners. The Ecological Exposure Research Division (EERD) in NERL has 

extensive facilities and trained personnel to conduct state-of-the-art molecular biology research 

in aquatic animals. The Mid-continent Ecology Division (MED) in NHEERL is recognized 

throughout the world for cutting edge aquatic toxicology research with small fish species, 

including the fathead minnow. The Ecosystems Research Division (ERD) in NERL will be 

responsible for metabonomics on the project; this will be achieved through the use of a new 600 

mhz NMR. In addition to EPA partnerships on the project a number of external groups will be 

involved. The EPA STAR program recently awarded a grant Dr. Nancy Denslow (University of 

Florida) for a proposal entitled “Systems biology modeling of fathead minnow response to 

endocrine disruptors”. The research proposed by Denslow et al. and that described in the present 

proposal are extremely complementary. The University of Florida team includes leaders in the 

area of ecotoxicogenomics, and strong systems biology component (contributed to the University 

of Florida effort by Dr. Karen Watanabe, Oregon Health and Sciences University). Further, their 

proposal has a significant emphasis on effects of estrogens on the HPG axis, which is not 
28 

covered in this proposal. Hence, combining the efforts of EPA and the University of Florida on 

this common research problem will result in synergistic outputs. To facilitate this collaboration, 

Dr. Denslow has agreed for the University of Florida award to be converted from a grant to a 

cooperative agreement. 

Milestones/Products: 

FY06

 Development of a conceptual model for the HPG axis in small fish models as a basis for 

focused hypothesis testing of potential endocrine disrupting chemicals. 

FY07

 Preliminary results on the effects of model chemicals on the fecundity of fathead 

minnows and single gene and protein expression 

FY08

 Gene and protein expression as the basis for extrapolation of the effects of endocrine 

disrupting chemicals across small fish models (fathead minnow and zebrafish) 

QA: QA Plan is being evaluated 
IA-2 

Title: Systems Biology Modeling of Fathead Minnow Response to Endocrine Disruptors 

Lead: Nancy Denslow, University of Florida 

Research Issue and Relevance: The U.S. Environmental Protection Agency (EPA) is 

interested in the application of novel technologies, derived from computational chemistry, 

molecular biology and systems biology, in toxicological risk assessment. In assessing risk 

associated with exposure to a chemical or other environmental stressor, a number of scientific 

uncertainties exist along a “source-to-adverse outcome” continuum, beginning with the presence 

of the chemical in the environment, the uptake and distribution of the chemical in the organism 

or environment, the presence of the active chemical at a systemic target site, and the series of 

biological events that lead to the manifestation of an adverse outcome that can be used for risk 

assessment. The object of this study to develop a computational model to evaluate molecular and 

protein biomarkers in relation to reproductive dysfunction in fathead minnows exposed to 

environmental estrogens. The model will incorporate a number of biochemical endpoints along 

the entire hypothalamic-pituitary-gonadal axis, direct evaluation of physiological changes and 

reproductive endpoints and the pharmacodynamics and kinetic distribution of the contaminants. 

The hypothesis that we will test is that genomic and proteomics biomarkers will be diagnostic of 

the estrogenic effects of environmental estrogens and that they will provide a global 

understanding of mechanisms of action that will relate specifically to reproductive endpoints in 

FHM that are adversely affected by exposure to estrogenic compounds. 

Approach: Fathead minnows will be exposed to three concentrations each of ethinylestradiol 

(EE2), and its antagonist ZM 189,154 and to combinations of the two compounds for 48 hrs or 

21 days to measure a battery of biochemical, physiological and reproductive endpoints. Short 

exposures will be used for gene expression and proteomics studies while both short and long 

exposures will be used to measure the physiologic, biochemical and reproductive endpoints. 

These data will be brought together in a predictive computational model for the action of 

environmental estrogens. The model will then be tested with an exposure of fathead minnows to 

zearalenone (estrogen mimic and positive testor) and trenbolone (nonaromatizable androgen and 

negative testor), compounds that are used in the cattle industry. 

Expected Results: We expect to develop a computational model and identify 10-15 molecular 

and protein biomarkers that are specific and predictive of adverse effects of exposure to 

estrogenic compounds in reproduction of fathead minnows. This quantitative model will help 

improve risk assessment of exposure of wildlife and, by extrapolation, of mammals to endocrine 

disrupting compounds. 

Specific Aims: 

Specific Aim 1: To determine and compare gene and protein expression profiles and 

physiological and reproductive endpoints for adult FHM exposed to a model estrogen 17 alphaethinylestradiol (EE2), androgen (17β-trenbolone), or their antagonists (ZM 189,154 and 

flutamide, respectively). 
Specific Aim 2: To predict gene expression patterns of two compounds (zearalenone and EE2) 

that are environmental estrogens.

Specific Aim 3: To develop a computational modeling framework that integrates exposure 

concentration, gene expression, and proteomic profiles with physiological endpoints. 

Progress Summary: Endocrine disrupting compounds (EDCs) can target the HPG axis at 

different levels of complexity. These levels span from the molecular level to the whole 

organism. Our work is centered on understanding how changes at the molecular and protein 

levels affect downstream physiological endpoints and how well these biomarkers predict adverse 

effects on reproduction. There are many published studies that have shown a direct impact of 

EDCs on the reproductive success of fish and wildlife. These compounds include certain 

organochlorine pesticides, industrial compounds, pharmaceuticals, plasticizers, surfactants, and 

metals. These compounds have been shown to alter fertility, fecundity, egg hatchability, survival 

of young, sex ratio, and other reproductive parameters. Although these endpoints have high 

ecological value, they do not point to specific mechanisms of action. We are pursuing a 

“Systems Toxicology” approach in which we apply molecular tools to understand global 

mechanisms of action that are affected by chemical exposure. We will relate these changes to 

reproductive endpoints using a novel physiology-based mathematical model. 

In Year 1 of the project, significant progress was made in the formulation of a physiologically 

based model of the HPG axis in male FHM. Following the suggestions of our proposal 

reviewers, a modular approach to model development is being taken so that useful submodels 

will be developed in the process of constructing an integrative model representing multiple 

scales of biological organization (from molecular-level gene expression to physiological-level 

reproductive effects). The first submodel to be developed is a physiologically based 

pharmacokinetic (PBPK) model that simulates the disposition of EE2 in male FHM. 

Milestones/Products: 

FY06

 To determine and compare gene and protein expression profiles and physiological and 

reproductive endpoints for adult FHM exposed to a model estrogen 17 alpha-ethinylestradiol 

(EE2), androgen (17β-trenbolone), or their antagonists (ZM 189,154 and flutamide, 

respectively). 

FY07

 To predict gene expression patterns of two compounds (zearalenone and EE2) that are 

environmental estrogens. 

FY08

 To develop a computational modeling framework that integrates exposure concentration, 

gene expression, and proteomic profiles with physiological endpoints. 

QA: QA Plan is being evaluated 
IA-3 

Title: Chemically Induced Changes in Gene Expression Patterns along the HPG Axis at 

Different Organizational Levels Using a Small Animal Model (Japanese Medaka) 

Lead: John Giesy, Michigan State University 

Research Issue and Relevance: The EPA is interested in the application of novel technologies, 

derived from computational chemistry, molecular biology and systems biology, in toxicological 

risk assessment. In assessing risk associated with exposure to a chemical or other environmental 

stressor, a number of scientific uncertainties exist along a “source-to-adverse outcome” 

continuum, beginning with the presence of the chemical in the environment, the uptake and 

distribution of the chemical in the organism or environment, the presence of the active chemical 

at a systemic target site, and the series of biological events that lead to the manifestation of an 

adverse outcome that can be used for risk assessment. "Endocrine-disrupting" compounds have 

been defined as exogenous agents that interfere with the "synthesis, secretion, transport, binding, 

action, or elimination of natural hormones in the body that are responsible for the maintenance of 

homeostasis, reproduction, development, and/or behavior". Much of the recent concern and 

energy has been focused on compounds that are hormone direct agonists or antagonists, 

especially those that interact with the estrogen receptor (ER). Effects consistent with exposure to 

ER agonists have been observed in fish exposed to natural hormones and some synthetic 

chemicals such as nonylphenol (NP), nonylphenol polyethoxylates (NPEs), and octylphenol 

(OP). Because chemicals can cause both direct (receptor-mediated) and indirect effects through 

changes in signal transduction pathways, methods are needed that permit the screening of 

multiple effects. Furthermore, methods are needed that can screen for these effects 

simultaneously in a number of tissues, including during critical windows of development, when 

tissues may be small and the amount of material available for testing is small and difficult to 

remove from the organism. We propose to develop a screening method to use molecular 

techniques such as in situ hybridization, in situ RT-PCR and immuno-histochemical staining 

(IHCS) to screen for effects of chemicals on the hypothalamic-pituitary-gonadal (HPG) axis with 

a special emphasis on steroidogenic pathways and hormonal control mechanisms along the HPGaxis in the Japanese medaka. The proposed method will allow for screening of multiple effects in 

multiple tissues, even at points in development when the tissues are too small to be accurately 

dissected for use in more traditional molecular techniques. The proposed methods will be applied 

to a set of "model" and "test" compounds for a set of target genes. Once the methods have been 

developed and validated, they can be adapted for use with other genes and/or species of interest 

and used to efficiently and completely screen for endocrine disruptor effects beyond simple 

receptor binding. 

Approach: To investigate the tissue-specific expression of genes in the most efficient manner a 

variety of in situ molecular techniques will be used to visualize and quantify mRNA and/or gene 

products. To maximize sensitivity and permit multiplexed gene expression quantification 

methods will be based on in situ hybridization and a variety of in situ PCR techniques. The aim 

of these investigations is to produce the simplest and most 'transportable' techniques so the use of 

a common technique for all genes investigated will be a priority for the final protocol. 
Expected Results: The proposed research program will establish an integrative and quantitative 

small fish model to identify and evaluate the modes of action, key target sites, and biological 

relevance of EDCs acting along the HPG-axis. To achieve this we will apply state-of-the-art 

molecular techniques in a whole animal systems approach (see approach section). The studies 

will be conducted in two phases. In the first phase normal physiological processes along the 

HPG-axis will be described in order to establish a natural basis for the evaluation of EDC effects. 

Information on the natural variability, reproductive cycling, diurnal pattern, needs, etc. will be 

used to optimize the proposed test system. Thus, we will establish a general model system that 

can be used in the assessment of single chemical and complex mixture effects on reproductive 

endocrinology. In the second phase we will use this system to detect and assess changes in 

tissue-specific gene expression patterns (GEPs) caused by the exposure to endocrine active 

"model" compounds, and their mixtures. We will identify key genes for each individual exposure 

scenario and establish a quantitative RT-PCR system as a rapid screening tool for these genes. 

A model will be developed to predict the biological relevance of the observed changes in GEPs. 

By identifying the systemic target sites, and the series of biological events from gene expression 

to the manifestation of an adverse outcome (e.g., reproductive performance), we will identify 

thresholds at the molecular level that are indicative of effects on the fitness of the individual, 

including survival, growth and reproduction (fertility and fecundity as well as survival of the 

offspring). The proposed model test system will provide a new and powerful approach in hazard 

identification and toxicological risk assessment. Understanding the potential of individual 

chemicals and complex environmental mixtures to interfere with molecular pathways of concern 

will enhance our understanding of the basic mechanisms of toxicities, and thus, will have the 

potential to develop better focused, more rapid and cost effective models for quantitative risk 

assessment. The bioassay can be used to screen for a wider range of endocrine disruptor effects. 

Progress Summary/Accomplishments: A subset of genes has been selected for the development 

of whole fish in situ hybridization methods. Beta-actin was selected as the housekeeping gene, and 

two isoforms of the aromatase gene, CYP19A and B, were selected as initial target genes due to 

the fact that their expression is highly tissue specific and responsive to endocrine disruptors. 

cDNA and riboprobes have been successfully developed for these genes and currently are being 

tested and optimized using cryosectioned tissues and parafin-embedded sections for whole fish 

mounts. The in situ hybridization procedures are being optimized in the laboratory. The 

methods have demonstrated a very high level of staining for all probes tested. Although some of 

the staining seems to be of nonspecific background, the sense and antisense probes do exhibit the 

expected binding profiles. Certain factors that may decrease the nonspecific binding are in the 

process of being tested, including high stringency posthybridization washes, high temperature 

hybridization washes, and high temperature posthybridization washes. At this point, the probes 

in use are digoxygenin-labeled. They are visualized using standard protocols. 

Milestones/Products: 

FY06

 NCER needs to provide 

FY07 

QA: QA Plan is being evaluated 
IA-4 

Title: A Systems Approach to Characterizing and Predicting Thyroid Toxicity Using an 

Amphibian Model. 

Lead: Sigmund J. Degitz, MED/NHEERL 

Research Issue and Relevance: The EPA was recently mandated to evaluate the potential 

effects of chemicals on endocrine function and has identified Xenopus as a model organism to 

use as the basis for a thyroid disruption screening assay. The main objective of this work is to 

develop a hypothalamic-pituitary-thyroid (HPT) model for this bioassay. This model will provide 

a rational framework to organize and interpret toxicological data from the molecular to the 

organismal levels and will serve as a basis for development of predictive tools related to thyroid 

toxicity. Recent developments in understanding the molecular events involved in TH 

homeostasis and action suggest that thyroid toxicity might be identifiable using appropriate 

molecular endpoints in an abbreviated test. This potential improvement, which will lower costs 

and reduce animal use, is currently more likely to succeed due to expanding genomic 

information. This project will focus on building linkages between early molecular events 

associated with exposure and organismal-level effects. Understanding these linkages and their 

relative importance will be assessed using the HPT model. One of the major goals of this work 

is to populate the HPT model with data specifically at the molecular and biochemical levels 

which will provide a basis to interpret interspecies homology and comparative toxicity. 

Approach: Development of this assay is progressing, but its widespread use on Agency 

chemical inventories will be limited due to limited resources. As a consequence, a strategy to 

objectively rank and prioritize the order of chemical testing needs to be developed. One of the 

most likely uses for a HPT systems model is to aid in the understanding and discrimination of 

different toxic modes of action. As such, these models further enable the development of 

quantitative structure activity relationships (QSARs) by providing a basis for sorting chemicals 

by mode of action, a necessary step prior to quantifying features of chemical structure associated 

with a particular type of toxicity. If these relationships can ultimately be established, then 

predictive models can be developed to rank chemicals for future in vivo testing. In vivo testing 

for HPT effects will be improved through this research by providing a basis to link early 

molecular events to organismal outcomes. 

There are four specific aims for this research. The first is to develop an HPT systems model which is 

capable of integrating data from different levels of biological organization, molecular to organismal, 

into a coherent system. The second specific aim is to develop an understanding of the compensatory 

mechanisms at the genomic level involved in TH homeostasis and how they respond to chemical 

perturbation. The third specific aim is to use in vitro models to help define the functions of 

component systems of the HPT system. Both pituitary and thyroid gland cultures will be used as 

experimental approaches to define tissue-specific outputs in response to endogenous and xenobiotic 

chemical inputs. The fourth specific aim is to use the emerging knowledge of the HPT system to 

develop usable, predictive toxicological tools. Once the input-output relationships are developed for 

the thyroid glands in culture, this work will be extended to specifically address chemicals known to 

inhibit T4 synthesis via disruption of iodine uptake by the sodium/iodide symporter (NIS). 

