Download REVIEW Computational Toxicology—A State of

TOXICOLOGICAL SCIENCES 103(1), 14–27 (2008)
doi:10.1093/toxsci/kfm297
Advance Access publication December 7, 2007
REVIEW
Computational Toxicology—A State of the Science Mini Review
Robert J. Kavlock,*,1 Gerald Ankley,† Jerry Blancato,* Michael Breen,‡ Rory Conolly,* David Dix,* Keith Houck,*
Elaine Hubal,* Richard Judson,* James Rabinowitz,* Ann Richard,* R. Woodrow Setzer,* Imran Shah,*
Daniel Villeneuve,† and Eric Weber‡
*National Center for Computational Toxicology; †National Health and Environmental Effects Research Laboratory; and ‡National Exposure Research
Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
Received October 5, 2007; accepted December 5, 2007
Advances in computer sciences and hardware combined with
equally significant developments in molecular biology and
chemistry are providing toxicology with a powerful new tool
box. This tool box of computational models promises to increase
the efficiency and the effectiveness by which the hazards and risks
of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types
of biological interactions that can be evaluated. In addition,
knowledge of toxicity pathways gathered within the tool box will
be directly applicable to the study of the biological responses
across a range of dose levels, including those more likely to be
representative of exposures to the human population. Progress in
this field will facilitate the transformative shift called for in the
recent report on toxicology in the 21st century by the National
Research Council. This review surveys the state of the art in many
areas of computational toxicology and points to several hurdles
that will be important to overcome as the field moves forward.
Proof-of-concept studies need to clearly demonstrate the additional predictive power gained from these tools. More researchers
need to become comfortable working with both the data generating tools and the computational modeling capabilities, and
regulatory authorities must show a willingness to the embrace new
approaches as they gain scientific acceptance. The next few years
should witness the early fruits of these efforts, but as the National
Research Council indicates, the paradigm shift will take a long
term investment and commitment to reach full potential.
1
To whom correspondence should be addressed at B-205-01, National
Center for Computational Toxicology, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
Fax: 919-541-1194. E-mail: [email protected].
This mini review is based on presentations and discussions at the
International Science Forum on Computational Toxicology that was
sponsored by the Office of Research and Development of the U.S.
Environmental Protection Agency and held in Research Triangle Park, NC
on May 21–23, 2007. The complete agenda and copies of the individual
presentations from the Forum are available on the Internet (www.epa.gov/ncct/
sciforum).
Published by Oxford University Press 2007.
Key Words: bioinformatics; biological modeling; QSAR;
systems biology; cheminformatics; high throughput screening;
toxicity pathways.
Computational toxicology is a growing research area that is
melding advances in molecular biology and chemistry with
modeling and computational science in order to increase the
predictive power of the field of toxicology. The U.S.
Environmental Protection Agency (U.S. EPA) defines computational toxicology as the ‘‘integration of modern computing
and information technology with molecular biology to improve
Agency prioritization of data requirements and risk assessment
of chemicals’’ (U.S. EPA, 2003). Success in this area would
translate to greater efficiency and effectiveness in determining
the hazards of the many environmental stressors that must be
dealt with, and deciding what types of information are most
needed to decrease uncertainties in the protection of human
health and the environment. Computational toxicology differs
from traditional toxicology in many aspects, but perhaps the
most important is that of scale. Scale in the numbers of
chemicals that are studied, breadth of endpoints and pathways
covered, levels of biological organization examined, range of
exposure conditions considered, and in the coverage of life
stages, genders, and species. It will take considerable progress
in all these areas to make toxicology a broadly predictive
science. Key advances leading the field include construction
and curation of large-scale data repositories necessary to
anchor the interpretation of information from new technologies; the introduction of virtual and laboratory-based highthroughput assays on hundreds to thousands of chemicals per
day and high-content assays with hundreds to thousands of
biological endpoints per sample for the identification of
toxicity pathways; and the latest advances in computational
modeling that are providing the tools needed to integrate information across multiple levels of biological organization for
characterization of chemical hazard and risk to individuals and
COMPUTATIONAL TOXICOLOGY
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TABLE 1
Tasks Identified by the National Research Council (2007) in Each Main Topic Area that are Necessary to Transform Toxicity Testing
from the Current Animal-Model Based Approach to One that is more Reliant on In Vitro Systems to Detect and Characterize Toxicity
Pathways of Concern
Population-based and human-exposure data
Develop novel approaches to gather exposure data needed for making
hazard ID and risk assessment decisions.
Chemical characterization
Environmental chemicals would be first characterized for a number of properties
related to environmental distribution, exposure risk, physicochemical properties.
Toxicity pathway characterization
Toxicity pathways describe the key details of modes and mechanisms at a molecular level.
By characterizing these and developing relevant in vitro assays, one can make definitive
statements about the potential hazards posed by chemicals being tested.
Targeted testing
In many cases, even when it is known what toxicity pathways are activated by a chemical,
it will be necessary to perform specialized or targeted tests, for instance to determine
dose–response relationships. The targeted testing phase may continue to use animal models.
Dose–response and extrapolation modeling
Increasingly accurate and predictive computer models need to be developed to make use
of the information derived from the earlier phases and to aid in making regulator decisions.
populations. Collectively, these advances reflect the wave of
change that is sweeping and reinvigorating toxicology, just in
time to facilitate the vision of toxicology in the 21st century
that was recently released by the National Research Council
(NRC) of the National Academy of Science (National Research
Council, 2007). The NRC report’s overall objective is to foster
a transformative paradigm shift in toxicology based largely on
the use of in vitro systems that will (1) provide broad coverage
of chemicals, chemical mixtures, outcomes, and life stages; (2)
reduce the cost and time of testing; (3) use fewer animals and
cause minimal suffering in the animals used; and (4) develop
a more robust scientific base for assessing health effects of
environmental agents. The report describes this effort as one
that will require the involvement of multiple organizations in
government, academia, industry, and the public. This mini
review describes advances that are now occurring in many of
the areas that are contributing to computational toxicology, and
is organized along the dimensions outlined by the National
Research Council (2007). The principle tasks outlined in the
NRC report are presented in Table 1, and each relevant aspect
of computational toxicology is discussed accordingly.
CHEMICAL CHARACTERIZATION
Chemical characterization involves the compilation of data
on physical and chemical properties, uses, environmental
surveillance, fate and transport, and properties that relate to the
potential for exposure, bioaccumulation, and toxicity (National
Research Council, 2007).
