ToxSci Advance Access originally published online on October 12, 2005
Toxicological Sciences 2006 89(2):352-360; doi:10.1093/toxsci/kfj018
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REVIEW AND COMMENT |
Toxicogenomics in Risk Assessment: Applications and Needs
Department of Biochemistry and Molecular Biology, National Food Safety and Center for Integrative Toxicology, Michigan State University, East Lansing Michigan 48824
1 To whom correspondence should be addressed at Michigan State University, Department of Biochemistry and Molecular Biology, 224 Biochemistry Building, Wilson Road, East Lansing, MI, 48824–1319. Fax: (517) 353-9334. E-mail: tzachare{at}msu.edu.
Received August 30, 2005; accepted October 8, 2005
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONAPPLICATIONS OF TOXICOGENOMICSIMPEDIMENTS AND NEEDS OF...CONCLUSIONREFERENCES |
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Since its inception, there have been high expectations for the science of toxicogenomics to decrease the uncertainties associated with the risk assessment process by providing valuable insights into toxic mechanisms of action. However, the application of these data into risk assessment practices is still in the early stages of development, and proof of principle experiments have yet to emerge. The following discusses some potential applications as well as impediments that warrant a concerted investigation from all stakeholders in order to facilitate the acceptance and subsequent incorporation of toxicogenomics into regulatory decision making.
Key Words: toxicogenomics; risk assessment.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONAPPLICATIONS OF TOXICOGENOMICSIMPEDIMENTS AND NEEDS OF...CONCLUSIONREFERENCES |
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Genomic technologies are rapidly evolving as powerful tools for discovery- and hypothesis-driven research, a fact evidenced by the exponential increase in the number of publications involving microarrays, proteomics, and metabolomics (Pognan, 2004
; Shi et al., 2004). Toxicogenomics, the integration of omic technologies, bioinformatics, and toxicology, has seen significant investment in the pharmaceutical industry for both predictive and mechanism-based toxicology in an effort to identify candidate drugs more quickly and economically (Lesko and Woodcock, 2004; Lord, 2004; Yang et al., 2004). Despite significant progress in its development and implementation, deciphering meaningful and useful biological information from toxicogenomic data remains challenging for toxicologists, risk assessors, and risk managers. In general, toxicogenomic studies have been limited to a qualitative description of alterations in transcript, protein, and metabolite levels with little correlation to toxicity or contributions toward the elucidation of mechanisms of toxicity. Despite this, reviews and commentaries continue to pledge that toxicogenomics will support the development of high-throughput assays and computational models and revolutionize mechanistically based quantitative risk assessment, thereby improving predictions of environmental and human health safety (Bishop et al., 2001; Olden and Wilson, 2000; Petricoin et al., 2002; Suk et al., 2002; Tennant, 2002). Although laudable goals, significant challenges impede the incorporation of toxicogenomic data into risk assessment practices (Lesko et al., 2003; Lesko and Woodcock, 2004; Petricoin et al., 2002).
To date, drug discovery and development has been the driving force behind toxicogenomics in an effort to identify and prioritize new chemical entities (NCEs) with a greater likelihood of success in clinical trials. The high cost associated with the development of a single drug, which ranges from $500 to 900 million with a 12–15 year commitment (Luhe et al., 2005), has prompted efforts to improve the preclinical evaluation of NCEs to reduce failures in clinical trails due to unfavorable adsorption, distribution, metabolism, and excretion (ADME) characteristics as well as unacceptable toxicity (NIH, 2004). Historically, only 1 in 5,000–10,000 screened chemicals successfully reaches the market, with 30–50% of drug candidates failing due to toxicity, and only 30% of marketed drugs producing sufficient revenue to recover research and development investments. These factors significantly contribute to the time and cost of drug development (Dimasi, 2001a,b; Li, 2001), and therefore, even incremental improvements in the success rate will have favorable impacts for all stakeholders (Lesko and Woodcock, 2004). This, combined with the increasing pressure for cheaper and safer drugs, has the pharmaceutical sector reorganizing their screening and preclinical development strategies. Many are examining toxicogenomic approaches in order to develop and incorporate high-throughput toxicology screening earlier in the drug development pipeline.
Regulatory agencies such as the Food and Drug Administration (FDA) and the Environmental Protection Agency (EPA) also recognize the potential of toxicogenomics and encourage the use and submission of complementary toxicogenomic data in an effort to establish guidelines, and eventually protocols, for its inclusion in submitted applications and incorporation into regulatory decision-making (Hackett and Lesko, 2003; USFDA, 2005). At this time, the FDA and EPA, along with European and Asian regulatory bodies, are carefully monitoring developments as the field continues to mature and a workable consensus is reached among the various stakeholders.
