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4 Tools and Technologies In Chapter 2, the committee provided an overview of its vi- sion for toxicity testing, and Chapter 3 described the main com- ponents of the vision. Here, tools and technologies that might be used to apply the committeeâs vision are briefly discussed. The tools and technologies will evolve and mature over time, but many are already available. The committee emphasizes that tech- nologies are evolving rapidly, and new molecular technologies will surely be available in the near future for mapping toxicity pathways, assessing their functions, and measuring dose-response relationships. TOOLS AND TECHNOLOGIES FOR CHEMICAL CHARACTERIZATION A variety of computational methods are available for chemi- cal characterization. The discussion here focuses on structure- activity relationship (SAR) analyses, which use physical and chemical properties to predict the biologic activity, potential toxic- 98
Tools and Technologies 99 ity, and metabolism of an agent of concern. All are conceptually based on the similar-property principle, that is, that chemicals with similar structure are likely to exhibit similar activity (Tong et al. 2003). Accordingly, biologic properties of new chemicals are often inferred from properties of similar existing chemicals whose hazards are already known. Specifically, SAR analysis involves building mathematical models and databases that use physical properties (such as solubility, molecular weight, dissociation con- stant, ionization potential energies, and melting point) and chemi- cal properties (such as steric properties, presence or absence of chemical moieties or functional groups, and electrophilicity) to predict biologic or toxicologic activity of chemicals. SAR analyses can be qualitative (for example, recognition of structural alerts, that is chemical functional groups and substructures) or quantita- tive (for example, use of mathematical modeling to link physical, chemical, and structural properties with biologic or toxic end points) (Benigni 2004). Key factors in the successful application of SAR methods include proper representation and selection of structural, physical, and chemical molecular features; appropriate selection of the initial set of compounds (that is, the âtraining setâ) and methods of analysis; the quality of the biologic data; and knowledge of the mode or mechanism of toxic action (McKinney et al. 2000). Current applications of SAR analyses include soft drug de- sign, which involves improving the therapeutic index of a drug by manipulating its steric and structural properties (Bodor 1999); de- sign and testing of chemotherapeutic agents (van den Broek et al. 1989); nonviral gene and targeted-gene delivery (Congiu et al. 2004); creating predictive models of carcinogenicity to replace animal models (Benigni 2004); predicting the toxicity of chemicals, particularly pesticides and metals (Walker et al. 2003a); and pre- dicting the environmental fate and ecologic effects of industrial chemicals (Walker et al. 2003b). Among the available predictive- toxicity systems, the most widely used are statistically based cor-
100 Toxicity Testing in the 21st Century relative programs (such as CASE/MultiCASE and TOPKAT) and rule-based expert systems (such as DEREK and ONCOLOGIC) (McKinney et al. 2000). There are many examples of successful applications of SAR and quantitative SAR (QSAR) analysis. One successful application of SAR analysis in risk assessment is the modeling of Ah-receptor- binding affinities of dioxin-like compounds, including the structurally related polychlorinated dioxins, dibenzofurans, and biphenyls. Specifically, SAR methods were used to establish a common mechanism of action for toxic effects and in the further development of toxic equivalency factors in risk assessments involving exposure to complex mixtures of those compounds (van der Berg et al. 1998). Other successful applications have examined how structural alterations influence toxicity. For example, toxic effects of nonpolar anesthetics are mediated by a nonspecific action on cell membranes and have been shown to be directly correlated to their log octanol-water partition coefficient (log Kow). However, the polar anestheticsâwhich include such chemicals as phenols, anilines, pyridines, nitrobenzenes, and aliphatic aminesâgenerally show an anesthetic potency 5-10 times higher than expected on the basis of their log Kow alone (Soffers et al. 2001). Much effort has been directed toward the modeling and pre- diction of specific toxicities, particularly mutagenicity and car- cinogenicity because of the importance of these end points, the cost and length of full rodent assays for carcinogenesis, and the availability of high-quality data for modeling purposes. Experi- mental observation has led to the identification of several struc- tural alerts that can cause both mutation and cancer, including carbonium ions (alkyl-, aryl-, and benzylic-), nitrenium ions, epox- ides and oxonium ions, aldehydes, polarized double bonds (alpha and beta unsaturated carbonyls or carboxylates), peroxides, free radicals, and acylating intermediates (Benigni and Bossa 2006). The structural alerts for mutagenicity and carcinogenicity have