Predicting the Environmental Fate and Transport of
Chemical Contaminants
The ability to conduct chemical exposure and risk assessments is dependent on tools and models capable of predicting
environmental concentrations. As the size (currently > 80,000
chemicals) and diversity of the regulated chemical universe
continues to increase, so does the need for more sophisticated
tools and models for calculating the physical–chemical properties necessary for predicting environmental fate and transport.
This need is further driven by the increasingly complex array of
exposure and risk assessments necessary to develop scientifically defensible regulations. As this modeling capability increases in complexity and scale, so must the data inputs. These
new predictive models will require huge arrays of input data,
and many of the required inputs are neither available nor easily
measured.
Currently, the Estimation Program Interface Suite (EPI
Suite) is the primary modeling system utilized within U.S. EPA
for providing estimates of the common physical–chemical
properties necessary for predicting chemical fate and transport
such as octanol/water partition coefficients, water solubility,
hydrolysis rate constants, and Henry’s law constants (http://
www.epa.gov/oppt/exposure/pubs/episuite.htm). The EPI Suite
calculators are based primarily on a fragment constant approach that has been validated with an independent set of
chemicals. In general, the EPI Suite predicts physical–chemical
properties within an order of magnitude, which is normally
sufficient for screening level regulatory assessments.
The limitations of the EPI Suite calculators (e.g., inability to
calculate ionization constants (pKas) and transformation rates
constants beyond hydrolysis) require the use of other computational methods for meeting data needs. SPARC Performs
Automated Reasoning in Chemistry (SPARC) uses computational algorithms based on fundamental chemical structure
theory (i.e., a blending of linear free energy [LFER] to compute
thermodynamic properties and PMO theory to describe quantum effects) to estimate numerous physical–chemical properties (Hilal et al., 2005; Whiteside et al., 2006). The power of
the tool box is its ability to couple whole molecule and sitespecific chemistry to calculate new properties. For example,
pKa and property models are coupled to calculate tautomeric
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equilibrium constants; and pKa, hydrolysis, and property
models are coupled to calculate complex macro pKa’s where
ionization, hydrolysis, and tautomerization may couple to yield
very complex apparent pKa’s. This capability is essential for
calculating physical–chemical properties of organic chemicals
with complex chemical structures that contain multiple ionizable functional moieties, such as many of the pharmaceuticals
that are being detected in the effluents of many waste water
treatment plants.
In addition to the more traditional computational approaches
such as the fragment constant approach and LFER, quantum
mechanical calculators coupled with aqueous solvation models
are also finding increasing applications in predicting physical–
chemical properties for predicting chemical reactivity (Lewis
et al., 2004) and for investigating reaction mechanisms for
transformation processes of interest such as reductive transformations (Arnold et al., 2002).
Tools for predicting transformation kinetics and pathways
are quite limited, particularly with respect to biological processes. The EPI Suite and SPARC calculators have limited
capability for the calculation of hydrolysis rate constants, and
currently have no ability to calculate biodegradation rate
constants. CATABOL is an expert system that begins to fill this
gap by predicting biotransformation pathways and calculating
probabilities of individual transformations (Jaworska et al.,
2002). The core of CATABOL is a degradation simulator,
which includes a library of hierarchically ordered individual
transformations (abiotic and enzymatic reactions). It also
provides the magnitude and chemical properties of the stable
daughter products resulting from biodegradation.
The future development of models for predicting the environmental fate and transport of chemical contaminants is driven primarily by the need for multimedia and multipathway assessments
over broad spatial and temporal scales. Geographic information
system–based technologies will be required for accessing,
retrieving, and processing data contained in a wide range of
national databases maintained by various government agencies.
Toxico-Cheminformatics
The term ‘‘Toxico-Cheminformatics’’ encompasses activities
designed to harness, systematize, and integrate the disparate
and largely textual information available on the toxicology and
biological activity of chemicals. These data exist in corporate
archives, published literature, public data compilations, and in
the files of U.S. government organizations such as the National
Toxicology Program (NTP), U.S. EPA, and the U.S. Food and
Drug Administration. Data mining approaches and predictive
toxicity models that can advance our ability to effectively screen
and prioritize large lists of chemicals are dependent upon the
ability to effectively access and employ such data resources.
The National Center for Biotechnology Information
(NCBI)’s PubChem project (http://pubchem.ncbi.nlm.nih.gov/)
is a large, public chemical data repository and open search/
retrieval system that links chemical structures to bioassay data.
PubChem has become an indispensable resource for chemists
and biologists due to its wide coverage of chemical space (> 10
million structures) and biological space (> 500 bioassays),
structure-searching and analysis tools, and linkages to the large
suite of NCBI databases (http://www.ncbi.nlm.nih.gov). PubChem includes data for the NCI 60 cell line panel, used by the
NCI Developmental Therapeutics Program to screen more than
100,000 compounds and natural products for anticancer
activity and providing a rich data resource for a comprehensively characterized set of cells. Weinstein (2006) has incorporated these data into a fully relational, public resource titled
‘‘CellMiner,’’ and coined the term ‘‘integromics’’ to convey the
highly flexible functionality of this system for chemical/
biological profiling, spanning genomics, high-throughput
screening (HTS), and chemical information domains. Contributing to efforts in data standardization and access, U.S. EPA is
creating a large relational data warehouse for chemical and
toxicity data from various public resources. This Aggregated
Computational Toxicology Resource is designed to support
flexible data mining and modeling efforts across a wide range
of biological information domains and the new U.S. EPA
ToxCast program (Dix et al., 2007).
With HTS approaches being increasingly applied to toxicology data sets, such as represented by the NTP HighThroughput Testing Program (National Toxicology Program
High-Throughput Screening Program, 2006), come challenges
to determine the most effective means for employing such data
to improve toxicity prediction models. Anchoring large matrices of HTS activity data to relatively sparse phenotypic
endpoint data across chemical compound space presents a fundamental challenge. Yang (2007) has demonstrated the value
of linking bioassays with toxicity endpoints via the structural
feature dimension, rather than the compound level, generating
matrices to determine correlation of bioassays with toxicity.
This paradigm addresses the practical problem of the sparse
data space and allows quantitative multivariate analysis.