1
Progress to Date: This is a New Start in the Computational Toxicology Research Program that 

began in January 2005. Past efforts have been focused on the development of a thyroid screening 

assay for OSCP. This assay, which is based primarily on whole organism responses, is proposed to 

last 14 - 21 days beginning in late pre-metamorphosis or early pro-metamorphosis using the 

amphibian, X. laevis. We have worked with three T4 synthesis inhibitors (6-propylthiouracil (PTU), 

perchlorate (PERC), and methimazole (METH)), two receptor agonists (T4 and T3), and a deiodinase 

inhibitor (iopanoic acid (IOP)) in an effort to test the responsiveness of this model system to 

chemicals with different modes of action. We have developed dose-response relationships for each of 

these chemicals. Interestingly, we have found that in the case of the T4 synthesis inhibitors, clear 

signs of thyroid system disruption, as evidenced by thyroid follicular cell hypertrophy and 

hyperplasia, occurs at concentrations below those which effect developmental rate. This observation 

suggests that the HPT executed a successful compensatory response that permitted normal 

development (Tietge et al, 2004; Degitz et al, 2004). More extreme glandular changes were observed 

at higher chemical concentrations accompanied by delayed development. 

We have initiated studies to examine the changes in molecular and biochemical endpoints 

specific to components of the HPT system. As a prerequisite to understanding the changes associated 

with exposure to TH synthesis inhibitors, we have been determining the expression patterns of 

several genes throughout normal metamorphosis. TSH and type II deiodinase gene expression 

patterns in the pituitary, for example, have been developed and demonstrate patterns of up-regulation 

coincident with initiation of metamorphic climax These patterns of gene expression are critically 

important as they establish the baseline conditions against which changes induced by chemical 

exposure will be compared and interpreted. 

Impact: The primary benefit of this work is to develop a sufficient understanding of the HPT so 

that predictive models can be developed, testing protocols can be abbreviated, and efforts in 

inter-species extrapolation can be improved. 

Partnerships/Collaborations: This work in being done in conjunction with systems biology 

modeler at the PNNL. 

Milestones/Products:

FY06

 Establish data management system 

 Formulate initial systems model for thyroid function 

 Develop in vitro thyroid and pituitary culture systems 

FY07

 Characterize molecular changes associated with TH inhibition 

 Characterize specific molecular responses in thyroid gland culture 

 Develop initial QSAR for NIS inhibition 

FY08

 Refine systems model for relating MOA to outcome 

 Complete first round QSAR hypothesis for testing for NIS 

QA: 

QA Plan is being evaluated 

2
IA-5 

Title: Estrogen Elicited Gene Expression Network Elucidation in the Rat Uterus 

Lead: Tim Zacharewski, Michigan State University 

Research Issue and Relevance: The EPA is interested in the application of novel technologies, 

derived from computational chemistry, molecular biology and systems biology, in toxicological 

risk assessment. In assessing risk associated with exposure to a chemical or other environmental 

stressor, a number of scientific uncertainties exist along a “source-to-adverse outcome” 

continuum, beginning with the presence of the chemical in the environment, the uptake and 

distribution of the chemical in the organism or environment, the presence of the active chemical 

at a systemic target site, and the series of biological events that lead to the manifestation of an 

adverse outcome that can be used for risk assessment. Systems biology involves the iterative 

development of strategies that integrate disparate physiological and biochemical data into 

computational models that are capable of predicting the biology of a cell or organism. In order to 

facilitate hazard identification and risk assessment, a comprehensive quantitative understanding 

of the molecular, cellular, physiological, and toxicological effects that are elicited following 

acute and chronic exposure to synthetic and natural chemicals is required within the context of 

the whole organism. The objective of this proposal is to develop a computational model that will 

identify and predict critical estrogenic endocrine disruptor elicited changes in gene expression 

which play a central role in the observed physiological/toxic effects based on systematic and 

quantitative data obtained from comparative in silico, genomic, molecular and histopathological 

approaches. 

Approach: Gene expression and histopathological changes elicited by ethynyl estradiol, 

genistein, bisphenol A, o,p'-DDT will be assessed in ovariectomized immature female SpragueDawley rats. Dose-and time-dependent gene expression changes will be determined using 

customized sequence-verified cDNA rat arrays enriched with estrogen responsive genes. 

Significant changes in expression will be identified and weighed using Bayes and t-statistic 

approaches, and verified by quantitative real-time PCR (QRTPCR), western analysis, in situ

hybridization and/or immunohistochemistry. Chromatin immunoprecipitation (ChIP) assays will 

further elucidate the estrogen receptor (ER)-mediated mechanisms of action and causal 

relationships between genes. In addition, histopathological assessments will be conducted to 

distinguish adaptive and toxic responses using various computational methods including 

canonical correlation, and Fisher discriminate analyses. Genetic algorithm (GA)/partial least 

squares (PLS) analysis will then be used to integrate this disparate data into a model that can 

identify the most relevant genes associated with a histopathological outcome. All data will be 

captured in dbZach (http://dbzach.fst.msu.edu), a MIAME-compliant toxicogenomic supportive 

database that facilitates data analysis, integration of disparate data, and sharing with other 

investigators. 

Expected Results: The models developed will identify gene expression changes most highly 

associated with EED elicited histopathological uterine responses. Examination of multiple EEDs 

with varying potencies will also identify key regulatory nodes responsible for eliciting these 

responses, which could lead to the development of high throughput endocrine disruptor 

screening assays for chemicals in commerce. The data and resulting models can also be 
integrated with other algorithms (i.e. PBPK) to create a more comprehensive model of the 

hypothalamic-pituitary-gonadal axis. 

Milestones/Products: 

FY06

 NCER to provide 

FY07

 To predict gene expression patterns of two compounds (zearalenone and EE2) that are 

environmental estrogens. 

QA: QA Plan is being evaluated 
IA-6 

Title: Risk Assessment of the Inflammogenic and mutagenic effects of diesel exhaust particles: 

A systems biology. 

Lead: James Samet, NHEERL 

Research Issue and Relevance: Diesel exhaust particulate matter (DEP) is a ubiquitous 

ambient air contaminant derived from mobile and stationary diesel fuel combustion. Exposure to 

DEP is associated with carcinogenic and immunotoxic effects in humans and experimental 

animals. At the cellular level, these health effects are underlain by genotoxic and inflammatory 

properties of chemical compounds present in DEP. DEP is composed of elemental, inorganic and 

organic compounds that vary widely in composition with the source of the fuel, engine operating 

conditions, sampling methods and other parameters. The genotoxic and inflammatory potencies 

of DEP also vary with its physicochemical properties, and these differences along with multiple 

health effects impede the development of targeted regulatory strategies for mitigating the impact 

of DEP exposure on human health. While traditional reductive toxicology approaches are not 

likely to succeed in quantifying relationships between DEP composition and its numerous health 

effects, generating a database for modeling the toxicological effects of DEP would provide a 

framework for quantitative hazard identification. This project proposes a systems approach to 

developing and applying predictive computational models that quantitatively describe 

relationships between the composition of DEP and its genotoxic and inflammogenic potencies. 

 

Approach: The objectives of this project will be met by conducting research in three phases. In 

phase 1 (Specific Aim 1), 16 distinct DEP will be generated using a combination of fuels, engine 

types, engine loads and collection temperatures. These DEP will then be characterized through 

extensive chemical and physical analyses. In phase 2 (Specific Aims 2 and 3), the inflammogenic 

and genotoxic potencies of each of the 16 DEP will be determined quantitatively. Specific 

bioassays will measure the expression of the pivotal inflammatory mediator IL-8/MIP-2 in 

cultured human and mouse lung cells in response to DEP exposure. Signaling mechanisms that 

regulate the expression of IL-8/MIP-2 in response to DEP exposure will also be examined in 

order to provide mechanistic insight and support for the models. The genotoxicity of the 16 DEP 

will be assayed using bacterial mutagenicity assays. Human, mouse and bacterial gene 

expression arrays will be used to provide additional mechanistic insights on patterns of gene 

expression induced by DEP. Phase 3 (Specific Aim 4) will utilize the generated data to construct 

a series of statistical and mathematical models that quantitatively relate DEP composition, its 

inflammogenic and mutagenic effects and the relevant intracellular signaling mechanisms. 

Progress to Date: See below

Impact: The information generated by this multidisciplinary research program is intended for 

use in risk assessment aimed at mitigating the health effects of DEP exposure, including 

quantitative and computational approaches, cross-species extrapolation and endpoint validation. 

This proposal is directly responsive to priority research needs identified by NCEA, Office of Air 

and Radiation (OAR) as well as the ORD PM Research Program. The research will be conducted 

using a custom-designed diesel emission sampling system, leading edge genomics and 

1
proteomics technologies and the latest tools in bioinformatics and computational modeling 

software. Beyond providing biological plausibility in support of DEP regulations, the usefulness 

of the findings from these studies will hinge upon identifying toxicological effects mechanisms 

in the context of DEP characteristics. Thus, this project’s primary aim is to deliver a set of 

predictive models that quantitatively describe the relationship between DEP composition and its 

genotoxic and inflammogenic properties. An important impact of the process of developing and 

applying this set of models via the systems biology approach will be the generation of novel 

mechanistic data with the specific intent of identifying and characterizing critical paths that lead 

from DEP exposure to toxicity. It is anticipated that many of these data will be applicable to the 

study of other sources of particulate air pollution whose effects include mutagenisis or 

inflammatory responses. Moreover, application of the systems biology approach will represent a 

case study adaptable to computational studies of exposure and toxicological effects of a broad 

range of environmental agents. Although the reductive studies will also yield novel mechanistic 

information about DEP toxicity, the actual deliverable product of this research will be a series of 

computational models that can be used by client offices in support of assessment and regulatory 

efforts. These models will have particular practical application in offices responsible for DEP 

assessment and regulatory programs including the Office of Air and Radiation (OAR) and the 

National Center for Environmental Assessment (NCEA). 

Partnerships/Collaborations: The NRMRL high bay facility is designed for combustion 

research. Drs Linak and Gilmour have retrofitted a combustion bay complete with dilution tunnel 

and baghouse collection units. In addition, the diesel exhaust may be piped to inhalation 

exposure chambers. The facility is equipped with a computer-integrated gas and particlemonitoring bench. The NRMRL research laboratories and the Environmental Carcinogenesis 

Division also operate core organic chemistry laboratories for routine solvent extraction and GCMS capability. The laboratories of Drs Samet, Reed and Gilmour are equipped with cell culture 

areas, nucleic acid extraction and measurement instruments, ELISA, western blot and real time 

PCR capability. Dr DeMarini’s laboratories have capabilities for high throughput screening of 

bacterial mutagenicity assays using a number of Salmonella Sp substrains, and through other 

members of ECD has access to methodologies for eukaryotic DNA damage and repair testing. 

Additional core support for genomics and bioinformatics will be available to all investigators 

through the NHEERL genomics and proteomics Committee (NGPC). As a faculty member of the 

University of North Carolina at Chapel Hill (UNC-CH), Dr. Reed has access to the resources of 

the UNC-CH Center for Bioinformatics and the UNC Shared Bioinformatics Resource that 

provide access to software packages for the analysis of gene expression profiling data, including 

GeneSpring (Silicon Genetics), PathArt (Jubilant Biosys) and GenoMax (InforMax). 

 

Milestones/Products: 

FY06

 Generate the first four DEP samples 

 Conduct chemical characterization of the DEP samples 

Conduct a pilot study assessing the inflammogenicity and signal transduction profile of 

two DEP samples in human and rodent airway epithelial cells. 

FY07

2
 Generate eight DEP samples 

Identify overlaps in human and mouse gene expression patterns associated with DEP 

inflammogenicity or mutagenicity 

Perform a bioassay-directed fractionation on the available DEPs and determine the 

distribution of mass and mutagenicity among the fractions. 

FY08

 Generate remaining four DEP samples 

Define signal transduction pathways involved in inflammogenic and mutagenic responses 

to DEP exposure based on gene expression patterns 

 Complete biological models of relevant signaling pathways 

QA: 

QA Plan is being evaluated 

3
IA-7 

Title: Mechanistic Indicators of Childhood Asthma (MICA) 

Lead: Jane Gallagher NHEERL; Haluk Ozkaynak NERL 

Research Issue and Relevance: The US Environmental Protection Agency (EPA) is interested 

in the interplay of environmental and genetic factors on the development and exacerbation of 

asthma. The Mechanistic Indicators of Childhood Asthma (MICA) study will use exposure 

measurements and markers of environmental exposure and health effects. Based on 

epidemiological studies of air pollution and asthma, there is sufficient evidence to justify 

investigations that incorporate state-of-the art technologies including genomics and proteomics, 

to study how and which genes and environmental factors interact in a way that increases the risk 

of worsening asthmatic responses. EPA scientists will use collected markers of exposure and 

effects to analyze, characterize, and possibly quantify combined risk factors that relate to asthma 

severity from multiple agents/stressors. Our study will also provide information on some key 

molecular events associated with chemical exposures, giving risk assessors more reliable data to 

assist in defining exposure-response relationships and in making estimates on the range of risks 

expected in the population compared to data based on biological monitoring and/or screening 

level hazard data. 

Approach: Phase 1 of MICA includes an assessment gene expression data collected from 

rodent blood and respiratory tissues RNA produced by short-term controlled exposures to 

concentrated particulate matter. Mobile units housed with rodent exposure chambers (State 

University) were employed in Grand Rapids, Michigan and in the Detroit Metropolitan Area. 

Phase 1 will provide 1) information on the reliability of surrogate blood RNA samples to predict 

target tissues effects and 2) context for the human gene expression data, collected in phase 2 

planned for the summer of 2006. Phase 2 is a children exposure assessment/biomarker study 

focusing on three broad classes of particulate associated chemicals: volatile organic compounds 

(VOCs), metals, and polycyclic aromatic hydrocarbons (PAHs). MICA will study 150 asthmatic 

and 50 non-asthmatic children. Blood and urinary measures of these chemicals will be compared 

to benchmark levels of these chemicals and metabolites from the Center for Disease Control and 

Prevention’s (CDC) National Exposure Report. MICA consists of clinical measurements 

including (a) a skin prick test for allergen sensitivity; (b) analysis of blood, urine, nail clippings, 

and DNA; (c) immunological markers, odor testing, lung function and breath analysis; (d) gene 

expression and protein tests, viewed in the context of environmental assessments and respiratory 

health history. 

Progress to Date: New start Jan 2005. Rodent exposures completed. Blood RNA isolated from 

two species and two sites in Michigan. Isolation of RNA from matching rodent lung samples 

ongoing. Collaborations and/or contracts for over 30 exposure and health effects markers have 

been identified. 

Impact: 1). MICA provides linkages in the source-to-outcome paradigm 

2). Enhancement of quantitative risk assessment, produce better methods and predictive models 

for quantitative risk assessment and to provide a useful tool for large-scale biomonitoring in 

1
humans. 

3). Better determination of the shape of the exposure-response curve, especially in the lowexposure region, through the incorporation of gene expression data into experimental systems. 

4). The development of more accurate, biologically-based mathematical exposure-response 

models that predict responses outside the range of experimental values. 

 5)The identification of regulatory metabolic or physiologic pathways, that may act in concert 

and lead to adverse health outcomes, through the evaluation of multiple health end points with 

linkages to gene expression changes. •Identification of molecular indicators of exposure and 

toxicity that can be applied to other epidemiological studies or risk assessment. 

 More specifically the Office of Air and Radiation (Office of Indoor Air, Office of Air Quality 

Standards and Planning and the Office of Transportation and Air Quality) and the Office of 

Environmental Information. Our study will provide information on some key molecular events 

associated with chemical exposures, giving risk assessors more reliable data to assist in defining 

exposure-response relationships and in making estimates on the range of risks expected in the 

population compared to data based on biological monitoring and/or screening level hazard data. 