Fundamental differences in drug versus environmental safety/risk assessment may be a factor contributing to the predominant use of toxicogenomics in the pharmaceutical sector. For example, some level of toxicity may be acceptable provided it can be monitored and managed, and the new drug provides clear health benefits relative to available treatments. Moreover, pharmaceutical companies will likely utilize toxicogenomics data to "screen out" candidates with unacceptable levels of toxicity or to demonstrate that toxicity exhibited in rodents, dogs, or nonhuman primates is irrelevant to humans. Economic influences could also play a role in the predominance of toxicogenomics in pharmaceutical research, as investigative toxicology may be supported to a greater extent in this industry. In contrast, chemical and agrochemical sectors have been less receptive to the implementation of toxicogenomics due to its questionable benefits in supporting risk assessment. Furthermore, there are significant concerns regarding its potential naive and premature use in hazard identification, possibly leading to unfounded product deselection (Freeman, 2004). The demonstration of any effects elicited by commerce chemicals is considered by some advocacy groups to be an adverse, involuntary, and therefore unacceptable risk. Companies are concerned that unsubstantiated toxicogenomic data could be inappropriately extrapolated to toxicity which could evoke actions such as the Precautionary Principle (Freeman, 2004; Tuomisto, 2004). The inability to place all toxicogenomic data into biological context may therefore increase the uncertainty of the exposure-to-outcome linkage associated with commerce chemicals and environmental contaminants, which could "screen in" more chemicals requiring further investigation in the absence of any toxicity. Nevertheless, the use of toxicogenomic data in environmental risk assessment must continue to be explored in parallel with drug safety assessments in an objective manner to determine its potential role and further define its limitations (Table 1).
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APPLICATIONS OF TOXICOGENOMICS |
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TOPABSTRACTINTRODUCTIONAPPLICATIONS OF TOXICOGENOMICSIMPEDIMENTS AND NEEDS OF...CONCLUSIONREFERENCES |
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One of the most promised applications involves the screening and prioritization of commerce chemicals and drug candidates that warrant further development and testing. This consists of comparing their toxicogenomic profiles to databases containing profiles of known toxicants and identifying biomarkers of exposure and toxicity that can be used in high-throughput screening programs. These applications are analogous to the development of diagnostic signatures and classification protocols for disease states which can identify more effective treatment regimens for selected populations and can also be used to monitor drug efficacy during clinical trails (Bleharski et al., 2003
; Chang et al., 2005). Toxicogenomic-based biomarkers will likely comprise an agglomeration of responses that allow for further stratification of the population to identify sensitive groups, which could then be treated more effectively while minimizing the risk of unacceptable toxicities. Ideally, these biomarkers will be mechanistically based and causally associated with the adverse effect, which is expected to further minimize uncertainties in the source-to-outcome continuum and extrapolations between species (rodent to human) and across models (in vitro to in vivo). Classifications based on mechanisms of action will identify biomarkers with greater predictive accuracy that could be used for exposure assessments in humans and extended to include wildlife species. Moreover, they will provide evaluations of the appropriateness of cross-species extrapolations by assessing the degree of conservation of mechanisms of toxicity, which would facilitate the implementation of mechanistically based chemical-specific uncertainty factors that account for both within- and across-species variability. Furthermore, toxicogenomics provides strategies for the comprehensive assessment of mixtures, since all possible chemical, gene, protein, metabolite, and network interactions that may be important in eliciting mixture-specific toxicities can be considered. However, these applications, including the identification of biomarkers, will require broad acceptance and comprehensive validation procedures such as those proposed by the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) and the European Centre for the Validation of Alternative Methods (ECVAM) (Hartung et al., 2004; Stokes et al., 2002).
Overall, expectations that toxicogenomics will facilitate the development of safer drugs and commerce chemicals are justified. Initial reports have demonstrated that chemicals and drugs can be classified based on their gene and metabolite profiles (Burczynski et al., 2000; Hamadeh et al., 2002a,b; Lindon et al., 2003; McMillian et al., 2004; Natsoulis et al., 2005; Steiner et al., 2004; Thomas et al., 2001; Waring et al., 2001a,b), but these approaches are not yet ready to be utilized as stand-alone tools. Consequently, it is likely that expression profiles and agglomerative biomarkers will initially be used to: (1) rank and prioritize the potential toxicity of NCEs in the early stages of development and, therefore, would not be included as a regulatory reporting requirement (e.g., investigational new drug application), and (2) demonstrate that toxicities observed in traditional models (i.e., rodent, dog, nonhuman primate) are not relevant to humans, since the mechanisms of action are not conserved across species.