These toxico-cheminformatics tools and public resources are
evolving in tandem with increasing legislative pressures within
the United States, Europe, and Canada to prioritize large lists of
existing chemicals for testing and/or assessment. Health
Canada has been the first to fully implement a tiered Hazard
ID and Exposure Assessment evaluation process relying upon
weight-of-evidence consideration of existing data and results
of toxicity prediction models, and structure-analog inferences
(Health Canada, 2007). The approach is pragmatic and
transparent, relying upon existing capabilities and technologies, and was successfully employed to prioritize the Domestic
Substance List inventory of 23,000 chemicals by the
legislatively mandated deadline under the Canadian Environmental Protection Act of September 2006. This approach will
greatly benefit from advances in toxico-cheminformatics, and
will influence other governmental agencies as they struggle
with similar mandates for prioritizing large lists of chemicals.
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COMPUTATIONAL TOXICOLOGY
Molecular Modeling Methods as a Virtual Screening Tool for
the Assessment of Chemical Toxicity
Molecular modeling methods provide an approach for
estimating chemical activity when the relevant data is not
available. When used in this way it becomes an important tool
for screening chemicals for toxicity and hazard identification.
Computational molecular methods may also be applied to
model toxicity pathways when some of the relevant experimental data are unavailable. As noted above, some of these
methods have been used to estimate various physical and
chemical properties of the molecules relevant to environmental
fate and transport. Other molecular modeling methods may be
applied to simulate critical processes in specific mechanisms of
action involved in toxicity. An initial and often differential step
in many of these mechanisms of action requires the interaction
of the molecular environmental contaminant, or one of its
descendants, with a (macro)molecular target. An element of
a virtual screen for potential toxicity may be developed from the
characterization of these toxicant–target interactions. One large
and important subset of target–toxicant interactions is the
interaction of chemicals with proteins. Many computational
approaches for screening libraries of molecules for pharmaceutical application have been developed. These methods also may
be applied to screen environmental chemicals for toxicity, but the
differing requirements of these two similar problems must be
considered. For example, screening of environmental chemicals
requires minimizing false negatives, whereas drug discovery only
requires the identification of some of the most potent chemicals,
which can yield a significant number of false negatives.
Molecular modeling methods that incorporate both the
structure of the protein target and/or that of known ligands
have been used to investigate nuclear receptor and cytochrome
P450 targets. In addition to the ligand binding site, features on
the protein surface, such as the Activation Function 2 site or
other coactivator and corepressor regions of the Human
Pregnane X Receptor, are potential sites for interference by
environmental chemicals (Wang et al., 2007). Methods that
map the binding of functional groups from chemicals to protein
surfaces and binding sites have been developed (Kaya et al.,
2006; Sheu et al., 2005). These maps of the favorable positions
of molecular substructures provide fragment libraries to
which chemicals may be fitted and their suitability for binding
evaluated. Current studies have demonstrated the importance of
the motion of the target for ligand binding, protein function,
and subunit assembly. Local motion of the amino acids in the
binding site provides the flexibility to allow the potential ligand
to sculpt the ligand binding domain. Concepts that incorporate
protein flexibility to identify binding modes of toxicological
interest are being developed (Lill et al., 2006; Vedani et al.,
2006). This technology combines structure-based molecular
docking with multidimensional quantitative structure activity
relationships. Global modes of protein motion have been found
to influence protein function by affecting binding and subunit
assembly (Wang et al., 2007). Metabolizing enzymes present
potential targets for clearance of chemicals as well as activation
that could result in toxicity. Understanding the relationship
between structure and function for P450 serves to illuminate both
of these issues that are relevant for assessing the effects of chemicals. Pharmacophores and quantitative structure activity relationships have been developed for the various CYPs (Jolivette and
Ekins, 2007), and machine learning methods have been developed
to predict metabolic routes (Ekins et al., 2006). These approaches
will allow relatively rapid and comprehensive coverage of the
interaction of chemicals with multiple macromolecules, thus
complementing results from HTS assays (see below).
TOXICITY PATHWAYS
Toxicity pathways represent the normal cellular responses
that are expected to result in adverse health effects when
sufficiently perturbed by chemical exposure (National Research
Council, 2007). A wide variety of in vitro and in vivo tools are
being developed to identify critical toxicity pathways.
Application of Drug Discovery Technologies in
Environmental Chemical Prioritization
Strategies for investigating the toxicity of environmental
chemicals have changed little over many years and continue to
heavily rely on animal testing. However, recent advances in
molecular biology, genomics, bioinformatics, systems biology,
and computational toxicology have led to the application of
innovative methods toward more informative in vitro
approaches. The application of quantitative, HTS assays is
a key method. Originally developed for use in drug discovery
by the pharmaceutical industry, these assays quantify molecular
target-, signaling pathway-, and cellular phenotype-focused
endpoints with capacity to evaluate thousands of chemicals in
concentration–response format. As an example, National
Institutes of Health (NIH) Chemical Genomics Center has
built an infrastructure for robust, quantitative, HTS assays
(Inglese et al., 2006) that is currently being used to screen
thousands of environmental chemicals for a variety of
toxicology-related endpoints. This project utilizes data provided
by the NTP’s HTS Initiative (http://ntp.niehs.nih.gov/index.
cfm?objectid¼05F80E15-F1F6-975E-77DDEDBDF3B941CD)
and U.S. EPA’s ToxCast Program (Dix et al., 2007).
HTS using cellular assays offers perhaps the greatest hope
for transformation of the current toxicity testing paradigm.
Such systems incorporate comprehensive, functioning, cellular
signaling pathways, the disturbance of which by environmental
chemicals would suggest a potential for toxicity. Development
of high-content screening (HCS) platforms consisting of
automated, fluorescence microscope imaging instruments and
image analysis algorithms greatly facilitated quantitation of
chemical perturbations of cell signaling pathways and vital
organelle function on a single cell basis. As an illustration of
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the utility of this approach, human liver toxicants with a variety
of mechanisms of action were detected with both good sensitivity and specificity through screening multiple endpoints
such as nuclear area and cell proliferation in a human liver cell
line (O’Brien et al., 2006). This approach also is useful in
examining effects of new classes of chemicals (e.g., nanomaterials) for potential toxicity by reporting effects on toxicityassociated endpoints and allowing visual appreciation for
novel, and perhaps unexpected, effects on cellular morphology
and function (Ding et al., 2005). With an eye toward reproducing normal physiology in vitro to the greatest extent
possible, Berg et al. (2006) established coculture systems of
primary human cells and developed assays that measure many
endpoints encompassing a wide variety of signaling pathways.