Research will support the Government Performance and Results Act (GPRA) goal "Risk 

Assessment for Susceptible Subpopulations", Long-Term Goal 4 of the Human Health MultiYear Plan (MYP), Objective 4.4 Science/Research, Sub-Objective 4.4.2 Research. More 

specifically, the proposed research would be associated with Annual Performance Goal (APG) 

#6: "By 2012, provide risk assessors and managers with methods and tools for measuring 

exposure and predicting effects in children, including adolescents, characterizing cancer and 

non-cancer hazards and risk to children, and reducing risks to children in schools from harmful 

environmental agents to support EPA risk assessment and risk management" 

Partnerships/Collaborations: (e.g., where is the data coming from?). This is a multidisciplinary proposal requiring the expertise of epidemiologists, modelers, toxicologists, and risk 

assessors, and the knowledge of computational data analysis and systems biology from NERL 

and NHEERL investigators, EPA Region 5 and potentially Detroit’s City Health Department 

(Department of Health and Wellness Promotion (a recipient of a CDC grant to conduct asthma 

education, training and outreach activities). It is expected the DCHD or other recognized 

community leaders will have a role in effectively educate parents of the enrolled children related 

to potential asthma triggers and how best to communicate health and research data back to the 

community participants. 

Milestones/Products: 

FY05 

• Collect bloods and isolate RNA from rodent lung and blood samples and blood 

• Develop QA plan for gene expression analysis 

• Develop and seek approval for Intramural Research Protocol including recommended and 

standard operating protocols for collecting pilot study gene expression analysis data. 

FY06 

• Develop relevant educational modules appropriate for schoolchildren and their parents to 

2
enhance participant recruitment and ensure their informed consent. 

• Develop asthma severity score criteria based on asthma diagnosis, medicine and FEV1 in 

school-based questionnaires and lung function measurements for selection of schoolchildren. 

• Develop standard operating procedures for the collection, processing, and analysis of 

biological samples from children. 

• Complete monitoring of ambient exposure levels indoor/outdoor 

FY07

● Develop plan for collection and analysis of biological samples. 

• Collect, prepare process and analyze human biological samples from 200 children. 

• Prepare comprehensive data base. 

FY08 

• Complete a data base of all biological measurements and analytic results and the transfer of 

this data to modelers and risk assessors for the preparation of manuscripts. 

• Develop exposure models based on ambient exposure data, geographic information (locations 

of roadways and major sources of PAHs and metals), and home /school locations. 

• Compare exposure model estimates with biomarkers of exposure and early effect. 

QA: 

QA Plan is being evaluated 

3
1

IIB-1 

Title: Integrated Chemical Information Technologies Applied to Toxicology 

Lead: Ann Richard, NCCT (FTE .0.8)

Research Issue and Relevance: A central regulatory mandate of the Environmental Protection 

Agency, spanning many Program Offices and issues, is to assess the potential health and 

environmental risks of large numbers of chemicals released into the environment, often in the 

absence of relevant test data. Models for predicting potential adverse effects of chemicals based 

primarily on chemical structure play a central role in prioritization and screening strategies yet 

are highly dependent and conditional upon the data used for developing such models. Hence, 

limits on data quantity, quality, and availability are considered by many to be the largest hurdles 

to improving prediction models in diverse areas of toxicology. Generation of new toxicity data 

for additional chemicals and endpoints, development of new high-throughput, mechanistically 

relevant bioassays, and increased generation of genomics and proteomics data that can clarify 

relevant mechanisms will all play important roles in improving future SAR prediction models. 

The potential for much greater immediate gains, across large domains of chemical and toxicity 

space, comes from maximizing the ability to mine and model useful information from existing 

toxicity data, data that represent huge past investment in research and testing expenditures. In 

addition, the ability to place newer “omics” data, data that potentially span many possible 

domains of toxicological effects, in the broader context of historical data is the means for 

optimizing the value of these new data. 

The challenges for application of information technologies, including chem-informatics and 

bioinformatics, are fourfold: 1) to more efficiently migrate legacy toxicity data from diverse 

sources into standardized, electronic, open, and searchable forms into the public domain; 2) to 

employ new technologies to mine existing data for coherent patterns that can provide scientific 

underpinning for extrapolations; 3) to place a new chemical, of unknown hazard, appropriately in 

the context of existing data and chemical and biological understanding; and 4) to integrate data 

from different domains of toxicology and newer “omics” experiments to look beyond traditional 

means for classifying chemicals, inferring modes of action, and predicting potential adverse 

effects. 

Approach: In the area of improving data resources for structure-based mining, the NCCT is 

supporting further development and expansion of the DSSTox (Distributed Structure-Searchable 

Toxicity) database network. The DSSTox project is primarily focused on migrating toxicity data 

from diverse areas of study into structure-annotated, standardized form for use in relational 

structure-based searching and structure-activity model development. An essential element of this 

effort involves bridging understanding and forging productive linkages between the toxicology 

domain experts and the data users and modelers by means of focus on clarifying the chemistry 

content and summary presentation of the toxicology data. The larger goal of these efforts is to, 

in effect, overcome inherent and limiting data constraints in focused domains of toxicological 

study (e.g., cancer, developmental toxicity, neurotoxicity, etc) by expanding the searchable and 

mine-able data network across both chemical and biological domains. 
2

As an extension of the DSSTox project, NCCT researchers are promoting adoption of 

standardized chemical structure data fields for public toxicogenomics datasets to enable broader 

searchability across these data domains, and to enable integration of these datasets with legacy 

toxicity data and other public data. In particular, collaboration of the DSSTox project with the 

NIEHS Chemical Effects in Biological Systems (CEBS) project is working towards 

incorporation of DSSTox data fields and providing structure-searching capability and linkages 

across CEBS data and public genomics data, as well as DSSTox and National Toxicology 

Program legacy toxicity databases. Chemical structure and genomic expression patterns provide 

common metrics for exploring diverse toxicological effects, and can provide the basis for 

development of predictive patterns or signatures of a toxicological effect. Similarly, biological 

activity profiles consisting of experimentally determined, or computationally predicted 

interaction spectra (receptors, proteins, enzymes) could be viewed as expanded “properties” of 

the chemical and could augment structure-based information for enhancing toxicity classification 

and prediction algorithms. 

Finally, NCCT researchers are taking a lead in efforts to address more fundamental and essential 

needs to migrate older paper legacy data (such as within EPA Program Offices such as OPP and 

OPPT) into electronic form suitable for incorporation into standardized, searchable relational 

databases. New commercial technologies from IBM, SciTegic and others that allow for more 

automated structure-annotation, and chemical indexing and retrieval procedures are being 

evaluated to facilitate efficient electronic conversion and structured content-annotation of legacy 

EPA data. In addition, related issues of quality control of chemical information are being 

addressed, and Agency-wide chemical structure-browser capabilities are being explored. 

Progress to date: 

DSSTox Project Status 

The EPA DSSTox website (http://www.epa.gov/nheerl/dsstox/), launched in March 2004, 

provides detailed information on DSSTox standard chemical fields, guidance for creating new 

DSSTox databases, and links to a wide range of public information resources. A major emphasis 

of the DSSTox project is on creating field-delimited, content-enhanced data files for diverse 

toxicity endpoints. Five DSSTox databases are currently published on the website and several 

others are in progress or currently undergoing review. Toxicity endpoints considered include: 

rodent carcinogenicity, mutagenicity, estrogen receptor binding affinity, fish acute toxicity, and 

pharmaceutical maximum adverse effect dose levels. Additional toxicity endpoints slated for 

DSSTox database publication include: skin sensitization, acute toxicity, nasal and eye irritation, 

androgen receptor binding, rodent developmental toxicity, DNA intercalation, pesticide 

ecotoxicity, and immunotoxicity. A large emphasis has been placed on the quality review of 

chemical information, which has led to the creation of a central DSSTox Master chemical 

structure reference data file and detailed quality data review procedures. 

CEBS DSSTox Project Status 

The DSSTox project is collaborating with the NIEHS National Toxicogenomics Center CEBS 

Knowledge-Base project firstly by the incorporation of DSSTox standard chemical fields into the 

data dictionary and CEBS data entry system. DSSTox Standard Chemical Fields (SCFs) have 

recently been revised to better handle the diverse chemical content of public toxicity databases, 

which include all variety of mixtures and less well-defined substances, and to better coordinate 
3

with other public data standards efforts, such as the ToxML public toxicity data schema project. 

These DSSTox SCFs will be additionally employed to index the largest public genomics 

databases, to provide expanded structure-searchability within and outside CEBS data content. A 

chemical inventory of these databases has begun and will be followed by attempts to encourage 

external public data standards organizations (e.g., MGED) and database sources (ArrayExpress, 

NCBI) to adopt more rigorous chemical structure annotations of genomics data. Coordination 

with other large public database efforts, such as the National Library of Medicine’s PubChem, 

NIH Molecular Libraries Initiative, and NIH National Cancer Institute molecular database 

projects, will also directly impact on the CEBS DSSTox project collaboration. 

EPA Communities of Practice – Chemoinformatics Workgroup Status 

As part of the effort to improve our ability to index, search and link chemical information data 

files across EPA Labs, Centers, and Program Offices, NCCT Researchers have formed a 

“Communities of Practice” Chemo-informatics Workgroup to begin to forge Agency-wide 

collaborations and coordination with respect to improving treatment and utility of chemical 

structure-related information within EPA data files. Additionally, the National Computer 

Center’s Scientific Visualization group is evaluating possible solutions for providing Agency 

wide structure browsing capability. 

Impact: NCCT researchers are involved in efforts that are poised to dramatically improve 

capabilities to access, mine, and integrate useful chemical-biological activity information from 

existing and new data, both within and outside EPA. These efforts have the potential to impact a 

wide variety of EPA program offices that heavily rely on chemical information resources, have 

large internal stores of data, and have a need for structure-based data exploration, analog 

searching, and improved toxicity prediction models. These include many programs within 

OPPTS [e.g., Green Chemistry, Premanufacture-Notification Program (PMN), Office of 

Pesticide Programs (OPP), High-Production Volume (HPV) Testing Program] as well as EPA’s 

Integrated Risk Information System (IRIS) Program, Office of Water, and Office of 

Environmental Information. New information technologies that incorporate more flexible and 

diverse means for assessing of biological and chemical similarity will also improve the 

identification of toxicologically relevant analogs by enhancing the ability to explore data and 

quantify associations in diverse chemical and biological domains. 

 

Partnerships/Collaborations: 

The DSSTox public toxicity database effort is being coordinated and linked with a number of 

other public efforts involving data-mining companies (e.g., LeadScope), non-profits 

(International Life Sciences Institute – ILSI) and LHASA, UK (VITIC SAR Toxicity Database 

Project), and government research laboratories (NIEHS, FDA) that are promoting controlled 

toxicity vocabularies, adopting data standards, and migrating diverse toxicity data into the public 

domain. The CEBS-DSSTox collaboration more broadly includes collaborations with FDA’s 

National Center for Toxicological Research, for adoption and use of a chemical structure 

browser, and the NIEHS National Toxicology Program (NTP), for incorporation and structurelinkage to legacy toxicity data in the NTP data files. Coordination of chemical data information 

standards that will greatly facilitate cross-platform structure-searching is also ongoing with the 

National Institutes of Standards and Technology and International Union of Pure and Applied 
4

Chemists (NIST-IUPAC), as well as the National Cancer Institute’s Structure-Browser, National 

Library of Medicine (ChemID) and PubChem. Additionally, dialog has begun with the NIEHS 

NTP and the NIH Molecular Libraries Initiative to coordinate efforts to use chemoinformatic 

tools and DSSTox project capabilities to propose sets of chemicals for high-throughput screening 

testing based on chemical structure-activity considerations. 

Through current efforts and the enlarged Communities of Practice workgroup, partnerships and 

collaborations with scientists across EPA are being forged to better improve chemoinformatic 

capabilities across the Agency. Collaborations are on-going with MED-Duluth and the 

ASTER/AQUIRE system, and the IRIS project, and are being continued or initiated with 

scientists in OPP, OPPT, and OEI to inventory, process, review, and integrate data across various 

Agency Programs from a unified chemical structure perspective. 

Milestones/Products: 

FY05

• Incorporate DSSTox standard chemical fields into CEBS. 

• Create and publish DSSTox database and documentation files for NTP Immunotox database; 

publish a chemical index file for the EPA IRIS programs. 

• Establish Communities of Practice - Chemoinformatics Workgroup to begin to coordinate 

efforts across the Agency to inventory, retrieve, and explore chemical information data 

records. 

• Propose plan to link DSSTox effort with the NLM PubChem project. 

FY06

• Create DSSTox standard chemical field index files for public genomics databases and NTP 

legacy toxicity, to provide linkages within the CEBS relational search environment. 

• Create and publish DSSTox database and documentation files for additional published 

toxicity databases (skin sensitization, acute tox, nasal irritation, etc) and publish additional 

chemical index files for EPA programs (e.g., HPV, SRS-TSCA). 

• Propose plan for coordinated effort to structure-index and quality review chemical 

information in EPA data files currently on the web and propose plan to provide for an EPAwide structure browser on the inter- and intranet EPA website. 

• Assist with formation of proposed toxicity chemicals subset for high-throughput testing in 

the NTP/NIH Molecular Libraries Initiative collaboration. 

FY07

• Assist with incorporation of NCTR Array Track chemical structure browser technology into 

CEBS, and full structure-searching integration with DSSTox data files and all CEBS-linked 

genomics data. 

• Establish procedures and protocols for automating the chemical annotation of new data 

submitted to CEBS from DSSTox Master chemical list. 

• Continue expansion of the DSSTox public toxicity database inventory to include varied 

databases from the published literature and public sources for a wide range of toxicological 

endpoints. 
5

• Implement plan to structure-index EPA data files and provide EPA website structuresearchability through curated EPA chemical data files. 

• Create structure-annotated database of test results for toxicity subset chemicals from the 

NTP/NIH Molecular Libraries Initiative. 

FY08

• Advise and assist with the implementation of chemical definitions (e.g., assigning a chemical 

to a class) and structure analog searching capability within CEBS, integrated with 

toxicogenomics data and bioinformatics capabilities, to serve as the foundation for 

developing chemoinformatics capabilities within CEBS. 

• Advise and assist with the development of procedures and capabilities for deriving chemical 

signatures for predicting toxicity outcomes from the complete profile of CEBS data, with the 

integration of chemical structure information and chemical analogy and structure-activity 

relationship concepts. 

• Begin to tabulate and explore data from NTP/NIH Molecular Libraries screening 

collaboration in relation to other DSSTox databases. 

• Implement procedures for expanding the structure-annotation of EPA chemical data records 

and providing multiple methods for flexible structure or analog searching on the EPA 

website. 

QA: 

QA Plan is being evaluated 
IIC-1 

Title: Simulating Metabolism of Xenobiotic Chemicals as a Predictor of Toxicity. 

Lead: – Jack Jones, NERL 

Research Issue and Relevance: The mission of the EPA is to protect human health and the 

environment from adverse effects caused by exposure to pollutants in the water, soil, air, and 

food. Of the approximately 80,000 chemicals used in the US commerce, relatively few have 

undergone extensive testing to allow a thorough evaluation of risk requiring an extensive array of 

data such as physicochemical properties, persistence, bioconcentration factor, exposure, uptake, 

distribution, metabolism, and toxicity. One of the major uncertainties in evaluating risk is 

determining exposure of the chemical stressor to the target organism. A compounding factor is 

that biotic and abiotic transformations of the chemical inside the target organism following an 

exposure may lead to the formation of reactive metabolites that are toxic. For the vast array of 

organic pollutants, the availability of transformation rate data and metabolite identification is 

sparse. Experiments to identify metabolic pathways can be analytically demanding and costly, 

and are often incomplete with respect to potentially reactive intermediates. Thus, elucidating the 

metabolism of a chemical and formation of reactive metabolites is a major challenge in 

determining pollutant exposure and toxicity for risk assessments. 