Both EPA and FDA are encouraging the use of toxicogenomics and have described its applicability in regulatory decision-making. EPA's interim policy states that toxicogenomic data may be considered, but these data alone are insufficient as a basis for decisions and, therefore, will be used on a case-by-case basis (EPA, 2004, 2005). However, the recent establishment of a Computational Toxicology Program to build systems biology capacity within the agency signals its intent to use more computational approaches in the future to prioritize data requirements and reduce uncertainties in the source-to-outcome continuum used in quantitative risk assessments (Kavlock et al., 2003).
Concurrently, the FDA recognizes that toxicity and human safety testing has not kept pace with the emerging technologies, and drug development has become more challenging, inefficient, and costly (FDA, 2004). Although traditional toxicology testing has a proven track record of safety, the approaches are laborious, time-consuming, and have failed to predict specific human toxicity (Lesko and Woodcock, 2004; Olson et al., 2000). Consequently, the FDA is encouraging the incorporation of new tools, such as toxicogenomics and computational toxicology, to improve the critical path to the development of new therapeutics. They are also requesting the voluntary submission of complementary toxicogenomic data in order to facilitate training and to establish guidelines, which will eventually lead to policies regarding its submission and use in regulatory decision making (Hackett and Lesko, 2003; USFDA, 2005; Yang et al., 2004).
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IMPEDIMENTS AND NEEDS OF TOXICOGENOMICS |
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TOPABSTRACTINTRODUCTIONAPPLICATIONS OF TOXICOGENOMICSIMPEDIMENTS AND NEEDS OF...CONCLUSIONREFERENCES |
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There are a number of technical, interpretation, and implementation issues that impede the use of genomic, proteomic, and metabolomic approaches in biomedical research, regulatory decision-making, and quantitative risk assessment. These include the lack of uniform study designs, multiplicity of normalization and analysis strategies (Quackenbush, 2002
), questionable reproducibility of microarray data across platforms (Mah et al., 2004; Tan et al., 2003; Ulrich et al., 2004; Yauk et al., 2004), the semiquantitative nature of proteomics (Cox et al., 2005; Garbis et al., 2005), limited availability of metabolite annotation to support metabolomics (Kell, 2004), absence of data quality control measures and standards (Shi et al., 2004; Tong et al., 2004a), and lack of effective data sharing and reporting standards. Fortunately, several organizations (MGED (Brazma et al., 2001), MIAPE (Orchard et al., 2003), and SMRS (Lindon et al., 2005) (Table 2)) are addressing a number of these issues by developing guidelines and standards for the user community. Many journals are now requiring omic data to be uploaded into public database repositories that adhere to these standards as a prerequisite for publication in an effort to ensure unhindered public access to the primary data (Ball et al., 2004a,b). However, the availability of published toxicogenomic data, and more specifically microarray and proteomic data, will only be of value if issues regarding cross-platform comparisons and the lack of uniform data quality control measures are resolved.
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One of the most challenging aspects of implementing toxicogenomics in risk assessment involves establishing the appropriate supportive infrastructure to facilitate the effective management, integration, interpretation, and sharing of toxicogenomic data. An effective, flexible, and comprehensive knowledge base is required that is populated with phenotypically anchored toxicogenomic data complemented with ADME, histopathology, clinical chemistry, and toxicity data. Currently, several public and commercial toxicogenomic database efforts have been initiated (Table 3) utilizing the Minimum Information About a Microarray Experiment (MIAME) standards (Brazma et al., 2001
) as a guide. Although commercial databases are highly promoted, there is a lack of peer reviewed publications critically assessing their utility, although these are now starting to emerge (Fletcher et al., 2005; Ganter et al., 2005). Future publications from independent laboratories will further demonstrate their utility and facilitate increased acceptance of toxicogenomics in the scientific community. In contrast to the commercial databases, public database efforts are still in development.
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Regardless of their origin, it is imperative that these databases are able to effectively communicate and share deposited data. Strategies to facilitate electronic data exchange between databases such as Microarray Gene Expression-Markup Language (MAGE-ML) (Spellman et al., 2002
) and Systems Biology Markup Language (SBML) (Hucka et al., 2003) are being developed and will facilitate effective electronic data exchange between compliant repositories. Ideally, these databases will provide access to the large, disparate, and robust toxicogenomic data sets required to develop the necessary computational algorithms and models needed to support quantitative risk assessment.