Screening of pharmacological probes in these assays demonstrated similar behavior of chemicals related by mechanism
of action, thus providing a system potentially useful for
understanding mechanisms of toxicity. Although HCS was not
used in this application, the marriage of complex, primary
human cell cultures with HCS analysis is a likely, and highly
valuable, development in the field of toxicity screening. HTS
approaches do have imposing hurdles to overcome, however,
including volatile or aqueous insoluble environmental chemicals, need for inclusion of biotransformation capacity in the
in vitro test systems, the myriad of potential toxicity pathways
that must be covered, the likelihood of cell-type dependent
activity, and the probability of dependence of some mechanisms of toxicity on higher level interactions not found in cell
culture systems (Houck and Kavlock, 2007).
The HTS and HCS methods described are all data-intensive
and require computational approaches to analyze and properly
interpret. The high dimensionality of the data may require novel
statistical approaches. Results are likely to be used in building
models that predict the potential for toxicity for new chemicals
based on their behavior in in vitro assays. In addition, screening
results integrated into systems biology models should lead to
insights into mechanisms of action that will be invaluable for
risk assessment. Validation and harmonization of protocols at
the international level should result in a much more efficient and
comprehensive safety net for hazardous chemical protection,
and greatly reduce the number of laboratory animals needed to
accomplish this (Hartung, 2006).
Using Genomics to Predict Potential Toxicity
Transcriptomics is a useful approach for understanding the
interactions of chemicals with biological targets, and can
complement the HTS assays used for bioactivity profiling.
Using bioactivity profiles to accurately predict toxicity and
prioritize chemicals for further testing would allow for the
focusing of resources on greater potential hazards or risks.
Prioritization efforts to which genomics data might contribute
include U.S. EPA’s voluntary high production volume (HPV)
program, wherein chemicals manufactured in large amounts are
identified and hazard characterized according to chemical
category. Genomics is being developed as part of a suite of
tools to help confirm the category groupings of HPV chemicals,
and identify which chemicals or chemical categories may
present greater hazard or risk. The U.S. EPA is actively
developing the methods, policies, and infrastructure for using
genomics data in such a regulatory context (Dix et al., 2006).
In vitro toxicogenomics methods are being developed and
evaluated for toxicity prediction and for addressing fundamental questions about the ability to identify toxicity pathways for
large numbers of chemicals in a number of research programs
in the United States, Europe, and Asia. The throughput,
molecular specificity, and applicability of this approach to
human cell systems are highly consistent with the goals and
directions described in the NRC report on the future of toxicity
testing (National Research Council, 2007).
Genomic signatures predictive of toxicological outcomes
have been derived from in vivo studies, and the evaluation and
application of these signatures to hazard identification and
risk assessment is an area of active research. Perhaps most
significantly, genomic signatures predicting tumor incidence in
2-year rodent cancer bioassays have the potential to provide
shorter-term tests as an alternative to the expensive two-year
rodent bioassay. The ability to predict chemically induced
increases in lung tumor incidence based on gene expression
biomarkers has been demonstrated in microarray studies
performed on mice exposed for 90 days to chemicals that
were previously tested by the National Toxicology Program
(Thomas et al., 2007). In an even shorter 5-day study design,
liver gene expression data from rats treated with structurally
and mechanistically diverse chemicals was used to derive
a genomic signature that predicted nongenotoxic liver tumorigenicity in the 2-year bioassay (Fielden et al., 2007). In both
of these studies, sensitivity and specificity of the genomic
signatures was high, and the signatures provided accurate
predictions and identified plausible modes of action. Both the
Thomas et al. and the Fielden et al. data sets are being utilized
in the Microarray Quality Control assessment of best practices
in developing and validating predictive genomic signatures (http://
www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/).
Success in developing predictive genomic signatures from
in vitro studies has been more modest, to date, than what has
been accomplished using in vivo data. Gene expression profiles
for more than 100 reference compounds in isolated rat
hepatocytes have been used to derive predictive signatures
identifying potential mitochondrial damage, phospholipidosis,
microvesicular steatosis, and peroxisome proliferation, with
a high degree of sensitivity and specificity (Yang et al., 2006).
A large European Union program project entitled carcinoGENOMICS (http://www.carcinogenomics.eu/) was initiated in
2006 to develop genomics-based in vitro screens predictive of
genotoxicity and carcinogenicity in the liver, kidneys, and
lungs. In vitro toxicogenomics is also part of U.S. EPA’s
ToxCast research program, which is being designed to forecast
COMPUTATIONAL TOXICOLOGY
toxicity based on genomic and HTS bioactivity profiles (Dix
et al., 2007; http://www.epa.gov/comptox/toxcast/). The initial
goal of these in vitro toxicogenomic efforts is hazard prediction
and chemical prioritization for subsequent in vivo testing, but
the ultimate goal goes beyond refinement to actually replacing
in vivo testing. This will require a sustained, systematic, and
substantial effort on the part of government, academic,
industry, and nongovernmental organization partners.
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knowledge-based and data-driven approaches will aid in organizing and refining biological insight on perturbations leading
to adverse outcomes. Second, dynamic simulation of these
mechanisms will help in predicting dose-dependent response.
This will reduce the scope of animal testing and the time
required for understanding the risk of toxic effects due to
environmental chemicals.
Systems Biology Models of the HPG Axis
Signaling as a Determinant for Systems Behavior
Understanding processes at the molecular, cellular, and
tissue levels is an ongoing challenge in toxicology. Central to
this hierarchy of biological complexity is the field of signal
transduction, which deals with the biochemical mechanisms
and pathways by which cells respond to external stimuli.
Computational systems approaches are critical for mechanistic
modeling of environmental chemicals to predict adverse outcomes in humans at low doses.