Approach: Using computational approaches to screen and prioritize chemicals for risk 

assessments and to minimize animal toxicity testing has been a goal of EPA and other regulatory 

agencies for years. In silico simulation of biotransformations and descriptions of metabolic maps 

have great potential for assessing chemical impacts on human and ecosystem health. Predicted 

metabolism may also be useful for guiding strategic studies to identify >bioactive= intermediates 

for toxicity assessment and for pollution prevention by avoiding commercial use of chemicals 

forming toxic metabolites. The proposed research will develop a capability for forecasting the 

metabolism of xenobiotic chemicals of EPA interest, to predict what chemical metabolites are 

the most likely to be formed, and to interface that information with toxic effect models that 

predict chemical binding to the estrogen receptor (ER), a well-recognized pathway of toxicity 

leading to endocrine disruption. This project will (a) illustrate the importance of considering 

metabolic activation in toxic effects modeling to predict not only parent chemical toxic potential 

but to identify chemical metabolites of equal or greater potential toxicity than the parent 

chemical, and (b) demonstrate an approach to provide this capability for large chemical lists of 

risk assessment concern. 

Progress to Date: ERD-Athens has initiated in vitro metabolism studies using rat microsomes 

to track chemical transformation and metabolite formation for a select group of chemicals (e.g. 

conazole fungicides) of importance to OPP. Analytical methods for metabolite identification, 

utilizing GC/MS, LC/MS, and NMR spectroscopy, are currently being developed and will be 

applied where appropriate to metabolism studies in the current proposal. An advanced analytical 

capability at ERD-Athens, a 600 MHz NMR coupled to a LC/MS/MS for metabolite 

identification and metabolism profiling, will be available in 2005. The cost of testing chemicals 

as reproductive toxicants precludes the possibility of evaluating large chemical inventories 

without a robust strategic approach for setting priorities. A systematic approach has been 

1
developed at MED-Duluth for efficiently expanding the small chemical training sets used for 

QSAR development to adequately cover the chemical lists of EPA concern. Chemical selection 

algorithms are being developed and strategic testing is ongoing at MED to develop the ER 

binding QSAR for chemical prioritization. 

Impact: The development of a metabolic simulator for prediction of chemical metabolic maps 

integrated with a quantitative structure-activity relationship (QSAR) toxic effects model will 

provide effects-based prioritization of chemicals (parent compound and metabolites) relevant to 

EPA Program Offices. One of the primary objectives of the CompTox Program and this 

proposal is the development of computational tools for prioritization of chemicals for toxicity 

testing with the goal of minimizing dependence on test animals. A primary function of OPP and 

OPPT is to provide ecological and human health risk assessment for chemicals to anticipate and 

limit adverse outcomes. The significant challenge that EPA Program Offices (OPP and OPPT) 

face for performing this prioritization process is due to the large chemical inventories (coupled 

with minimal data requirements) for chemical registration and screening. Clearly, computational 

tools that provide the necessary information to achieve these Program Office goals are needed. 

The following outputs for the proposed research will be of benefit in this regard. (1) Provide 

OPPT and OPP the ability to prioritize chemical lists (based upon predicted toxic effects of 

parent chemical and metabolites) with reliability estimates for use in chemical evaluations and to 

rank chemicals for in vitro or in vivo screening and toxicity testing. (2) Provide capability to 

OPP and OPPT for predicting bioactive metabolites. (3) Develop searchable metabolism 

database for OPP and OPPT use for identification of relevant chemical and/or substructures of 

interest for risk assessment And (4) Provide linkage of effects based toxicity model with 

metabolic simulator. 

Partnerships/Collaborations: The research will proceed iteratively from modeling to testing to 

refining models, so frequent communication among ORD scientists and outside partners will be 

essential. ORD investigators will meet at least monthly by conference call and will communicate 

much more routinely by email. The lead PIs from the Mid-Continent Ecology Division (MED) in 

Duluth, MN, and at the Ecosystems Research Division (ERD) in Athens, GA will oversee daily 

research activities at their respective Divisions. Analytical techniques for metabolite 

identification at MED and ERD will be initially developed by ERD and shared across groups. 

Enhanced development of the metabolism simulator will be the primary function of the 

Cooperative Agreement awardee (scientists at Bourgas University) with input and oversight by 

the lead PIs from ERD and MED. Dr. Jack Jones will serve as Project Officer for the awarded 

Cooperative Agreement and will routinely interact with Bourgas scientists via e-mail and 

conference call. Annual on-site meetings will occur between all investigators. Linkage of 

metabolic simulator outputs with a QSAR-based toxic effects modeling will be done at MED. 

The breadth of expertise of this scientific team will enhance the planning and execution of this 

project. There will be frequent interactions with EPA Program Office staff by phone and email to 

verify chemical lists, to discuss needs and concerns of all involved, and to communicate 

progress. Program Offices managers and NERL and NHEERL senior management will be 

regularly informed of research planning and progress. 

2
Milestones/Products:

FY06 

 For priority chemicals, incorporate available chemical metadata, metabolism data, and 

metabolic maps into a searchable database for data management and structure/substructure 

searchable access 

 Forecast metabolic pathways for selected priority chemicals using existing simulator for 

liver metabolism 

 Initiate in vitro Phase I liver microsome experiments; incorporate new laboratory and 

literature metabolism data into simulator training sets 

FY07

 Confirm formation of predicted metabolites for priority chemicals and compare observed 

maps to forecasted maps 

 Develop and approach to evaluate and enhance simulator performance through 

improvement of transformation probability estimates and expansion of transformation reaction 

domain 

 Begin evaluation of refined simulator and model predictions in context of Program Office 

prioritization needs 

QA: 

QA Plan is being evaluated 

3
1

IIC-2 

Title: Modeling Molecular Targets for Toxicity, a Computational Approach to Understanding 

Key Steps in the Mechanisms for Toxicity and a Tool for Prioritizing Bioassay Requirements 

Lead: James Rabinowitz, NCCT (FTE 2.5) 

Research Issue and Relevance: The Agency frequently encounters situations where it must 

make decisions about the potential health and environmental effects of chemicals when all of the 

relevant data is not available. One rational approach to this problem is to estimate the relevant 

missing information by extrapolating from existing data. Knowledge of key steps in the 

potential mechanisms of action provides a template for developing models for this extrapolation. 

These models may be used to inform experimental studies and provide a tool for prioritizing data 

requirements. One relevant example is the Agency need to make decisions on the potential of 

specific anthropogenic chemicals to cause endocrine disruption. Developing molecular models 

that can be used for rapid screening of the interaction of environmental chemicals with receptors 

in the endocrine system will provide an important tool for selecting priorities. 

Approach: For many mechanisms of toxicity the key differential process is the interaction 

between the ultimate toxicant and a macromolecular target (receptor-ligand, enzyme-substrate, 

DNA-genotoxicant, etc.). Modeling this process on a molecular level provides an approach for 

prioritizing chemical information needs. This interaction initiates a cascade of events leading to 

the ultimate (adverse) outcome. Computational molecular models of the interaction of a 

molecule (potential toxicant or its metabolites) with the relevant target, provides insight into the 

capacity of a chemical to initiate the relevant cascade. 

A number of recent scientific and technical advances facilitate this approach. First, many 

of the relevant targets have been identified experimentally. The molecular structure of some of 

these targets has been determined and additional information of this type is likely to become 

available in the future. Second, molecular modeling software for the simulation and analysis of 

interactions of this type has become more sophisticated in a number of relevant ways and make it 

possible to more realistically simulate the processes of toxicity. These advances have resulted 

from both basic computational investigations of the structure and dynamics of macromolecules 

in biological systems and the requirements of the pharmaceutical industry for the discovery of 

new therapeutic agents. Third, computational hardware and visualization techniques have 

steadily improved. Increased processing speed and memory have made it possible to include 

large segments of macromolecules in classical simulations and even in quantum mechanical 

calculations. We are applying molecular modeling methods, fueled by these current scientific 

and technological advances, to investigating chemicals for their capacity to cause toxicity 

through specific modes of action and using a target-toxicant paradigm. 

Initially, this approach is being applied to the study of environmental endocrine 

disruption. Crystal structures exist in the literature for many receptors in the endocrine system. 

By removal of the ligand computationally these crystal structures are used to create virtual 

biomolecular targets for endocrine disruption. The best fit to the target for each of a series of 

potential ligands can then be determined by computational methods. The properties of this fit 

may then be used as part of a scheme to predict the potential of an environmental agent to cause 

endocrine disruption. 
2

The ultimate goal of this research is to develop a library of biomolecular targets for 

chemical toxicity and methods appropriate for the prediction of the ability of a chemical to 

interact with these targets. These targets may then by used as part of a chemical prescreen 

Partnerships/Collaborations: Scientists from RTD/NHEERL are providing a data base of 

chemicals that interact with receptors in the endocrine system and also expertise on the endocrine 

system. Scientists from NTD/NHEERL will provide similar expertise and data and important 

environmental targets in the nervous system. Commercial molecular modeling software and 

advice are available from Schrodinger Inc. These products have been developed primarily for 

the pharmaceutical industry and their use must be adjusted for the needs of this project. Dr. Weitao Yang of the Chemistry Department of Duke University is a collaborator for advanced 

molecular modeling methods. 

Progress to Date: There is increasing concern about the potential of environmental chemicals to 

produce adverse health effects through interaction with the endocrine system. One general 

mechanism for disruption of the endocrine system involves competition for steroid hormone 

binding sites by xenobiotic chemicals that may fully or partially mimic natural hormones. 

Crystal structures of the estrogen and androgen receptors have been used to create 

macromolecular targets. Multiple crystal structures available for the estrogen receptor have been 

used to demonstrate the importance of including receptor flexibility in determining the best fit 

and therefore the chemicals likely have the most effect. Studies on including flexibility are in 

progress. In other preliminary studies, methods existing methods are being employed. Using a 

data base containing estrogens, androgens and chemicals that bind to other nuclear, it was found 

that the results for a single receptor, considered individually, did not correctly order the ligands 

according to the ability to bind to that receptor. However, when the data for a series of receptors 

is considered simultaneously and the results analyzed to determine which receptor a chemical 

interacts with most favorably, all chemicals are classified correctly. 

 An additional Agency concern, which may be approached with the target-toxicant 

paradigm and molecular modeling, is the cumulative risk of some specific pesticides on the 

enzyme acetylcholinesterase (AChE). There is some data that indicates that AChE has two 

binding sites, the catalytic site and an allosteric site that affects the specificity and efficacy of the 

enzyme. In experiments involving just a single toxicant it is not possible to identify the site of 

interaction but we have shown that for mixtures of AChE active chemicals the cumulative 

toxicity depends on the relative interaction of each chemical with each site. A computational 

scheme is being developed for the cumulative toxicity of mixtures that takes advantage of the 

two site model. 

Relevance: This research addresses the Agency need for predictive models for hazard 

identification, both the sub areas of QSAR and other computational approaches and High 

Throughput Screening. 

Milestones: 

FY06 – Results of the use of the target-toxicant paradigm to screen for 

estrogenicity/androgenicity; Results of the affect of the two binding site model on the cumulative 

risk of chemicals acting through the enzyme AChE. 
3

FY07 – Results of the importance of protein flexibility in evaluating the interaction of chemicals 

with macromolecular receptors. Report on the importance of protein flexibility in evaluating the 

interaction of chemicals with macromolecular receptors. 

FY08 – Evaluation of the use of the target-toxicant method as a tool in a diverse chemical 

screen. Application of the target-toxicant approach to other targets of toxicity 

QA: 

QA Plan is being evaluated 
1

IIC-3 

Title: ToxCast, a Tool for Categorization and Prioritization of Chemical Hazard Based on 

Multi-Dimensional Information Domains. 

Lead: Keith Houck, NCCT and David Dix, NCCT (FTE 2.7) 

Research Issue and Relevance: Across several EPA Program Offices (e.g., OPPTS, OW, 

OAR), there is a clear need to develop strategies and methods to screen large numbers of 

chemicals for potential toxicity, and to use the resulting information to prioritize the use of 

testing resources towards those entities and endpoints that present the greatest likelihood of risk 

to human health and the environment. This need could be addressed using the experience of the 

pharmaceutical industry in the use of advanced modern molecular biology and computational 

chemistry tools for the development of new drugs, with appropriate adjustment to the needs and 

desires of environmental toxicology. A conceptual approach named ToxCast has been 

developed to address the needs of EPA Program Offices in the area of prioritization and 

screening. 

Approach: Modern computational chemistry and molecular biology tools bring enabling 

technologies forward that can provide information about the physical and biological properties of 

large numbers of chemicals. The essence of the proposal is to conduct a demonstration project 

based upon a rich toxicological database (e.g., registered pesticides, or the chemicals tested in the 

NTP bioassay program), select a fairly large number (50-100 or more chemicals) representative 

of a number of differing structural classes and phenotypic outcomes (e.g., carcinogens, 

reproductive toxicants, neurotoxicants), and evaluate them across a broad spectrum of 

information domains that modern technology has provided (i.e., physical-chemical properties, 

predicted biological activities based on existing structure-activity models, biochemical properties 

based on high throughput screening assays, cell based organotypic assays, and genomic analysis 

of cells or organisms). These domains represent increasing biological relevance, as well as 

increasing resource requirements. The ultimate goal of the project would be to mine the 

resulting data for association between and among the various domains and the known 

toxicological properties of the base set of chemicals in order to provide a structured strategy to 

identify potential toxicity pathways, and to prioritize chemicals them for subsequent testing 

based on that information. 

 The underlying hypothesis is that whether is concerned with the off target effects of 

drugs, as desired to be understood by the pharmaceutical industry, or toxicity in case of 

environmental agents of interest to the EPA, the response is driven by interactions with 

biomolecular targets of one form or another. One needs only to identify those receptors of 

concern and identify tools for assessing the likelihood of interaction with the chemicals of 

concern. In moving from the drug development arena (which can be compared to working along 

one or just a few vectors) to the environmental toxicology arena (which can be likened to 

working on a matrix instead of a vector), one needs to shift from a specific screening target to a 

more global agenda, and it becomes necessary to vastly expand the number of potential 

biomolecular targets, be these obtained from in silico assays, biochemical assays, cell based in 

vitro assays, surrogate animal models, or short term studies in traditional species. Hence, a wider 

net of endpoints and information sources will be applied, at least initially, as the concept 

transgresses from a concept to a reality. 
2

 Of course, a number of hurdles would need to be addressed before launching such an 

effort, including: (1) identification of a subset of chemicals for serving as the proof of concept 

models; (2) developing a chemical inventory management and distribution system; (3) 

identifying an upper cap on the per chemical cost of obtaining screening level data: (4) selecting 

assays within the available resources; (5) flexibility, or tiering, of domains based upon preexisting knowledge; (6) perhaps initially targeting only a few manifestations of toxicity rather 

the all possible ones to decrease the complexity of the task; (7) evaluating the impact of 

metabolizing capability, or lack thereof, on the efficiency of the screening assays; (8) developing 

a bioinformatic approach to mining the resulting data and identifying signatures of concern; and 

(9) carrying out a prospective assessment of the bioinformatic approach using chemicals 

currently entering a traditional testing process. These hurdles would be the subject of 

considerable discussion as the potential feasibility of this concept proposal is discussed further. 

Progress to Date: At this point in time, ToxCast is nearly the end of its conceptual design 

phase. The information domains have been identified, and a number of potential contributing 

data sources have been investigated (e.g., Iconix, MDS Pharma, CEREP, Amphora, PASS). 

Recruitment actions are underway to add two staff members to the NCCT who will be 

responsible for the biological and information processing components of ToxCast. 

Communications have been established with the NTP/NIEHS which has similar interests and 

which is beginning to work with the NIH Molecular Libraries Initiative (see also the DSSTox 

implementation plan). Outreach to the OPPTS, ACC, EDF and other external groups have also 

begun to help develop a consensus on the specific directions and contents of ToxCast. 