Although databases provide effective data management solutions, the ability to integrate toxicogenomic data across chemical and biological space to develop mechanistic pathways and networks remains limited. With few exceptions, most toxicogenomic studies to date provide a qualitative description of changes with minimal reporting regarding the implications to physiological outcomes and limited contributions toward further elucidating mechanisms of toxicity (Cunningham and Lehman-McKeeman, 2005). Similarly, reproducibility problems, quantification issues, and limited throughput compromise the utility of proteomics (Cox et al., 2005; Garbis et al., 2005). The lack of comprehensive peptide and metabolite reference databases also hinders the ability to elucidate mechanisms of toxicity associated with changes in protein and metabolite profiles (Cox et al., 2005; Kell, 2004). Nevertheless, these technologies have demonstrated their utility in classification and diagnostics, but significant contributions toward deciphering mechanisms of toxicity and aiding in risk assessment have yet to materialize. This is not surprising, since most studies lack the required replication and appropriate bioinformatic and statistical support and fail to phenotypically anchor the data to adverse outcomes. Moreover, it is not clear what toxicogenomic data is required and how it would be used in the current regulatory paradigms. Ideally, disparate gene, protein, and metabolite data would be integrated with phenotypic toxicity data and other traditional toxicology endpoints in order to identify mechanistically based agglomerative biomarkers and elucidate mechanistic networks that could be used to develop predictive quantitative models. These data could then be used to determine points of departure, establish thresholds of toxicity, and predict exposure levels to a contaminant or complex mixture required to elicit a particular biomarker or adverse response (Barabasi and Oltvai, 2004; Hwang et al., 2004; Li and Chan, 2004a,b).
Comparative toxicogenomics has the potential to identify conserved responses between humans and animal research models that are associated with toxicity which can be used to develop predictive toxicity tools. In addition, these approaches are likely to provide empirical evidence supporting the transfer of functional annotation from known human and mouse genes to unknown genes or ESTs in the rat or ecologically relevant species, based on sequence similarity and comparable expression patterns. To date, very few studies exploit comparative approaches to transfer functional annotation between orthologous genes based on comparable gene expression patterns and conserved protein interactions in addition to the traditional use of sequence homology (Lee et al., 2004; Stuart et al., 2003; Yu et al., 2004). However, platform differences, inaccurate annotation across species and microarrays, the lack of tools to facilitate comparative analysis, one-to-many relationships between genes and probes (e.g., one gene in rat has two or more orthologs in humans), incomplete or poorly annotated genomes, discrepancies between databases which define orthologous relationships (National Center for Biotechnology Information (NCBI) vs. European Bioinformatics Institute (EBI)), and the limited availability of functional annotation complicate effective cross-species comparisons and confound comparative analyses. Current gene ontologies are also imprecise, incomplete, and inconsistent across species, which compromises the accurate interpretation of toxicogenomic data relative to a phenotypic endpoint. For example, a large proportion of the current gene annotations for human, mouse, and rat are inferred exclusively by electronic associations (Table 4), which include low quality associations prone to changes and errors (Khatri and Draghici, 2005; King et al., 2003). Therefore, consistent approaches to annotation curation are required to ensure the accurate interpretation of the data (Park et al., 2005). In addition, despite more complete and accurate annotation for the human and mouse genomes, the rat continues to be the traditional rodent model of choice for toxicology studies (Table 4). More comprehensive human and mouse annotation provides the information necessary for a more thorough interpretation of the data and facilitates a more complete elucidation of pathways and networks involved in mediating toxicity. The availability of murine knock-out models also allows for more in-depth and definitive mechanistic studies. Consequently, from a toxicogenomic perspective, the mouse is a more powerful mechanistic model that is underutilized in toxicology.
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The interpretation of toxicogenomics data will continue to be a difficult task, and more effective tools to facilitate their integration and interpretation are required. Currently a number of tools exist to aide in the interpretation of genomic, proteomic, and metabolomic data independently; however, tools that integrate these disparate data are required. Typically, toxicity is a persistent and easily identified endpoint; however, toxicogenomic responses are dynamic and subject to reversible temporal changes that can be displaced in time relative to toxicity. Therefore, capturing predictive profiles will be time sensitive, and temporal toxicogenomic data will need to be collected and phenotypically anchored to well-established endpoints of toxicity (Paules, 2003
). Comparison of transcriptomic, proteomic, and metabolomic data will require sampling at multiple time points, as the relationships that exist between these measures will also exhibit temporal displacement. Relating early toxicogenomic changes to distant effects is further complicated when only a subpopulation of the treatment group experiences the toxic effect, as in the case of carcinogenesis, reproductive toxicity, and teratogenicity. The added challenge is to accurately determine whether acute or short term toxicogenomic responses are predictive of subchronic or chronic toxicity outcomes. In addition, dose-response studies are required to differentiate adaptive versus toxic responses and to establish toxicogenomic thresholds that need to be exceeded prior to the initiation of the cascade of molecular responses leading to an adverse effect. Each of these applications will require the development of powerful bioinformatics tools that can integrate disparate data across time, dose, and technologies to develop comprehensive toxic response profiles.