For decades, computational modeling has complemented
laboratory-based biology with in silico experiments to generate
and test mechanistic hypotheses. Computational approaches
have been used to model biological networks as dynamical
systems in which the quantitative variation of molecular entities are elucidated by the solution of differential equations
(Aldridge et al., 2006). Such models of signaling networks
have been used to predict the dynamic response at molecular
(Behar et al., 2007), cellular (Sasagawa et al., 2005), and tissue
levels (Schneider and Haugh, 2006). Postgenomic, large-scale
biological assays present new challenges and opportunities for
modeling signaling networks. Though large-scale data provide
a global view of a biological system, they remain difficult
to utilize directly in traditional dynamic models. This has
stimulated research on alternative formalisms for modeling
pathways (Fauré et al., 2006). In addition, concurrent measurements on thousands of proteins, genes, and metabolites in
response to stimuli, or in different disease states, enable the
‘‘reverse-engineering’’ of biological networks from data using
empirical methods (D’haeseleer et al., 2000).
Synthesizing disparate information into coherent mechanistic hypotheses is an important challenge for modeling toxicity
pathways. Knowledge-based approaches (Karp, 2001) provide
an avenue for efficiently managing the magnitude and complexity of such information. Through such techniques, largescale biological interaction data can be algorithmically searched
to infer signaling pathways (Scott et al., 2006), to extrapolate
between species, or to signify mechanistic gaps. Some of these
gaps may be filled by literature mining (Krallinger et al., 2005)
and others will require additional experiments. Moreover,
intelligent computational techniques will aid in designing such
experiments by using biological knowledge to infer testable
hypotheses about novel mechanisms (Nguyen and Ho, 2006).
Computational predictive modeling of cellular signaling
systems will aid risk assessment in two important ways. First,
Over the past decade, there has been a focused international
effort to identify possible adverse effects of endocrine disrupting chemicals (EDCs) on humans and wildlife. Scientists
have identified alterations in the concentration dynamics of
specific hormones as risk factors for common cancers such as
breast cancer (estrogen, progesterone), endometrial cancer
(estrogen), and prostate cancer (estrogen, testosterone) in
humans (Portier, 2002). Chemicals capable of acting as EDCs
include pesticides, pharmaceuticals, and industrial chemicals.
Ecological exposures to EDCs are primarily from industrial
and waste water treatment effluents, whereas human exposures
are mainly through the food chain. There is convincing evidence that fish are being affected by EDCs both at the
individual and population levels.
As many of the adverse effects have been related to
alterations in the function of the hypothalamus-pituitarygonadal (HPG) axis, the development of computational system
biology models that describe the biological perturbations at the
biochemical level and integrate information toward higher
levels of biological organization will be useful in predicting
dose–response behaviors at the whole organism and population
levels. For example, a mechanistic computational model of the
intraovarian metabolic network has been developed to predict
the synthesis and secretion of testosterone and estradiol and
their responses to the EDC, fadrozole (Breen et al., 2007).
Physiologically based pharmacokinetic (PBPK) models coupled with pharmacodynamic models that include the regulatory
feedback of the HPG axis also can be used to predict the
biological response to EDCs in whole organisms (Plowchalk
and Teeguarden, 2002; Watanabe et al., 2006). In addition,
these computational models can be developed for fish and other
wildlife. They can be used to identify biomarkers of exposure
to EDCs that are indicative of the ecologically relevant effects
at the individual and population levels in support of predictive
environmental risk assessments (Rose et al., 2003).
Because the mechanism of action of EDCs is generally
understood, there has been a considerable emphasis on the
development of screening tools for use in hazard identification,
and the involvement of feedback loops in physiological regulation of hormone function has provided a foundation upon
which to build computational models of the relevant biology.
Hence, EDCs represent a prime example of how toxicity
pathway elucidation and characterization can be applied to
hazard and risk assessment as envisioned by the National
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Research Council (2007). Of course, additional research is
needed in this area to bring a higher level of involvement of
cell based screening assays, especially those which incorporate
human cells or receptors, and to employ the computational
models of response.
DOSE–RESPONSE AND EXTRAPOLATION MODELS
Dose–response is the combination of the relationship between
exposure and a relevant measure of internal dose (pharmacokinetics), and the relationship between internal dose and the
toxic effect (pharmacodynamics). They are intended to reliably
predict the consequences of exposure at other dose levels and
life stages, in other species, or in susceptible individuals.
Dose–Response and Uncertainty
Risk analysis for environmental exposures involves exposure assessment (factoring in various routes such as drinking
water, food, air, and skin exposure) and the effects of those
exposures on individuals (dose–response assessment). In
modern exposure assessments, exposure may well be characterized by a distribution of possible exposure levels over a
population, with confidence intervals on the quantiles of that
distribution (e.g., specifying the 99th percentile of the exposure
distribution and its 95 percent confidence bounds), and
a sophisticated analysis of the components of variability and
uncertainty (e.g., Cullen and Frey, 1999; U.S. EPA, 1997). In
contrast, standard approaches to dose–response analysis treat
the uncertainties surrounding dose–response metrics simplistically, using standard factors to extrapolate across species and
to quantify variability among exposed people. Probabilistic
dose–response assessment methods allow a more complete
characterization of uncertainty and variability in dose–response
analysis (Evans et al., 2001; Hattis et al., 2002; Slob and
Pieters, 1998), and are naturally compatible with probabilistic
exposure assessments (van der Voet and Slob, 2007). Dose–
response analysis is divided into the analysis of the delivery of
toxic substances to target tissues (pharmacokinetics), and the
action of toxic substances at their targets (pharmacodynamics).
Much progress has been made in understanding pharmacokinetics and in building models (PBPK models) that quantify
that understanding. Such models may be used to quantify the
relationship between potency in animals and humans, human
variability for internal dose, and the overall uncertainty of such
predictions (Barton et al., 2007). Hierarchical Bayesian
techniques are useful for characterizing the uncertainty of
model outputs (Hack et al., 2006). Monte-Carlo methods allow
uncertainty and variability in model parameters to be translated
into distributions of internal doses in a human population with
attendant uncertainty (Allen et al., 1996; Clewell et al., 1999).
Ideally, pharmacodynamic relationships also would be
modeled based on mechanistic understanding (Setzer et al.,
2001). In practice, however, dose–response evaluations are
based on empirical dose–response modeling of animal
toxicology data. Typically, many empirical curves may fit a
given dataset, reflecting real uncertainty about the ‘‘true’’ dose–
response relationship. Wheeler and Bailer (2007) have developed a method using model averaging that approximates the
uncertainty in our understanding of a given dose–response
relationship.