Impact: The availability of a biologically and chemically based system to begin to associate 

chemicals of like properties and activities will provide a number of EPA Program Offices with 

an extremely useful tool that heretofore has been seriously lacking. The tool may be one of the 

first broad scale products of the NCCT that addresses the mission of improving the efficiency 

and effectiveness of hazard identification and risk assessment methodologies employed by the 

EPA. 

Partnerships/Collaborations: The NCCT is working to establish partnerships with a number of 

external groups that can facilitate development of the information needed in ToxCast. These 

groups include the OPPTS, the NTP/NIEHS, the ACC, the EDF and a number of commercial 

vendors that market some of the enabling technologies. 

Milestones/Products:

FY06 - Develop conceptual framework for ToxCast 

FY07 - Establish initial battery of assays across the information domains, identify list of 

chemicals to evaluate proof of concept for framework and begin data acquisition 

FY08 - Report on the utility of statistical clustering techniques on assay results from pilot 

chemicals to group them according to known toxicity patterns; revise framework as dictated by 

results. 

QA: 

QA Plan is being evaluated 
IIC-4 

Title: Development of microbial metagenomic markers for environmental monitoring and risk 

assessment. 

Lead: Jorge W. Santo Domingo, NERL 

Research Issue and Relevance: The microbiological water quality standards established by 

EPA depend on culturing fecal indicator bacteria to predict the risks associated with water usage. 

For decades this has been the favored approach to microbiological monitoring in spite of the fact 

that culture-based methods tend to underestimate the densities and diversity of microorganisms 

in environmental samples (Amann et al., 1995). Relevant to public health is the fact that 

nonculturable pathogens could be the etiological agents of many waterborne illnesses (Sails et 

al., 2002). In addition, nonculturable organisms may be better indicators of fecal contamination. 

Moreover, nonculturable bacterial indicators may be useful in the identification of the fecal 

sources impacting a particular water system. Identifying the sources responsible for the fecal 

pollution of a natural waterway is important in order to reduce the fecal bacterial levels and in 

turn reduce the illnesses associated with recreational activities, and food (e.g., fish) and water 

consumption (Simpson et al., 2002). Consequently, strict dependence on culturing techniques 

continues to be a notable roadblock in the path towards understanding the correlation between 

bacterial indicators of fecal contamination and the hazards associated with fecally impacted 

waters. Nucleic acid-based approaches can circumvent many of the shortcomings of the culturebased methods. For instance, the possibility of rapidly and simultaneously monitoring hundreds 

of microorganisms and genes relevant to public and environmental health is now becoming a 

reality in light of the recent advances in microarray technology (Stahl and Tiedje, 2002). Such a 

capability will categorically improve microbial risk assessment and the framework used in the 

development of environmental monitoring and risk management tools. 

Approach: We propose the development of an innovative metagenomics program to enhance 

our current capabilities for environmental microbial monitoring, risk exposure, risk management, 

and risk assessment. The general goal of this study is to develop a novel molecular markers 

based on fecal microbial genomes to better assess the sources of fecal contamination in natural 

water systems. Assuming that the evolutionary pressure for the selection of host specific 

populations must involve ecologically driven processes we hypothesize that genes playing a role 

in host-microbial interactions and cell-cell recognition (e.g., cell surface proteins, toxins, and 

adhesin) are better markers of host specificity. The primary objective of this study is to discover 

novel and validate the use of fecal metagenomic sequences as MST host specific markers. By 

looking at collective genomes we will substantially increase the number of potential genes and 

microbial species that can be used in the development of molecular microbiological assays 

(Schloss and Handelsman, 2003). The first phase of this program will focus on constructing fecal 

metagenomic libraries specific to animals that are known to be relevant pollution sources of 

watersheds in the United States. The following phases will focus on systematically identifying 

DNA sequence markers for specific human and bovine fecal sources and testing the specificity of 

these markers against waters impacted with different pollution levels and pollution sources. We 

will use genomic subtraction of metagenomic libraries to generate unique sequences from the 

collective microbial genomes present in animal feces. Due to the complexity of microbial 

1
communities, this approach will facilitate the identification of candidate host specific microbial 

genes. Real-time PCR assays will be developed to test the host specificity of the latter markers 

and to quantify the target genes in environmental waters impacted with different levels of fecal 

contamination. A similar approach will be undertaken with samples from ongoing 

epidemiological studies. Markers that show host specificity will be included on biochips 

containing sequences specific to fecal indicator bacteria and pathogens and challenged with 

DNA extracts of different fecal samples and water samples impacted with different levels of 

pollution. By combining the epidemiological studies with the host specific assays and 

pathogenic markers, we hope to gain better predictive capability to determine outcomes from 

scenarios associated with fecally impacted waters. 

Progress to Date: See below 

Impact: Distinguishing between human and nonhuman fecal contamination in our waterways 

has never been more important than in recent years in light of the need by the States to comply 

with deadlines associated with the Total Maximum Daily Loadings program (Simpson et al., 

2002). Accurate assessment of the primary sources of fecal pollution is needed in order to 

calculate the different risks associated with each of the potential sources impacting water 

systems and to correctly implement and evaluate Best Management Practices (BMP) used to 

remediate fecally polluted waters. As currently developed, most MST methods depend on 

markers that have no ecological meaning. The main goal of this study is to develop assays that 

can be used in source identification, environmental monitoring, and risk assessment. In order to 

accomplish this goal we will combine a metagenomic-scale approach with high-throughput gene 

mining (microarrays) and real-time PCR assays. Therefore, thiis research supports EPA’s GPRA 

Goal 2 (Clean and Safe Water). The proposed work will provide a rich source of metagenomic 

information (i.e., novel molecular markers) for sequenced-based analyses aimed at better 

monitoring of fecal pollution and improving the assessment of the outcome associated with 

different pollution sources. Consequently, it is anticipated that the results will provide a 

molecular-based framework to (1) better predict the relationship between microbial water quality 

and public health risks, (2) determine the impact of different microbial pollution sources on 

watershed biology, and (3) effectively control or eliminate pollution in our Nation’s watersheds. 

Partnerships/Collaborations: As this program is relevant to water fecal contamination and 

public health issues related to water, we have requested the participation of personnel from 

EPA’s Office of Water and the National Center for Environmental Assessment as consultants. 

The primary points of contact of the latter organizations will be Dr. Robin Oshiro and Dr. Mary 

Rothermich. Drs. Oshiro and Rothermich will act as liaisons for their respective organizations. 

Their role will be to provide advice on how the data can be utilized by their offices. They will be 

invited to participate in mid year review meetings and conference calls. We have also briefed 

other personnel from the Office of Water, and they have expressed interested in receiving 

updates on the progress of this project. As this project has an immediate impact on problems that 

states and tribes are currently facing, we will keep Drs. Ron Landy and Bobbye Smith (Region 3 

and 9 ORD-regional science liaisons, respectively) informed on the current progress of this 

project. In addition, we will consult with members of EPA’s Genomics Task Force Workgroup 

on issues pertaining Data Management, Data Analysis, and Data Submission and Methods 

Performance. Drs. Rebecca Calderon and Al Dufour will also be consulted on a regular basis as 

2
they are managing the epidemiological study that is currently been conducted by members of 

NERL and NHEERL. 

Milestones/Products: 

FY06 - Evaluation of metagenomic databases as a source of molecular markers to assess human 

and animal fecal contamination in surface waters 

FY07 - Report on the evaluation of PCR-based host-specific assays to confirm the presence of 

animal fecal sources of pollution in waters impacted by fecal contamination. Report on fecal 

indicator microorganisms and/or genetic markers from fecal material whose densities in 

recreational waters best correlate with the rates of illnesses in users of recreational waters. 

QA: 

QA Plan is being evaluated 

3
1

IIID-1 

Title: Statistical Methodology for Estimating Parameters in PBPK/PD Models 

Lead: R. Woodrow Setzer, NCCT (FTE 0.8) 

Research Issue and Relevance: PBPK/PD models are large dynamic models that predict tissue 

concentration and biological effects of a toxicant before PBPK/PD models can be used in risk 

assessments in the arena of toxicological hypothesis testing, models allow the consequences of 

alternative mechanistic hypotheses to be predicted in highly non-linear systems. Whether for 

quantifying the uncertainty of the extrapolation of a dose metric or determining which hypothesis 

is better supported by the evidence, and by how much, it is critical that the uncertainties inherent 

in such modeling be properly quantified, and established principles of statistical inference be 

brought to bear. 

 However, such quantification is far from simple. Biological systems models generally 

involve large numbers of parameters whose values are known with varying degrees of 

uncertainty. Some parameters (for example, tissue-specific fractions of cardiac output and 

fractions of body mass) can reasonably be thought of as varying among individuals of a 

population. Other parameter values have been estimated in in vitro systems, or may have been 

calculated from properties of the chemicals involved (for example, partition coefficients in a 

physiologically-based pharmacokinetic model). It is typically the case that the values of 

parameters in a biological systems model will be derived from the results of several different 

experiments, of very heterogeneous designs, and often conducted in different laboratories at 

different times. Over the last decade or so a small number of statistical investigators have begun 

to explore the issues involved in applying statistical methods to such systems. While there are 

examples of the application of statistical methodology to PBPK/PD models in the literature, there 

is as yet no coherent approach to this activity that takes into account the special issues that arise 

in PBPK/PD modeling. The NCCT is establishing a program to further develop the statistical 

methodology to support the rigorous quantification of both the uncertainty of parameter 

estimates and of model predictions, with the aim of providing a framework for statistical 

applications in PBPK/PD modeling. 

Approach: The outstanding issues are a mix of computational and theoretical statistical 

problems, and are best investigated through exploring specific models and datasets. Thus, we 

need to identify PBPK/PD models for chemicals of interest to the Agency, identify the 

outstanding theoretical statistical issues that need to be solved, and set up a computing platform 

to handle the large computing loads this exploration will entail. Finally, theoretical statistical 

exploration needs to precede hand-in-hand with practical PBPK/PD model development, 

ultimately leading to the publication of a practical framework for applying statistical 

methodology to PBPK/PD models. 

Progress to Date: Investigators in NHEERL, NERL, and CIIT Centers for Health Research 

have been approached for appropriate models and data sets, and several models have been 

identified for further work. A graduate student in biostatistics and his advisor have been 

recruited to work on outstanding theoretical issues and apply solutions to models and data sets of 
2

interest to the Agency. The literature surrounding this topic has been surveyed, and a literature 

review is in progress. 

Impact: PBPK/PD models are of increasing interest in the Agency for use in extrapolation from 

animals to humans, across routes of exposure, and for integrating acute effects over discrete 

episodic exposures. Better characterization of methods for estimating parameters and 

quantifying uncertainties in such models predictions will remove one major impediment to their 

more general application. This will allow replacement of default uncertainty factors with 

transparent mechanism-based statements of scale and uncertainty, in turn decreasing the 

subjectivity and increasing the transparency of environmental health risk assessments. 

Partnerships/Collaborations: Collaborations with NERL, NHEERL, and the CIIT Centers for 

Health Research and the Department of Biostatistics in the UNC School of Public Health are 

being developed to develop statistical methods for parameter estimation in PBPK/PD models. 

Milestones/Products: 

FY06 – Submission of a journal article outlining outstanding statistical issues in the analysis of 

PBPK models. 

FY07 - Joint NIEHS/NCCT workshop on Statistical Issues in PBPK/PD modeling. 

FY08 - Publication of framework for the statistical analysis of PBPK/PD models. 

QA: 

QA Plan is being evaluated 
1

IIID-2 

Title: Modeling Toxicokinetics for Cross-Species Extrapolation of Developmental Effects 

Lead: Hugh Barton, NCCT (FTE 1.4) 

Research Issue and Relevance: Animal toxicology studies used to evaluate the potential for 

effects due to exposures during developmental periods are extrapolated to humans based 

upon the maternal exposure dose. The approach does not address whether the toxicokinetics 

are similar across species during the relevant critical developmental window. In this research 

program we will develop toxicokinetic models for estimating internal dosimetry during early 

life stages to serve as a basis for improved interspecies extrapolation. Absent information 

specifying the critical window, estimates of internal dosimetry during the in utero, 

lactational, and early post-weaning periods will characterize the uncertainty in evaluations 

based upon maternal exposure dose. With information on the critical window, models may 

describe toxicokinetic and toxicodynamic processes in the relevant toxicological species and 

humans permitting improved quantification. 

Approach: This research will incorporate a range of pharmacokinetic modeling methods 

including noncompartmental analyses and classical and physiologically-based 

pharmacokinetic modeling to describe dosimetry in early life-stages relevant to onegeneration toxicity studies (i.e. maternal exposure resulting in fetal exposure, lactation 

exposure of pups after birth, and exposures of growing pups). Perfluooctanoate (PFOA) and 

related compounds will be used as initial prototype chemicals due to the importance of 

effects observed in offspring, which are used as endpoints in risk assessments. Lactational 

exposure will be modeled first because dosimetry during this period has not yet been 

evaluated. A physiologically based pharmacokinetic (PBPK) model for PFOA will be 

developed to characterize dosimetry during these different lifestages and identify data gaps 

that limit the ability of the model to address both rats and humans. Finally, this early life 

stage model will be used to evaluate dosimetry during different early life periods for a 

selected group of other prototype compounds for which developmental toxicities are 

observed. Prototype compounds would be selected to have a range of pharmacokinetic and 

physical chemical properties. This analysis will aid in the evaluation of the uncertainties in 

the default risk assessment approach based upon maternal exposure. 

Progress to Date: Literature reviews have been ongoing to obtain physiological parameters 

for growing rat pups. Compartmental pharmacokinetic modeling of PFOA in adult animals, 

including pregnant and lactating females, has been completed. 

Impact: Biologically based modeling of toxicokinetics and toxicodynamics during 

developmental periods can improve chemical risk assessments based upon developmental 

effects. At a minimum, characterization of internal dosimetry will inform the uncertainties 

attendant to analyses based upon the maternal exposure dose. In the presence of information 

on the critical window, the models may directly form the basis of the quantitative risk 

assessment by deriving the relevant internal dose metric for extrapolation to humans. 

Perfluorinated compounds, including PFOA, are an important proof of concept for this 
2

research because they are a class of compounds producing developmental effects of 

significant regulatory concern. 

Partnerships/Collaborations: There are active collaborations with NHEERL, NERL, and 

NTP to collect data for perfluorinated compounds and NHEERL to collect data on conazole 

pesticides. Evaluation of published literature to obtain physiological parameters has been an 

ongoing collaboration with NCEA. In addition, this project complements ongoing efforts by 

others to develop models for human children. Sharing of information and physiological 

parameters has been ongoing through an early life physiological parameters database project 

at the International Life Sciences Institute with participating by US and Canadian 

government agencies, academic institutions, companies and non-profit organizations. 

Milestones/Products: 

FY06 - Model rat pup lactational exposure for perfluooctanoate (PFOA) using 

compartmental approaches. 

 

FY07 - Develop initial physiologically-based pharmacokinetic model for PFOA in rat 

maternal-fetal-pup unit and identify data gaps in relation to rat and human. 

FY08 - Evaluate modeled dosimetry for rat fetus and pup for a limited number of prototype 

compounds to inform the uncertainty in use of maternal exposure dose in risk assessments. 

QA: 

QA Plan is being evaluated 
1

IIID-3 

Title: Development of a portable software language for physiologically-based 

pharmacokinetic (PBPK) models. 

Lead: R. Woodrow Setzer, NCCT (FTE 0.1) 

Research Issue and Relevance: The PBPK modeling community has had a long-standing 

problem with modeling software compatibility. The numerous software packages used for 

PBPK models are, at best, minimally compatible. This creates problems ranging from model 

obsolescence due to software support discontinuation to difficulty in linking models created 

in different packages. 