Historically, the data used in risk assessment has largely been descriptive, and agencies often differ in the choice of the critical toxic effect that is utilized when conducting risk assessments. The application of toxicogenomics has the potential to reduce the occurrence of such discrepancies by aiding in the identification of mechanisms of action, which will lead to increased confidence and consistency in risk assessment practices. Reductionist approaches have been successful in providing insights into mechanisms of toxicity by examining individual cellular components, their families, and functions. Despite this success, clear adverse effects can rarely be attributed to an individual event. Instead, most toxic responses likely involve complex interactions between genes, proteins, and metabolites. The emergence of toxicogenomics provides the opportunity to simultaneously interrogate the broad molecular status of an organism, tissue, or cell experiencing toxicity within its gene, protein, and metabolite domains (Fig. 1). Studies in simpler organisms such as yeast, fly, and worm demonstrate that individual responses are not independent, but form a network of interacting networks (Giot et al., 2003; Li et al., 2004; Luscombe et al., 2004; Tong et al., 2004b). Similar approaches have also been used to examine toxicologically relevant models (Johnson et al., 2004; Said et al., 2004; Yao et al., 2004). The challenge that remains is to comprehensively integrate the disparate chemical, biological, toxicological, and toxicogenomic data in order to elucidate the mechanisms and networks involved in toxicity and to develop quantitative models capable of accurately predicting thresholds. Complex network theory has been used to investigate technological and social networks, and similar principles have also been shown to govern complex biological networks (Barabasi and Oltvai, 2004) and are also likely to regulate toxicity. Therefore, the most significant challenge will be the application of comparable network approaches that integrate disparate toxicity data in order to reduce uncertainties and to support mechanistically based quantitative risk assessment (EPA, 2004). This will require multidisciplinary collaborative efforts, as well as significant retraining of toxicologists, modelers, risk assessors, and risk managers, consistent with the recommendations made by the Biomedical Information Science and Technology Initiative (BISTI) to integrate information and quantitative sciences into biomedical research (Friedman et al., 2004). Traditional toxicologists must understand the potential value and applications of toxicogenomics so it can be effectively tested and implemented alongside traditional research practices. In addition, individuals providing bioinformatic service and support need to understand the basic principles of toxicology in order to facilitate the development of effective and user-compliant toxicogenomic-based interpretation and storage tools.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONAPPLICATIONS OF TOXICOGENOMICSIMPEDIMENTS AND NEEDS OF...CONCLUSIONREFERENCES |
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The suggestion that toxicogenomic data such as changes in gene expression, protein levels, or metabolite levels may be used in risk assessment creates considerable unease with some stakeholders (Adelman, 2005
). Conversely, others are actively pursuing toxicogenomic approaches to identify putative high-throughput biomarkers to rank and prioritize lead candidates that warrant further development (Balbus, 2005; Luhe et al., 2005). The anxiety on one hand and enthusiasm on the other has created a discord within the risk assessment community on how to proceed with toxicogenomics. The concerns are justifiable due to the potential naive and premature use of the data which could have dire consequences for all stakeholders, including the general public. Nevertheless, there is general agreement that toxicogenomics will play an increasingly larger role in regulatory decision-making. Given the opportunity, effective and productive communication and collaboration will be a critical factor in establishing protocols for the interpretation and incorporation of toxicogenomics into quantitative risk assessment which will iteratively evolve in the presence of existing strategies as all stakeholders gain further experience with these emerging technologies. |
ACKNOWLEDGMENTS |
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Special thanks to members of the Zacharewski Lab: Lyle Burgoon, Jeremy Burt, Edward Dere, Cora Fong, and Josh Kwekel, whose discussions facilitated the drafting of this paper. D.R.B. is supported by a fellowship from the Michigan Agricultural Experimental Station. T.R.Z. is partially supported by the Michigan Agricultural Experimental Station. This work was supported by funds from NIH Grants R01-ES12245 and R01-ES011271 the Superfund Grant P42-ES04911.
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