Uncertainty in a risk assessment may be reduced by the
collection of further information, and sensitivity analysis
(Saltelli et al., 2000) can help to quantify the contribution of
individual sources of uncertainty and their interactions to that
of the overall risk analysis. Frey and Patil (2002) and Mokhtari
et al. (2006) have compared the utility of different sensitivity
analysis methods in a probabilistic risk assessment. Mokhtari
and Frey (2005) have recommended how sensitivity analysis
can be used and applied to aid in addressing risk management
and research planning questions. These approaches provide
considerable information to the risk manager for making
decisions about the exposure levels needed to protect target
populations.
Genetic Variation, Gene–Environment Interactions, and
Environmental Risk Assessment
Understanding relationships between environmental exposures and complex disease requires consideration of multiple
factors, both extrinsic (e.g., chemical exposure) and intrinsic
(e.g., genetic variation). This information must be integrated to
evaluate gene–environment interactions to identify vulnerable
populations and characterize life-stage risks. Although the
association between genetic and environmental factors in development of disease has long been recognized, tools for largescale characterization of human genetic variation have only
recently become available (The International HapMap Consortium, 2005).
It is well known that different species, and individuals within
species, react differently to identical exposures to pharmaceuticals or environmental chemicals. This is, in part, driven by
genetic variation in multiple pathways affecting multiple processes such as adsorption, metabolism and signaling. Recent
advances in our understanding of the pattern of human
molecular genetic variation have opened the door to genomewide genetic variation studies (Gibbs and Singleton, 2006).
Pharmacogenetics is a well-developed field studying the
interaction between human genetic variation and differential
response to pharmaceutical compounds (Wilke, 2007). Many
of the insights developed in these studies have direct relevance
to environmental chemicals. Pharmacogenetic studies increasingly analyze both pharmacokinetics and pharmacodynamics
pathways. Emphasis is shifting from a focus on individual
markers, such as single-nucleotide polymorphisms (SNPs), to
multi-SNP and multigene haplotypes.
Gene–drug interaction studies have provided many insights
for understanding the effects of chemical exposure in
COMPUTATIONAL TOXICOLOGY
genetically heterogeneous populations. For example, investigators in the NIH Pharmacogenetics Research Network are
examining multiple approaches to correlate drug response with
genetic variation. Data from this program is stored and
annotated in a publicly accessible knowledge base (Giacomini
et al., 2007). Lessons learned from these and related studies are
being incorporated into drug development and governmental
regulation, and are models for approaches to identify vulnerable populations in the context of environmental exposure.
Although genetic variation plays a major role in gene–
environment interactions, recent work has shown that epigenetic effects also are important. This complicates the picture
because the effects of exposure can lead to multigenerational
effects even in the absence of genetic mutations. Epidemiological evidence increasingly suggests that environmental exposures early in development have a role in susceptibility to
disease in later life, and that some of these effects are passed on
through a second generation. Epigenetic modifications provide
a plausible link between the environment and alterations in
gene expression that might lead to disease phenotypes. For
example, a potential mechanism underpinning early life programming is that of exposure to excess stress steroid hormones
(glucocorticoids) in early life. It has recently been shown that
the programming effects of glucocorticoids can be transmitted
to a second generation. This information provides a basis for
understanding the inherited association between low birth
weight and cardiovascular disease risk later in life (Drake et al.,
2005).
It is becoming increasingly clear that specific genetic
variants modulate individual vulnerability to many diseases.
A major challenge for future toxicogenomics research is to link
exposure, internal dose, genetic variation, disease, and gene–
chemical interactions (Schwartz and Collins, 2007). This effort
should yield improved dosimetry models that will reduce
uncertainties associated with the assumption that populations
are homogeneous in their response to toxic chemicals. Exposure information on par with available toxicogenomic information will improve our ability to identify vulnerable
populations, classify exposure in studies of complex disease,
and elucidate important gene–environment interactions.
The study of genetic variation intersects with several issues
discussed in the NRC report. At one end, genetic variation
provides a handle for investigating mechanism of action of
chemicals and for elucidating toxicity pathways. Gene
knockout strains in many species provide a standard tool for
delineating pathways (Wijnhoven et al., 2007), but less severe
changes in the form of genetic polymorphisms are also useful
and potentially more relevant to the understanding human
health effects. By testing a chemical in a panel of animals
with polymorphic, but well-characterized genetic backgrounds
(Roberts et al., 2007), one can generate valuable information
on what pathways are being modulated by the chemical
(Ginsburg, 2005). At the other end of the spectrum, it is
possible in some cases to understand in detail how genetic
21
differences alter dose–response relationships, and from there to
develop specific risk assessment recommendations which take
into account genetic variation in human populations. The
primary examples of this approach to risk assessment involve
chemical metabolism (Dorne, 2007), which is also the most
well studied area in the field of pharmacogenetics. In summary,
there is an ever growing body of knowledge about the effects
and uses of genetic variation in many species, and the field of
predictive computational toxicology will be able to increasingly benefit from these advances.
Computational Tools for Ecological Risk Assessment
Ecological systems pose some unique challenges for quantitative risk assessment. Human health risk assessment requires
extrapolation from effects in well-characterized animal models
to well-studied human biology, with the aim of protecting
individuals. In contrast, ecological risk assessment requires
extrapolation among widely divergent taxonomic groups of
relatively understudied organisms, with the intent of protecting
populations and critical functional processes within ecological
communities.
Modern computational capabilities and tools for conducting
high-content biological analyses (e.g., transcriptomics, proteomics, and metabolomics) have the potential to significantly
enhance our ability to predict or evaluate ecological risks. For
example, high-content assays that provide multivariate results
can be used to quantitatively classify individual organisms
(sentinels) or communities of organisms (e.g., microbial
communities) as within or deviated from a normal operating
range (Kersting, 1984; van Straalen and Roelofs, 2006). As a
key advantage, these general profiling and multivariate concepts can be applied to species that lack a well-characterized
genome (van Straalen and Roelofs, 2006). Beyond profiling
approaches, high-content biological analyses provide powerful
tools for examining system-wide responses to stressors.
Through iterations of system-oriented hypothesis generation,
testing, and gradual refinement of biologically based models, it
should be feasible to establish a credible scientific foundation
for predicting adverse effects based on chemical mode of
action and/or extrapolating effects among species with well
conserved biological pathways (Villeneuve et al., 2007).