The objective of this research effort is to create an XML extension to be used in 

conjunction with the systems biology markup language (SBML), an XML-based, machinereadable format for representing models of biochemical reaction networks, including 

metabolic networks, cell-signaling pathways, and regulatory networks. This extension will 

augment SBML to accommodate PBPK models with reasonable complexity, including full 

documentation, PBPK modeling vocabulary, and examples for instructional and 

demonstrational purposes. This extension will be constructed to permit seamless linking of 

PBPK models to the biological pathway models abundantly available in SBML format. A 

secondary goal is to develop a pseudo-generalized visualization program that is capable of 

translating an SBML file for a PBPK model into a visual schematic along with equations 

representing the schematic in differential equation form, accompanied by parameter values 

and references for the parameters. 

End-users of a PBPK model, such as risk assessors, would benefit greatly from being able 

to use the software of their choice to run the model and to visualize the model itself and 

documentation for it, making incorporation of PBPK models in the risk assessment process 

easier and more efficient. 

Approach: We will begin by coding simple to moderately complex PBPK models in SBML 

and testing the results. This initial step will map out the limitations and weaknesses of 

SBML for accommodating PBPK models and give insight into the magnitude of the work 

involved in creating an appropriate SBML extension. This initial step will be an iterative 

process with complexity of the models increasing until all major necessary attributes of the 

extension are uncovered. The next step will be to create an initial version of the schema and 

extensively test it. An NCCT-sponsored meeting will be held with all interested parties 

where the schema will be presented and a plan for moving forward will be presented and 

debated. This plan will include formal and informal collaborations and partnerships. Once a 

final version of the extension has been developed and fully tested, a formal SBML 

workgroup will be formed and a proposal will be written to the SBML Community at Large 

for incorporation of the extension into the current version of the base SBML schema. At this 

point, SBML would fully accommodate PBPK models. Simultaneously, we will be 

developing the visualization program with the objective of using it not only as an aid in the 

development and usage of PBPK models, but also as a powerful educational tool. 
2

Progress to Date: 

A team has been assembled to develop the schema as well as advise and manage the 

project. Some necessary features of the extension have been determined and we are currently 

coding a simple PBPK model in SBML as part of the process of determining all necessary 

attributes. The interest of the PBPK community has been assessed and a list of interested 

parties, some possibly for collaboration, has been created. 

Impact: 

The ability to create, transfer, use, augment, and review PBPK models without the 

limitation of software compatibility is a long-standing desire in the PBPK community. The 

ability to seamlessly link PBPK models to biological pathway models is of great interest, 

since this gives the modeler a fast and efficient means of extending the estimations of tissue 

dose from the PBPK model to cellular-level responses, thereby facilitating a quantitative 

investigation of dose-response relationships through mode/mechanism of action. By making 

the use of PBPK models faster, easier, and more reliable, this product will make the use of 

these models more common and widespread in risk assessment. 

Partnerships/Collaborations: 

Formal collaboration has been established with the Lockheed Martin Corporation through 

the U.S. EPA Office of Environmental Information. The possibility of collaborating with 

The Aegis Technologies Group (developers of the prominent PBPK modeling software 

package, ACSLXtreme) is currently being explored. Contact with original SBML developers 

and project managers have been established with positive feedback. Possible collaboration 

with CIIT Centers for Health Research is also being explored. Groups that have expressed 

interest in being immediate clients for the products of this project include EPA/NCEA and 

NIEHS/NTP. 

Milestones/Products: 

FY06 – Report on findings of assessing extensiveness required for the XML schema. 

FY07 – NCCT-sponsored meeting on evaluation of the performance of the completed XML 

schema and status of the visualization program. 

FY08 – Report on proposal to have finalized XML schema included in the next release of 

SBML and evaluation of visualization program. 

QA: 

QA Plan is being evaluated 
1

IIID-4 

Title: Systems Modeling of Prostate Regulation and Response to Antiandrogen 

Lead: Hugh Barton, NCCT (FTE 0.3)

Research Issue and Relevance: The prostate is an androgen-dependent tissue that is an 

important site of disease in human males as well as an important indicator of androgen status 

in animals. The rat prostate is used for studying antiandrogenic drugs as well as for 

evaluation of endocrine disruption (e.g., Hershberger Assay). Pubertal changes in the 

prostate have been observed to be as sensitive to environmental antiandrogens as in utero 

effects. The goal of this research is to model the biology of prostate androgen function on a 

systems level to determine the factors responsible for the dose-response observable with 

androgens and antiandrogens in the male rat. This includes investigation of the roles of 

positive and negative feedback loops in prostatic response following castration and dosing 

with testosterone and/or antiandrogens. 

Approach: A biologically-based, systems-level model will be developed describing the 

regulation of the prostate by androgens. The model will extend an existing model for the 

male rat central axis, which describes feedback between luteinizing hormone and testosterone 

production in the testes, to include the prostate and conversion of testosterone to 

dihydrotestosterone (DHT). The prostate model will describe binding of androgens to the 

androgen receptor, 5α-reductase catalyzed production of DHT, and gene regulation affecting 

cell proliferation, apoptosis, and prostatic fluid production. The model will combine 

pharmacokinetic models for endogenous hormones (i.e., testosterone, DHT, LH) and 

exogenous antiandrogens (e.g., finasteride, flutamide or casodex, vinclozolin), and a 

pharmacodynamic model for androgen-dependent prostate functions. Linkages of this model 

with genomics data obtained with castration, testosterone treatments, or antiandrogen 

treatment will be explored to assist in developing perspectives on how such data would fit 

into quantitative risk assessments. 

Progress to Date: A model has been constructed that recapitulates the time-dependent 

changes in prostate following castration of male rats resulting in decreased serum 

testosterone, prostatic androgen receptor, and prostate weight. The model currently poorly 

describes the dose-response for less drastic changes in testosterone; this is under 

investigation. A classical compartmental model for finasteride, a therapeutic 5α-reductase 

inhibitor, has been linked to the biologically-based model for the rat endogenous hormone 

function. Opportunities to link the model with genomics data are being explored. 

Impact: 

 This project is designed to evaluate how perturbations of endogenous biological systems 

produce observable dose-response behaviors using a widely used endpoint, rat prostatic 

response to androgen status. In addition, insights will be obtained concerning the potential 

utility of genomic data for dose-response analysis in relation to more traditional toxicological 

endpoints. 
2

Partnerships/Collaborations: (e.g., where is the data coming from?). The initial modeling 

utilizes data published in the peer-reviewed literature. Collaboration with Dr. Mitch Rosen 

(NHEERL/RTD) is under development to obtain prostate genomics data. Collaboration with 

Dr. Robert Chapin (Pfizer) also is being developed to obtain genomics data following 

testosterone implantation.

Milestones/Products: 

 FY06 - Report on the prostate function model following castration. 

 

 FY07 - Report on dose-response with testosterone and/or antiandrogens. 

 

 FY08 - Evaluate linkage of the biologically-based model of prostate androgen-

 dependent gene regulation with genomics data. 

QA: 

QA Plan is being evaluated 
1

IIID-5 

Title: Systems Biology Model Development and Application 

Lead: Rory Conolly, NCCT (FTE 2.0) 

Research Issue and Relevance: System biology models holistically describe, in a quantitative 

fashion, the relationships between different levels of a biologic system. Relationships between 

individual components of a system are delineated. System biology models describe how the 

components of the system interact to give rise to the physiologic function of the system. For the 

realm of toxicology these models will be developed to not only describe such interactions but to 

also describe how exposure to toxicants can perturb these interactions and the normal physiology 

of the system. A hallmark of these models is that they are designed to allow study of the 

multiple components of the system simultaneously. The resolution of such models depends upon 

the problem being studied. They can describe interaction between molecules, between molecules 

and tissue, organs, and whole systems. They can even extend to interaction between different 

species within ecosystems. Most populations, including humans, are simultaneously exposed to 

numerous potential toxicants under a myriad of conditions. Further those populations have a 

variety of other processes occurring during and with those exposures. Underlying disease, 

nutritional factors, and genetic predisposition are just a few examples of underlying factors that 

can greatly influence an organism’s or population’s response to environmental toxicants. System 

biology models offer the opportunity to describe and understand some of these mechanisms so 

that risk assessments can eventually be based on the most relevant biological information and not 

just on default assumptions for which the uncertainty is not easily identified nor quantified. The 

models are also excellent tools to help analyze data and test different hypotheses. These models 

will use and depend upon complex data such as is generated from genomics, proteomics, and 

metabolomics. Iteration between experimental measurements and computational modeling is 

necessary to understand the function of complicated biologic systems. 

Approach: This project will progress along four broad levels each informing and helping 

develop one another. First, and already on-going, are a number of tasks using existing 

physiologic pharmacokinetic and pharmacodynamic models to develop and then test different 

hypotheses describing the adverse affect that may result from environmental exposures. This 

work is being, at this time, applied to humans. Models describing enzymatic changes, such as 

cholinesterase inhibition, are being used to show the relative impact of different exposure 

scenarios. Further, these models are also being developed and used to help design the most 

useful and cost-effective exposure measurement studies. Collaborative work is being performed 

to test the suitability of using in-vitro and computationally derived parameters in models such as 

these. This is another important aspect as given the complex nature of future models and 

exposure scenarios methods to rapidly estimate key physiologic, thermodynamic, and 

biochemical parameters will be necessary, especially for those conditions that preclude practical 

laboratory measurements. In addition, a number of tasks are underway or being planned that will 

build more complicated pharmacodynamic models for this purpose. (See project description on 

pharmacodynamic modeling of the prostate as an example.) 

 Second, a task is being formulated that will begin to build a conceptual model that would 

used various types of data, such as pharmacokinetic data, mode of action data, “omics” data, etc. 
2

for a specific case. A mathematical model will then be built and implemented. The model will 

be used to show the importance of relevant data. That is, the question will be answered “how 

much can the risk assessment be improved and uncertainty reduced as more data demanded and 

become available?” Some work in this task will begin to develop the mathematical constructs 

necessary to incorporate “omic” data and information into quantitative models. 

 The third task will start to describe a fairly complicated endogenous physiologic or 

biochemical process in detail. Organisms have many biochemical processes that help maintain 

the homeostasis of the system. Understanding and describing such processes may be crucial to 

eventually describing and predicting the adverse effects resulting from perturbation of those 

processes resulting from exposure to exogenous substances and factors. Although this task is 

still in formative stages examples include the development of kinetic model of the microsomal 

oxygenation system in hepatoctyes, or describing the glutathione system in physiologic detail. 

After model formulation, implementation, and testing the model will be expanded for use with 

pharmacokinetic models to describe what occurs in the endogenous system after exposure to 

environmental toxicants. Again, such a model can be used for hypothesis testing as well as for 

predictive risk assessments. 

 The fourth task will seek to develop a model for a disease process affecting many people. 

By modeling a disease process with the subsequent pharmacologic exposures a model will be in 

place which can then be used to investigate the potential of exacerbation by environmental 

exposures. Again, in the very formative stages one such disease state being considered is type 2 

diabetes. 

 These latter two tasks will develop examples of how endogenous processes, disease 

states, and exposure to endogenous environmental factors may interact to cause adverse affects 

or exacerbate pre-existing conditions. It is obvious how all four of these tasks are related on how 

they build upon each other. Further some over arching themes are important to all of them. For 

each, eventual application to probabilistic systems will be an anchor. As such, advanced 

statistical techniques will be used (as developed here, in other computational toxicology projects 

and elsewhere) to account for variability, sensitivity, and uncertainty. Also, as mentioned, these 

models are excellent tools for hypothesis testing. That makes them also good tools to keep a 

balance between describing the detailed complexities of a biologic system and the parsimony 

that is often practically necessary real world risk assessment process. 

Progress to Date: In FY 2005 key staff has come into the center including an ST and two 

biomedical engineers (one as a post-doc). Further staffing plans are being made. Collaborations 

(see section below) are being established. Work on the first task and work in related Center 

modeling projects are well underway. Potential collaborators and staff for the third and fourth 

tasks have also been identified. 

Impact: As described in previous sections, this work will help take risk assessment to the next 

higher level. The latest mechanistic information and the interplay between exposure, 

endogenous factors, pre-existing conditions, and genetic predisposition can be rationally 

accounted for by using such models. There is a great need for quickly illustrating how the latest 

molecular information, especially “omic” information and information coming from high 

through-put studies will be used in risk assessments. NCEA and several program offices, 

including OPPTS, OW, OAR will all be faced with these types of biologic data and currently 

have few examples of tools for using such information in a beneficial way. This work will be 
3

accomplished over several years will gradually add more and more tools to their arsenal for risk 

assessment. 

Partnerships/Collaborations: National Health and Ecological Effects Laboratory, the National 

Exposure Research Laboratory, the National Institutes of Environmental Health Sciences, 

Department of Energy, University of Michigan, Moscow State University (Russia). 

Milestones/Products: 

FY06 - Journal Article on use of biologic models to ascertain necessary resolution of 

exposure measurements. Collaborative groups formed for task 2, 3, and 4 

 Formulation of conceptual model and writing the mathematics and code for that 

model for task 2, above. Selection and begin model implementation for 

endogenous biochemical system for task 3. Start model development of disease 

process for task 4. 

FY07 - Implementation of model for tasks 2 and 3 and begin application for cases with 

exposures to known environmental toxicants – abstracts and presentations. 

Disease model coded, exercised, and evaluated for task 4. Determine through 

literature search and other means for examples of exogenous exposure that impact 

the disease process selected for task 4. 

 

FY08 – Journal articles illustrating some uses of “omics” information in quantitative 

models. Summary report for Agency use on earliest best practices on “omics” and 

system biology models. Journal article for disease model. Enhancement of disease 

model to incorporate exposures to environmental toxicants. 

This project will continue beyond this three year period and more products relevant to 

Agency clients will result. 

QA: 

QA Plan is being evaluated 

Related Tasks: 

Systems Biology Modeling 

Computational Modeling of Interleukin 1 (IL-1) Mediated Intracellular Signaling 

(Collaboration with Jim McDougal, Wright State University) 

IL-1 signaling is an important component of the molecular mechanism by which the 

acute inflammatory reaction in skin develops in response to a dose of skin irritant. The shortterm goal of this project is to understand how best to develop computational models of signaling 

pathways given limitations of the databases describing network topology, protein concentrations, 

reaction rate constants, etc. The longer term goal is to develop a computational model that can 

provide useful dose- and time-response predictions of the acute inflammatory response in skin. 
4

Computational Modeling of Extracellular Signal-Regulated Kinase (ERK) Signaling in 

Cerebellar Granular Cells 

(Collaboration with Bill Mundy, NTD, and Qiang Zhang, CIIT Centers for Health Research) 

Extracellular signal-regulated kinase is important for regulating neuronal survival and 

growth. ERK its activation is susceptible to perturbation by environmental chemicals. The 

objective of this study is to 1) characterize the dynamics of ERK activation in response to BDNF 

and NMDA; 2) use computational modeling to promote understanding and dissecting of the 

signaling network underlying ERK activation. This project will improve our understanding of 

molecular mechanisms which stressors such as BDNF and NMDA perturb neuronal cells, 

thereby enriching the database needed for identification of predictive biomarkers. 

Modeling of Mammalian Biomolecular Responses: Computational Core in support 

of a Superfund Basic Research Project (SBRP) at Michigan State University 

(Collaboration with Norb Kaminski, Michigan State University, and Mel Andersen, CIIT 

Centers for Health Research) 

Starting in the summer of 2006, Dr. Conolly will work closely with a Computational 

Core housed at the CIIT Centers for Health Research in support of the SBRP at Michigan State 

University. The initial effort will be to develop computational descriptions of signaling pathways 

involved in B cell maturation and which are perturbed by exposure to low levels of TCDD. 