However, even with the ability to conduct high-content
analyses, high quality data sets for parameterizing computational models, particularly those that bridge from effects on
individual model animals to predicted effects on wildlife
populations, are likely to remain rare (e.g., Bennett and
Etterson, 2007). Consequently, strategies for making the best
possible use of laboratory toxicity data to forecast/project
population-level risks will remain critical (Bennett and
Etterson, 2007). Additionally, alternative computational approaches will have an important role to play. For example,
computational methods that examine overall network topology
may be used as a way to deduce system function, control
22
KAVLOCK ET AL.
properties, and robustness of biological networks to stressors.
Such approaches can be applied at many scales of biological
organization, from gene regulatory networks within a single
cell to trophic interactions and food webs at the ecosystem
level (Proulx et al., 2005). Similarly, there is an increasingly
important role for models, simulation, and landscape level
spatial forecasting related to the overlapping impacts of multiple stressors (e.g., chemicals, climate change, habitat loss,
exotic species). There are many examples of creative uses of
geographic information systems and remote sensing technologies for this purpose (e.g., Haltuch et al., 2000; Kehler and
Rahel, 1996; Kooistra et al., 2001; Leuven and Poudevigne,
2002, McCormick, 1999; Tong, 2001). Thus, although the
challenge of ecological risk assessment and balancing
environmental protection against the demands of human
commerce and activities remains daunting, ecotoxicologists,
‘‘stress ecologists’’ (van Straalen, 2003), and risk assessment professionals have increasingly powerful tools at their
disposal.
Virtual Tissues—The Next Big Step for Computational
Biology
To date, biologically motivated computational modeling in
toxicology has consisted largely of dosimetry models (PBPK
and respiratory tract airway models) and, to a lesser extent,
biologically based dose–response models that combine dosimetry with descriptions of one or more modes of action (Clewell
et al., 2005; Conolly et al., 2004). PBPK models are usually
highly lumped and contain little spatial information. Early
models of the lung were one-dimensional, though more recently, three-dimensional descriptions of both the nasal and
pulmonary airways have been developed (Kimbell et al., 2001;
Timchalk et al., 2001). Thus, for the most part, current
biologically motivated modeling in toxicology involves
significant abstraction of biological structure.
Ongoing developments in high-throughput technologies,
systems biology, and computer hardware and software are
creating the opportunity for ‘‘multiscale’’ modeling of biological systems (Hunter et al., 2006; Kitano, 2002). These
models incorporate structural and functional information at
multiple scales of biological organization. For example,
Bottino et al. (2006) studied cardiac effects of drugs using
a hierarchical set of models extending from ion channels to
cells to the tissue level. They showed how such models can be
developed for multiple species and how in silico experiments
can be conducted where drugs are used to perturb the cardiac
system. An additional important aspect of this kind of
modeling is that one can superimpose certain risk factors,
such as hypokalemia and ischemia, in order to make clinical
predictions prior to the actual use of the drug in the clinic. A
conceptually similar approach is being taken in the HepatoSys
project (HepatoSys, 2007), where a suite of models describing
various aspects of the functional biology of hepatocytes is
under development. The overall aim of the HepatoSys project
is to arrive at a holistic understanding of hepatocyte biology
and to be able to present and make these processes accessible
in silico.
A ‘‘virtual liver’’ is being developed at U.S. EPA’s National
Center for Computational Toxicology. The overall goal of this
project is to develop a multiscale, computational model of the
liver that incorporates anatomical and biochemical information
relevant to toxicological mechanisms and responses. As model
development progresses, integration of within-cell descriptions
and cell-to-cell communication will evolve into a computational
description of the liver. The approach will be to first describe
normal biological processes, such as energy and oxygen metabolism, and then describe how perturbations of these
processes by chemicals lead to toxic effects. In the longer
run, the project also will provide an opportunity to develop
descriptions of diseases, such as diabetes, and to examine how
such diseases influence susceptibility to environmental stressors.
Virtual tissues are being developed not only in the context of
computational toxicology, but also in clinical and translational
research. Thus, there is an increasing emphasis on systematic
integration of scientific data, visualization, and transparent
computing that creates easily accessible and customizable
workflows for users. This integration of basic research and
clinical data has created the demand for more streamlined tools
and necessary resources for on demand investigation and
modeling of pressing biological problems, and subsequent
validation of in silico predictions through further clinical and
environmental observations. In response to this need, the
National Biomedical Computation Resource (NBCR; http://
nbcr.sdsc.edu/) and their collaborators are developing tools
such as Continuity, which describes molecular interactions,
diffusion, and electrostatics in the human heart. Continuity is
capable of transparently accessing remote computational
resources from an end user’s desktop environment. Development of middleware at the NCBR, such as the Opal toolkit,
makes such transparent access possible.
The potential payoffs from development of virtual tissues in
toxicology are significant. Virtual tissues will build on current
successes with PBPK modeling and take the development of
quantitative descriptions of biological mechanisms to a new
level of complexity. Virtual tissues will have much greater
capabilities than PBPK models for providing insights into
dose–response and time course behaviors, and will promote
inclusion of larger amounts of integrated biological data into
risk assessment.
With adequate development, virtual tissues will also become
capable of providing capabilities necessary for a full implementation the National Research Council (2007) report.
Development of in vitro assays of toxicity pathways will
require validation studies that can at present only be conducted
in vivo. In the future, sufficiently mature virtual tissues will
provide an in silico alternative for at least some aspects of
in vivo testing. The continuing and probably increasing
23
***
***
**
*
*
***
**
***
Virtual
tissues
Genetic
variability
Uncertainty
analysis
System
biology
models
Exposure assessment
Risk characterization
Note. See the text under each area for specific examples of how the tools can be applied in the context of risk assessment.
*
**
**
**
***
***
Hazard identification
Dose–response
assessment
Chemical characterization
Toxicity testing
Toxicity pathways
Targeted testing
Dose–response and
extrapolation modeling
Risk contexts (populations
and exposure data)
***
***
***
**
***
**
Genomics
**
**
***
Cell
signaling
networks
High
thruput
screens
Molecular
modeling
Toxicoinformatics
Fate and
transport
models
The field of toxicology is rapidly approaching what could be
a golden era. Spurred on by-far reaching advances in biology,
chemistry, and computer sciences, the tools needed to open the
veritable black boxes that have prevented significant achievements in predictive power are being witnessed. We have
highlighted many of the topic areas that have demonstrated
advances in the state of the science, and from which more
advances are expected in the near future. Although the new
paradigm suggested by the NRC its Toxicity Testing in the
Twenty First Century: A Vision and a Strategy (National
Research Council, 2007) departs somewhat from the traditional
risk assessment approach exposed by the National Research
Council (1983), the two approaches can be mapped together,
and the tools of computational toxicology can provide outputs
that will help close gaps in many of the areas (Table 2). Some
aspects of computational toxicology discussed here, such as the
use of fate and transport models, the development of curated
and widely accessible databases, physiological based pharmacokinetic models, and characterizing uncertainty in models are
already being used in evaluating chemical risks, although
continued development is necessary to address emerging issues
such as nanomaterials. Other aspects, such as HTS and
toxicogenomics are witnessing extensive development and
application efforts in toxicology but have yet to become part of
mainstream data generation. Still others, like the assessment of
gene–environment interactions and development of virtual
tissues are really only beginning to be tested for applicability,
although these areas offer significant potential for improved
understanding of susceptibility and for extrapolating responses
across life stages, genders, and species.
Much of the high-throughput and genomics technology
beginning to be applied to toxicology was developed by the
pharmaceutical industry for use in drug discovery. Environmental chemicals differ from drug candidates in a number of
important ways. For example, drugs are developed with
discrete targets in mind, conform to physicochemical properties
that assist in absorption, distribution, metabolism, and
NAS toxicity
testing paradigm
(NRC, 2007)
SUMMARY AND CONCLUSION
NAS risk
assessment
paradigm
(NRC, 1983)
pressure to reduce animal use for toxicity testing will only
encourage this trend.
Finally, it must be noted that success in development of
virtual tissues will depend not only on coordination of
computational modeling with targeted data collection but also,
perhaps even more importantly, on the appropriate training of
a new generation of computational toxicologists. These
individuals will have expertise in computational tools,
mathematics, and biology, and will be able to move seamlessly
between the laboratory and the computer. It is likely that this
vision applies not only to development of virtual tissues but
also, more broadly, to research and development in toxicology
and risk assessment.
TABLE 2
Application of Computational Toxicology Areas as Discussed in this Review to Risk Assessment and Toxicity Testing Components described by the National Academy
of Sciences (the Asterisks Denote the Relative Importance of an Aspect of Computational Toxicology as Covered in this Review to Those Components)
COMPUTATIONAL TOXICOLOGY
24
KAVLOCK ET AL.
excretion, have well understood metabolic profiles, and have
use patterns that are known and quantified. In contrast,
environmental chemicals generally are not designed with
biological activity as a goal, cover extremely diverse chemical
space, have poorly understood kinetic profiles, and are
generally evaluated at exposures levels well in excess of likely
real world situations. The challenge to successfully employ
these screening technologies for broader goals in toxicology
will be considerable, given that they have yet to yield the
significant increase in the pace of drug discovery that was
expected. On the other hand, whereas the goal of drug
discovery is to find the ‘‘needle in the haystack’’ using targeted
screening tools, the goal of predictive toxicology is to use these
tools more broadly to discern patterns of activity with regard
chemical impacts on biological systems and hence may be
more achievable. It will take a concerted effort on the part of
government, academia, and industry to achieve the transformation of ‘‘Toxicity Testing in the 21st Century’’ that is so
eagerly awaited. Success will depend on building a robust
chemo-informatics infrastructure to support the field, on
conducting large-scale proof-of-concept studies that integrate
diverse data sources and types into more complete understanding of biological activity, on developing a cadre of scientists
comfortable with both molecular tools and mathematical
modeling languages, and on convincing risk managers in
regulatory agencies that the uncertainties inherent in the new
approaches are sufficiently smaller or better characterized than
in traditional approaches. The rewards from such a success
would be significant. More chemicals will be evaluated by
more powerful and broad based tools, animals will be used
more efficiently and effectively in the bioassays designed to
answer specific questions rather than to fill in a checklist, and
the effects of mixtures of chemicals will be better understood
by employing system-level approaches that encompass the
underlying biological pathways whose interactions determine
the responses of the individual and joint effect of components
of mixtures. Clearly this will not happen soon, or without
significant investment. The National Research Council (2007)
estimates a 10- to 20-year effort at about $100 million per year
will be required for the paradigm shift they envisioned. This is
probably several-fold more than is being invested currently in
the area and, in most cases, those funds have not been
specifically guided by an overarching strategic vision such as
put forth by the NRC. Nonetheless, there are pockets of
progress occurring and the first success will likely be seen in
the ability to detect and quantify the interactions of chemicals
with key identifiable biological targets (e.g., nuclear receptors,
transporters, kinases, ion channels) and to be able to map these
potentials to toxicity pathways and phenotypic outcomes using
computational tools. Later successes will be seen in modeling
responses that require ever greater understanding of systemlevel functioning that will ultimately take us to the understanding of susceptibility factors (be they for the individual,
life-stage, gender or species). All of these new methods,
capabilities, and advances offer great promise for the predictive
discipline of toxicology.
FUNDING
The Office of Research and Development of the United
States Environmental Protection Agency.
ACKNOWLEDGMENTS
The authors wish to recognize the contributions to the
International Science Forum on Computational Toxicology of
the session co-chairs (Steve Bryant, Richard Corley, Sean
Ekins, Tim Elston, Wout Slob, Rusty Thomas, Donald Tillit,
Raymond Tice, and Karen Watanabe), and presenters (Ellen
Berg, Robert Boethling, Steve Bryant, Lionel Carreira, Fanqing
Frank Chen, Harvey Clewell, Richard Corley, Christopher
Cramer, Amanda Drake, Sean Ekins, Tim Elston, Matthew
Etterson, H. Mark Fielden, Christopher Frey, Anna Georgieva,
Thomas Hartung, Jason Haugh, Kate Johnson, Jun Kanno,
Shinya Kuroda, Wildred Li, Markus Lill, Bette Meek, Ovanes
Mekenyan, John Petterson, Steve Proulx, Matt Redinbo,
Matthias Reuss, Kenneth Rose, Phil Sayre, Wout Slob, Roland
Somogyi, Clay Stephens, Justin Teeguarden, Rusty Thomas,
Raymond Tice, Sandor Vajda, Nico van Straalen, Chihae
Yang, Jeff Waring, Karen Watanabe, Richard Weinshilboum,
John Weinstein, and Matt Wheeler) all of whom were instrumental in bringing the state of the science of toxicology to the
International Science Forum on Computational Toxicology.
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