Mathematical Model of Steroidogenesis: Molecular Response to Endocrine Disruptor Exposures 

(Collaboration with Michael Breen, NCCT, Gerald Ankley and Dan Villeneuve, Mid-Continent 

Ecology Laboratory) 

Ankley et al. are using a systems biology approach to characterize the HPG axis in small 

fish. The project will increase our understanding of the basic biology that is perturbed by 

endocrine disrupters. A computational model of steroid hormone biosynthesis, starting from 

cholesterol in support of this effort is being developed. This project is expected to provide rich 

opportunities for iterative interaction between computational modeling and data collection. 

Visualizations of Computational Systems Biology Model Predictions 

(Collaboration with Michael Breen, NCCT, and the National Computer Center) 

The goal of this project is to develop software tools that facilitate effective interactions 

between computational modelers and laboratory experimenters. As an initial project, we are 

developing simulated western blots to visualize model-predicted concentration-time histories. 

These visualizations will be used to correlate model predictions with literature data typically 

shown as western blot images. 

Risk Assessment Modeling

Dose-response modeling of formaldehyde carcinogenicity 

(Collaboration with Fred Miller, Cary, NC) 

 An updated version of an existing dose-response model for the carcinogenicity of 

formaldehyde is being developed. This effort focuses on refinements directed at specific issues 

that were raised after publication of the earlier model. 
IIID-6 

Title: Use of Toxicogenomics Data in Risk Assessment: Case Study for a Chemical in the 

Androgen-Mediated Male Reproductive Development Toxicity Pathway

Lead: Susan Euling, NCEA 

Research Issue and Relevance: The goal of this project is to address the question, “Can 

existing toxicogenomics (TG) data improve Environmental Protection Agency (EPA) chemical 

health or risk assessments?” Although genomics data promises to impact multiple areas of 

science, medicine, law, and policy, there are only a few areas where genomics data currently has 

application (e.g., biomarkers of disease). As the technology continues to advance, EPA will 

need to prepare for genomics data availability and submission by: 1) identifying areas of risk 

assessment where such data may be particularly useful; 2) developing acceptance criteria for 

inclusion of toxicogenomics data in risk assessment; and 3) developing approaches for the use of 

toxicogenomics in risk assessment. These needs will likely require an iterative and collaborative 

research process between risk assessors and scientists (inside and outside the Agency). At the 

NCEA sponsored Genomics and Risk Assessment Colloquium in 2003, one of the 

recommendations was to conduct case studies that could provide a practical attempt to 

incorporate currently available toxicogenomics data that would illuminate issues and the 

methods development. This project is responds to this recommendation. 

Approach: To address the question of whether TG data can improve health risk assessments, a 

case study will be performed in which TG data for one chemical will be incorporated 

qualitatively within the hazard characterization step of a recent or ongoing EPA chemical health 

or risk assessment. Integrating TG data into an assessment case study will identify areas that 

may be impacted by TG data and contribute to the development of criteria and approaches for 

incorporating TG data in assessments. The chemical for the case study will be selected from 

among those that affect the androgen-mediated male reproductive development toxicity pathway. 

This pathway was selected because it is well-characterized, there are published TG studies, and a 

number of genes in the pathway have been identified. Criteria for chemical selection for the case 

study will include: 1) a relative abundance of available TG data; 2) a recent or ongoing EPA 

assessment; and 3) an interest by EPA Program and/or Regional Offices. Using the most recent 

or ongoing (depending on the selected chemical) EPA assessment as a starting point, the team 

members will conduct an evaluation of the data presented in the assessment, focusing on the 

hazard characterization and dose response sections. The following questions will be considered: 

Is the mode of action fully understood for all of the endpoints of concern? Could gene expression 

information aid in a further understanding of the MOA? Does the TG data provide insights into 

other aspects of the assessment (e.g., dose-response)? The TG data analysis will then be 

integrated into the assessment. A report of the case study will be developed and conclusions will 

be drawn about whether the TG data strengthened or corroborated the risk assessment, 

qualitatively, and the utility of the approach for incorporating TG data for future risk or health 

assessments. 
Progress to Date: Dibutyl phthalate (DBP) was selected among five candidate chemicals as the 

chemical for the case study. This decision was based on DBP having 1) the largest and most 

consistent toxicogenomics database including data indicating gene expression changes in genes 

known to be in the androgen-mediated male reproductive toxicity pathway, one dose-response 

gene expression study with low to high dose, and “phenotypic anchoring” (i.e., linkage between 

in vivo alterations and gene expression changes) for some of the gene expression data; 2) an 

ongoing IRIS assessment that allows our case study to address some broadly applicable 

questions about the use of toxicogenomics in risk assessment; and 3) an external review draft of 

the DBP IRIS assessment that our team could utilize. Two subgroups (Data and Approaches 

Subgroups) were formed to assess the toxicity data and the toxicogenomics data. The 

Approaches Subgroup has assessed the draft DBP IRIS assessment and associated toxicity 

studies. The toxicity database of studies with male reproductive and developmental effects has 

been assembled. The Data Subgroup has assembled the toxicogenomics database from eight 

publications (PCR and microarray studies) and summarized each of the studies. Possible 

questions to focus the case study were developed. For each question, its relevancy to the ongoing 

IRIS assessment and whether the available toxicogenomics data could address the question were 

considered. A collaboration with the STAR funded Bioinformatics Center at the Robert Wood 

Johnson Medical School University of Medicine & Dentistry of New Jersey (UMDNJ) 

Informatics Institute has been initiated. The collaborative project involves performing a genetic 

pathway analysis of the eight toxicogenomics studies. The results of this analysis may suggest 

additional pathways affected by DBP. In light of any additional genetic pathways affected by 

DBP treatment, the in vivo toxicity data will be further assessed. The team has begun writing 

drafts of chapters for the case study report. In addition to toxicogenomics and toxicity database 

assessments, our approach to integrating the toxicogenomics data into the DBP assessment is 

described. This approach may be useful to future health and risk assessments that utilize 

toxicogenomics data.

Milestones/Products: 

FY06: Draft of the case study report (includes scoping exercise complete for case studies; 

i.e., progress on case study and defining approach for integrating TG data into risk 

assessment); Discussions with the EPA chemical assessment team, Regions, and Program 

Offices. 

FY07: Conduct EPA Colloquium presenting results and lessons learned from the case 

study; External peer review draft of report; Submit manuscript on project to peerreviewed journal. 

QA: 

QA Plan is being evaluated
1

IIID-7 

Title: Developing Computational Tools for Application of Toxicogenomics to Environmental 

Regulations and Risk Assessment 

Lead: David Dix, NCCT (FTE 1.0) 

Research Issue and Relevance: Toxicogenomics is the study of changes in gene expression, 

protein, and metabolite profiles within cells and tissues, complementary to more traditional 

toxicological methods. Genomics tools provide detailed molecular data about the underlying 

biochemical mechanisms of toxicity, and could represent sensitive and precise approaches for 

detecting effects of exposures, or methods for comparing these effects between species or 

individuals. Thus genomics, proteomics and metabonomics can provide useful weight-ofevidence data along the source-to-outcome continuum, when appropriate bioinformatic and 

computational methods are applied towards integrating molecular, chemical and toxicological 

information. The Interim Policy on Genomics (http://www.epa.gov/osa/spc/genomics.htm) 

recognizes that if genomics is to become useful in regulatory decision-making, risk assessment, 

and environmental monitoring, the Agency will require the computational methods to handle 

such data. Measuring changes in gene expression using DNA microarrays has proven useful for 

identifying biological processes and informing hazard identification and mode of action in 

toxicological research. Similar microarray data have already arisen in Agency environmental 

decision-making, and regulatory applications of genomics are likely to increase. EPA’s Science 

Policy Council (SPC) paper on the Potential Implications of Genomics for Regulatory and Risk 

Assessment Applications at EPA (http://www.epa.gov/osa/genomics.htm) highlights the potential 

of toxicogenomics in chemical prioritization and risk assessment. To realize this potential, EPA 

must have the ability for proper analysis and storage, as well as the computational tools to 

incorporate these types of data into regulatory decisions. Development of these databases and 

tools, and application of these various toxicogenomic data within Program and Regional Offices 

will provide EPA staff with valuable, practical training in genomics and associated disciplines. 

As toxicogenomics grows more important to environmental science and policy, the NCCT will 

help EPA develop the computational tools and methods to properly evaluate genomics 

information 

Approach: To address the need for development of tools for managing and analyzing 

toxicogenomics data, the National Center for Computational Toxicology (NCCT) is working 

across the Office of Research and Development (ORD), the Program and Regional Offices of 

EPA, and with other Federal and extramural partners. The NCCT is coordinating it’s 

toxicogenomics efforts with the rest of the Agency through the SPC’s Genomics Technical 

Framework and Training Workgroup. This Workgroup has drafted an Interim Guidance for 

Microarray-Based Assays: Regulatory and Risk Assessment Applications at EPA, that 

recommends continued collaboration with other federal agencies and stakeholders in developing 

management and analysis tools for genomics data, and the execution of a series of case studies of 

genomics applications to chemical prioritization or risk assessment. The NCCT intends to 

follow these recommendations through a series of projects and partnerships within the Agency, 

and with the FDA and the STAR-funded Environmental Bioninformatic Centers (EBC) in NC 

and NJ. 
2

 First, the NCCT will develop a federated database(s) and analytical tools for the 

management and analysis of toxicogenomic data for both research and regulatory applications. 

This project was initiated in FY2006, and is building on the success of FDA’s ArrayTrack 

database. It is NCCT’s goal that this effort provides a complete data management solution that 

addresses requirements unique to scientifically-based risk assessments, confidential and 

proprietary data security, public access, and other aspects of regulatory application. Consistency, 

scientific and operational robustness, common access, and availability in a scalable environment 

are all part of these data management requirements. The expected result is an Agency-wide data 

management solution integrating genomics, toxicological, and other key data required for both 

research and regulatory applications. This EPA database containing gene expression profiles and 

toxicological data for a wide variety of chemicals will facilitate creation of the statistical and 

computational methods for predictive toxicology. As part of this effort to develop microarray and 

toxicogenomic analysis tools, the NCCT is continuing to participate in the Microarray Quality 

Control (MAQC) project. The MAQC is a comprehensive study of microarray quality control 

and cross-platform comparison, executed by a consortium of many commercial, government 

(FDA, EPA, NIST, NIH), and academic participants. The MAQC objectives include measuring 

intra-platform performance; inter-platform comparability; relative accuracy; and concordance of 

expression measurements to other technologies (e.g. TaqMan PCR). 

 The second part of NCCT efforts in toxicogenomics are a series of specific, model 

applications of toxicogenomics data to environmental chemical prioritizations or risk 

assessments. This includes some of the toxicogenomics elements of the ToxCast program for 

chemical prioritization, that are generating genomics and metabolomics data from cell cultures 

which can be loaded into the EPA toxicogenomics database. Also, toxicological data from EPA 

Program Offices for pesticides and other environmental chemicals will be captured into the 

toxicogenomic database for use for both chemical prioritization efforts (i.e., ToxCast) as well as 

risk assessment applications of toxicogenomics. Additional risk assessment applications of 

toxicogenomics data include ORD-wide work on the conazole fungicides cancer and non-cancer 

mode(s) of action; pyrethroid pesticides neurotoxicity; perfluorylalkyl acids (PFAA) 

developmental and hepatotoxicity; the antiandrogenicity of phthalates; the immunotoxicity of 

diesel particles; and the role of urban air particles in asthma. The NCCT will coordinate across 

ORD and the Program and Regional Offices, as well as with the EBC in NC and NJ, on how to 

manage and analyze these datasets in ways that maximize their utilization in risk assessments. 

Milestones/Products: 

FY06: Completion of participation in the Microarray Qaulity Control (MAQC) with FDA 

and publication of papers describing best practices- includes description of SPC 

Genomics Technical Framework. Installation of FDA ArrayTrack database for ORD and 

Agency use. 

FY07: Continued development of ArrayTrack database and analytical tools for 

toxicogenomics in cooperation across Agency, with FDA, and with the NC and NJ 

Environmental Bioinformatics Centers. Trial incorporations of toxicogenomic data into 

risk assessments in collaboration with various Program and Regional Office staff. 

 
3

FY08: Publication of examples and principles for integrating toxicogenomic data into 

risk assessments in peer-reviewed scientific journals and contribution of these principles into 

Agency science policy. 

QA: 

QA Plan is being evaluate 
1

IIIE-1 

Title: Dose-Time-Response Modeling for Evaluating Cumulative Risk of N-Methyl Carbamate 

Pesticides 

Lead: R. Woodrow Setzer, NCCT (FTE 0.4) 

Research Issue and Relevance: EPA’s Office of Prevention, Pesticides, and Toxic Substances 

(OPPTS) is required by the Food Quality Protection Act to completely reevaluate pesticide 

registrations by the end of August, 2006. This evaluation must include the evaluation of 

cumulative and aggregate risk of compounds that act through a common mechanism. One such 

group of pesticides is the N-methyl carbamates, whose common mechanism is the reversible 

inhibition of acetylcholine esterase (AChE) in nervous tissue. The inhibition of AChE is 

generally rapidly reversible, with half-lives in rodents at low doses of 1 – 3 hours or so. 

Accounting for episodic exposures of the carbamates, for example through the diet, then requires 

a more complex risk assessment model for combining the effects of multiple exposures (for 

example, an exposure of chemical A at breakfast, followed by an exposure of chemical B at 

lunch), than it would for exposures of compounds with longer-lived common effects. It also 

requires the estimation of parameters for models that describe both the dose-response and the 

recovery of AChE activity from multiple studies of the same dose response relationship. 

Approach: The approach to this problem is to adapt the dose-response model already used for 

modeling AChE activity after organophosphate exposure to include a model for recovery; to use 

a hierarchical model to account for the variability among studies, so that all the datasets for a 

chemical can be used to estimate dose-response parameters; to develop a model that accounts 

for AChE inhibition subsequent to multiple non-simultaneous exposures, using the parameter 

estimates derived from dose-response modeling. 

Progress to Date: A dose-time-response model has been developed that can be used both for 

modeling individual chemical dose-time-response data and for predicting AChE inhibition; 

preliminary parameter estimates have been generated. 

Impact: The dose-response results of this work will be used in the dose-response assessment 

portion of the Agency’s N-methyl carbamate cumulative risk assessment. Predictions of AChE 

inhibition under realistic exposure scenarios may be used in the hazard characterization portion 

of that risk assessment. Methods developed in this analysis could be used in the future for 

cumulative risk assessments for agents with ephemeral acute effects. 

Partnerships/Collaborations: This work is being done in collaboration with scientists in the 

Health Effects Division of OPPTS and from the Neurotoxicology Division of the National 

Health and Environmental Effects Research Laboratory. 

Milestones/Products: 

FY06 – Science Advisory Panel review of preliminary cumulative risk assessment, 

including dose-response modeling. 
2

FY07 – Release and Science Advisory Panel review of revised cumulative risk 

assessment. 

FY08 – Submission of methods and dose-response models for publication in the peerreviewed literature. Incorporation of dose-time-response methodology into the Agency’s 

BMDS software. 

QA: 

QA Plan is being evaluated 
1

IIIE-2 

Title: Application of Visual Analytic Tools to Evaluate Complex Relationships between 

Environmental Factors and Health Outcomes 

Lead: Elaine Cohen Hubal, NCCT (FTE 1.0) 

Research Issue and Relevance: Characterizing cumulative risk and understanding the complex 

relationships between environmental exposures and human health outcomes requires collection 

and analysis of a wide range of data. Information on the characteristics of multiple stressors 

(chemical, physical, biological and psychosocial), the characteristics of the human receptor 

(genetics, health status, life stage, behaviors, social factors, etc.) at multiple levels of 

organization (individual, community, population), and the temporal and spatial patterns of 

exposures and outcomes must be combined to assess cumulative risk. 

 Current approaches for designing studies and evaluating collected data tend to focus on a 

limited number of environmental factors and/or measures of outcome. As such, it is difficult to 

understand the potential impacts of the full range of factors on environmental health. More 

holistic approaches for interrogating this multidimensional data space are required to identify 

potentially important relationships for further study and analysis. Application of emerging 

computational tools will allow us to optimize utility of collected data, improve understanding of 

complex exposure-outcome systems, and improve risk assessment. 

Approach: In this project, we plan to use engineering principles to develop conceptual and 

mathematical models of the human-receptor, source-to-outcome system, and visual analytic tools 

to address the significant challenges associate with characterizing cumulative risks. First, we 

will apply a systems approach to develop a human-receptor based conceptual framework. We 

plan to adapt the strategy for creating conceptual models for complex ecological risk assessments 

presented by Suter (1999). 

 We will also identify and evaluate visual analytic tools required to address analysis needs 

for characterizing multi-factorial relationships between environmental factors and human health 

outcomes. Visual analytics is a new branch of visualization that merges scientific and 

information visualization and includes technologies from other fields, including information 

extraction, knowledge management, and statistical analysis. Visual analytics tools can be 

developed and applied to represent complex multidimensional data. Large, dynamic and 

complex data sets containing text, measurements, and images, can be effectively combined to 

reveal significant relationships and trends and to enhance discovery. Visual analytics can be 

used for outcome analysis and visualization, to find patterns and subtle relationships in data, and 

to infer rules that allow predictive analysis to prevent and mitigate environmental disease. 

 Finally, we will test and demonstrate VA tools using existing available data. We 

propose doing this demonstration using data from children’s cohort studies to explore the 

potential of visual analytics to facilitate evaluation of the effects of environmental exposures on 

child health and development. Another possible demonstration could involve using data 

collected for a community-based cumulative risk assessment. 
2

Progress to Date: 

 To date some preliminary (and very conceptual) research has been conducted to consider 

how biomonitoring data can be used to characterize cumulative risk and how psychosocial 

factors can be incorporated into cumulative risk assessments. In addition, a novel data mining 

tool is being tested and visual analytic software packages are being evaluated using extant 

children’s exposure data to identify relationships between exposure factors and body burden for 

a selected set of chemical agents. 

Impact: 

 Across EPA, program and regional offices are being called on to assess cumulative risk 

resulting from real-world exposures. The Agency is also being required to identify vulnerable 

populations, characterize life-stage risks, and evaluate gene-environment interactions. Multifactorial analyses of one form or another are required: to conduct national-scale regulatory-based 

risk assessments (program offices); to conduct community-based risk screening and remediation 

(regions and states); to support epidemiology studies investigating gene-environment interactions 

(interagency); and to characterize exposure and risk for public health tracking (all of the above). 

The approaches and tools developed through this research will help the Agency meet the 

increasingly complex needs for cumulative risk assessment. In addition, results of this effort 

may be used to develop concepts and tools for application to the Detroit Children’s Study, the 

North Carolina Cohort, and the National Children’s Study. 

Partnerships/Collaborations: 

 In this project, the NCCT will take advantage of visual analytic capabilities that are being 

developed in the Scientific Visualization Center at the EPA National Environmental Scientific 

Computing Center. In our initial evaluation of Visual Analytic software packages, we will build 

on statistical analysis of children’s exposure data conducted in NERL. We also hope to 

collaborate with investigators conducting children’s cohort studies to explore the potential of 

visual analytics to facilitate evaluation of the effects of environmental exposures on child health 

and development. As a first step in developing these collaborations, we presented this research 

concept during a breakout session on Computational Toxicology at the 2005 Collaborations for 

Children's Environmental Health Research Workshop (part of the EPA/NIEHS Children's 

Centers Scientist-to-Scientist Workshop, July 11, RTP). Finally, we are exploring a potential 

collaboration with PNNL’s National Visualization and Analytics Center. 

Milestones/Products: 

FY06 - Demonstration of potential for application of VA to evaluate exposure data. Workshop on 

applying VA to analyze children’s cohort data. 

FY07 - Generic conceptual model of complex relationships between environmental factors and human 

health outcomes. 

FY08 - Demonstration of application of VA to analyze children’s cohort data. 

QA: 

QA Plan is being evaluated and will be inserted in the next version. 







Intentionally Left Blank 
December 2005 

 - 1 - 

EPA Community of Practice: 

Biological Modeling Working Group (BMWG) 

The EPA Biological Modeling Working Group is proposed to be formed in Summer 2005 

to advance the principals for development and application of dosimetry and other 

biologically based models within the Agency. Dosimetry modeling includes multiple 

forms of toxicokinetic modeling (e.g., physiologically based toxicokinetic (PBTK) 

modeling, compartmental modeling), respiratory tract dosimetry modeling (e.g., 

computational fluid dynamics), and related modeling (e.g., dermal absorption modeling). 

The working group will also focus on biologically based response modeling with special 

emphasis on using the newest “omics” information in biologically based models . The 

goal of this work group is to foster adoption of modeling science by Agency clients in 

regulatory decision making. A cross-ORD group that also has representation from 

outside of ORD can carry great influence throughout the Agency, helping assure that 

ORD efforts are viewed as relevant and important to the Agency mission. 

The work group would include representatives of the ORD Labs and Centers with 

expertise in this area and have a representative from the Risk Forum. The BMWG would 

draw on the expertise of other scientists within ORD and the Agency as needed for 

specific issues. The BMWG would ask the Risk Forum to provide broader Agency-wide 

comment and review on specific matters as it determined necessary. Committee 

members would serve for a period of two years, following which the lab/center would 

renew their membership or appoint a new member. 

The charter of the BMWG includes the following functions: 

• Facilitate communication and co-ordination among the ORD biological modelers 

to foster their role as the major technical resource for the Agency in this area. 

• Develop consensus positions around issues for how analyses are conducted using 

models to provide consistency (e.g., processes for application of uncertainty 

factors in non-cancer evaluation, methods for using models to describe population 

distributions, durations over which AUC is calculated) 

• Develop consensus positions around issues of model evaluation and 

documentation to provide assistance to model developers inside and outside the 

Agency. 

• Develop recommendations for research needs to be addressed by ORD labs and 

centers (e.g., methods for statistically evaluating alternative model structures and 

characterizing model output uncertainties) 

• Facilitate the development of training materials and programs for chemical 

managers and risk assessors that assist in the implementation of these 

technologies. 

• As the science advances develop recommendations and guidance on the use of 

omics information to enhance dosimetry and pharmacodynamic models 

• Facilitate communication to the larger modeling and scientific on issues for 

modeling and applications to risk assessment (e.g., SOT symposia, specialty 

meetings) 
December 2005 

 - 2 - 

. 

The work group will: 

• Hold meetings: quarterly to identify issues/projects to be taken on and discuss 

progress 

• Organize internal meeting/conference calls around specific issues/projects 

with goal of identifying state of the science, research needs, consensus 

positions. 

• Prepare publications for peer-reviewed literature on specific issues/topics. 

• Identify research needs that could be addressed internally or through 

extramural contracts or grants. 

• Organize outreach to broader modeling and scientific community to 

demonstrate EPA’s interest and obtain input and participation 

• Report regularly (every six months) to the ORD science council on issues, 

progress, and outputs 

• The group’s representative from the Risk Assessment Forum will request time 

on the Forum’s Agenda to brief regular Forum members of issues, progress, 

and outputs. 
November 28, 2005 

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EPA Categorization and Prioritization Community of Practice 

The EPA Categorization and Prioritization Community of Practice (CPCP) formed in 

December 2005 to advance research into the utility of computational chemistry, highthroughput screening (HTS) and various toxicogenomic technologies for Agency use. 

Modern computational chemistry and molecular biology technologies can provide 

information about the physical and biological properties of large numbers of chemicals. 

The goal of the CPCP is to advise on the development of a research program within the 

National Center for Computational Toxicology (NCCT) generating data from these 

technologies and interpreting it in order to categorize chemicals and predict toxicity. If 

proven accurate, these toxicity predictions could then be used for prioritization of limited 

testing resources towards chemicals and endpoints that present the greatest risk to human 

health and the environment. 

The primary function of the CPCP is to advise on the NCCT’s planning, conduct and 

interpretation of a chemical categorization and prioritization research project entitled 

“ToxCast”. The ToxCast project will be designed to have the ability to predict, or 

forecast toxicity. Initially, this would entail a demonstration project based upon a set of 

chemicals with a rich toxicological database (e.g., registered pesticides, or the chemicals 

tested in the NTP bioassay program). This set of 200 or more chemicals would represent 

a number of differing structural classes and phenotypic outcomes (e.g., tumorigens, 

developmental and reproductive toxicants, neurotoxicants, immunotoxicants). The 

ToxCast project would evaluate chemical properties and effects across a broad spectrum 

of information domains: physical-chemical properties, predicted biological activities 

based on existing structure-activity models, biochemical properties based on HTS assays, 

cell based phenotypic assays, and genomic analysis of cells or organisms. The ultimate 

goal of the ToxCast project would be to mine the resulting data for associations between 

and among the various domains and the known toxicological properties of the base set of 

chemicals, in order to provide a structured strategy to categorize chemicals, identify 

potential toxicities and pathways, and to prioritize chemicals for subsequent testing based 

on that information. 

The Community would include individuals from the Office of Research and 

Development (ORD) Labs and Centers, as well as scientists outside of EPA with 

expertise in HTS, toxicogenomics, predictive toxicology or bioinformatics. The CPCP 

would be chaired by a member of the NCCT, but draw on the expertise of scientists 

across ORD and the Agency as needed for specific issues. The Community would ask 

ORD’s Computational Toxicology Implementation Steering Committee (CTISC) to 

provide broader Agency-wide comment and review on specific matters as necessary. 

Community members would serve for the period required for planning and execution of 

the ToxCast demonstration project. It is expected that the CPCP would work closely 

with the Chemoinformatics Community of Practice (chaired by Ann Richard) on issues 

relevant to both groups. 

The charter of the CPCP includes the following functions: 
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1. Organize periodic meetings or conference calls (at least quarterly) around specific 

issues or projects, with goal of identifying state of the science, research needs, and 

consensus positions. 

2. Organize outreach to broader HTS and toxicogenomics scientific community to 

demonstrate EPA’s interest and obtain input and participation. 

3. Report regularly to the Director of the NCCT and the ORD CTISC on issues, 

progress, and outputs. 

Specific goals of the CPCP are to advise on the following: 

1. Development of key partnerships and collaborations with external groups that can 

facilitate development of the information needed in ToxCast. These groups 

include the Office of Air and Radiation (OAR), Office of Prevention Pesticides 

and Toxic Substances (OPPTS), Office of Water (OW); the NTP/NIEHS and 

NIH/MLI; the ACC, CropLife, EDF, and other external groups to help develop a 

consensus on the specific directions and contents of ToxCast. 

2. Identification of a set of chemicals for the ToxCast demonstration project. 

3. Selection of data domains and specific assays based upon pre-existing knowledge 

and within the available resources. 

4. Selection of key target toxicities for initial focus of the ToxCast proof of concept. 

5. The impact of metabolizing capability, or lack thereof, on the efficiency of the 

screening assays. 

6. Development of a bioinformatic approach to mining the resulting data and 

identifying signatures of concern. 

7. Reporting the utility of assay results and analysis techniques to categorize pilot 

chemicals according to known toxicity patterns; revise methods and approaches as 

dictated by results. 

8. Expanding the ToxCast project beyond proof of concept, and carrying out a 

prospective assessment of the approach using chemicals currently entering a 

traditional testing process. 

Impact: The availability of a biologically and chemically based system (ToxCast) to 

categorize chemicals of like properties and activities will provide a number of EPA 

Program Offices with an extremely useful tool that heretofore has been seriously lacking. 

ToxCast may be one of the first broad-scale products of the NCCT that addresses the 

mission of improving the efficiency and effectiveness of hazard identification and risk 

assessment methodologies employed by the EPA. 
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EPA Communities of Practice: 

Chemoinformatics 

The Chemoinformatics workgroup is proposed to be formed in Summer 2005 to 

facilitate, coordinate and integrate efforts to address the challenges of chemical structure 

annotation (or indexing), retrieval, and mining of chemically-related data and documents, 

including newer toxicogenomics and metabonomics data, across EPA Program Offices, 

Labs and Centers. Much of EPA’s public and internal chemical data records and 

databases currently are indexed only by chemical name (imprecise and non-unique) and 

CAS registry numbers (proprietary) and are not searchable by the more universal and 

informative metric of chemical structure. Additionally, there is unnecessary duplication 

of efforts and lack of coordination in the area of chemical structure-annotation and 

quality review of chemical information (e.g., structures, CAS) across diverse EPA 

databases. Finally, no Agency-wide chemical structure searching capability currently 

exists to enable EPA scientists, regulators, and outside parties to efficiently and precisely 

locate Agency chemical information based on chemical structure. 

The Chemoinformatics workgroup would invite members of various Offices, Labs and 

centers currently involved in either chemical database construction or use, to coordinate 

ongoing efforts in the area of chemical-structure annotation and structure-searchability of 

EPA records and data, and to help chart a path forward for expanded Agency-wide 

adoption of capabilities in these areas. One of the first tasks of this workgroup would be 

to consider and refine the elements of the draft charter below, and create a finalized 

charter for the workgroup. 

A draft charter of this Chemoinformatics workgroup is suggested to include the following 

functions: 

• Facilitate communication and co-ordination among the Agency personnel who are 

directly using, developing, or managing chemical information data records, 

including those associated with newer toxicogenomics and metabonomics 

technologies, particularly in ORD, OPP and OPPT whose duties involve chemical 

searching and structure-activity assessments. 

• Propose adoption of application-independent standards for chemical structure 

representation (including information pertaining to the characterization of 

mixtures, racemic chemicals, polymers, and stereochemistry) that will facilitate 

the broadest possible utility and compatibility of such information, both within 

and outside of EPA. 

• Guide the creation of a consolidated EPA database of chemical identification 

information (structure, CAS, name), obtained from public sources 

(chemfinder.com, PubChem, NLM ChemID) but having undergone additional 

levels of quality consistency review, to serve as a common resource for the 

structural annotation of EPA records or databases. [Note that physical/chemical 

properties can be measured or derived from the accurate chemical structures by a 

variety of algorithms and means; hence, such property annotation of chemical 
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information resources will be considered as a separate and distinct task of 

chemical annotation not included in the initial charter of this workgroup.] 

• Create procedures and guidelines for chemical annotation of Agency data and 

records, as an adjunct to EPA Information Quality Guidelines, to improve 

consistency, quality, and efficiency related to storage, retrieval and mining of 

chemical information within the Agency. 

• Exchange information and experiences regarding available public and commercial 

approaches and initiatives in the areas of chemical structure representation, 

chemical searching, and managing and modeling of chemical data. 

• Evaluate and recommend which of the available approaches and initiatives might 

be adopted or coordinated to best serve EPA’s long-term needs in this area, and 

serve as an information resource to other Agency personnel on these matters. 

• Evaluate and provide guidance concerning the needs and requirements of an 

Agency-wide chemical structure-searching capability, possibly from publicly 

available or open-source parties, that would offer low-cost, flexible solutions to 

enable EPA scientists or others to locate records or data pertaining to chemical 

information on EPA’s intranet or internet websites. 

• Facilitate the development of training materials and programs for scientists, 

chemical managers and risk assessors that assist in the implementation and wider 

use of these chemical information resources and technologies. 

• Report regularly (every six months) to the ORD science council on issues, 

progress, and outputs 

Office/Division/Branch managers across various research offices, including ORD, OPP 

and OPPT, will be invited to appoint representatives to this workgroup who would 

benefit most from, and contribute most to these discussions, as well as who would serve 

as a conduit of information to and from their respective Branch/Division/Office.