Tools and Technologies 101 been incorporated into expert systems for predicting toxic effects of chemicals (Simon-Hettich et al. 2006). A number of structural alerts also have been associated with developmental toxicity. They were identified on the basis of known developmental responses to environmental agents, such as valproic acids, hydrazides, and carbamates (Schultz and Seward 2000; Cronin 2002; Walker et al. 2004). Studies have demonstrated that the presence of a hydroxyl group is required for estrogenic activity of biphenyls; symmetric derivatives are 10 times more ac- tive than nonsymmetric ones (Schultz et al. 1998). The relationship between the size and shape of the nonphenolic moiety and estro- genic potency among para-substituted phenols demonstrated the trend of increasing estrogenicity with increased molecular size (Schultz and Seward 2000). Thus, although predictive models for some toxic end points, such as mutagenicity, already exist, more mechanistically complex end pointsâsuch as acute, chronic, or organ toxicityâare more difficult to predict (Schultz and Seward 2000; Simon-Hettich et al. 2006). One final application of SAR analysis is in predicting absorp- tion, distribution, metabolism, and excretion. Qualitative SARs, QSARs, and the related quantitative structure-property relation- ships have been successfully used to estimate such key properties as permeability, solubility, biodegradability, and cytochrome P- 450 metabolism (Feher et al. 2000; Bugrim et al. 2004); to predict drug half-life values (Anderson 2002); and to describe penetration of the blood-brain barrier (Bugrim et al. 2004). As indicated above, the predictive ability of different models depends on selecting the correct molecular descriptors for the par- ticular toxic end points, the appropriate mathematical approach and analysis, and a sufficiently rich set of experimental data. The ability to adapt existing models continuously by building on lar- ger and higher-quality datasets is crucial for the improvement and ultimate success of these approaches.
102 Toxicity Testing in the 21st Century MAPPING TOXICITY PATHWAYS As discussed in Chapters 2 and 3, the key component of the committeeâs vision is the evaluation of perturbations in toxicity pathways. Many tools and technologies are available that can aid in the identification of biologic signaling pathways and the development of assays to evaluate their function. Recent advances in cellular and molecular biology, -omics technologies, and computational analysis have contributed considerably to the understanding of biologic signaling processes (Daston 1997; Ekins et al. 2005). Within the last 15 years, multiple cellular response pathways have been evaluated in increasing depth as is evidenced by the progress in the basic knowledge of cellular and molecular biology (Fernandis and Wenk 2007; Lewin et al. 2007). Moreover, systems biology constitutes a powerful approach to describing and understanding the fundamental mechanisms by which biologic systems operate. Specifically, systems biology focuses on the elucidation of biologic components and how they work together to give rise to biologic function. A systems approach can be used to describe the fundamental biologic events involved in toxicity pathways and to provide evolving biologic modeling tools that describe cellular circuits and their perturbations by environmental agents (Andersen et al. 2005a). A longer-term goal of systems biology is to create mathematical models of biologic circuits that predict the behavior of cells in response to environmental agents qualitatively and quantitatively (Lander and Weinberg 2000). Progress in that regard is being made in developmental biology (Cummings and Kavlock 2005; Slikker et al. 2005). The sections that follow outline tools and technologies that will most likely be used to elucidate the critical toxicity pathways and to develop assays to evaluate them.
Tools and Technologies 103 In Vitro Tests The committee foresees that in vitro assays will make up the bulk of the toxicity tests in its vision. In vitro tests are currently used in traditional toxicity testing and indicate the success of de- veloping and using in vitro assays (Goldberg and Hartung 2006). In vitro tests include the 3T3 neural red uptake phototoxicity as- say (Spielman and Liebsch 2001), cytotoxicity assays (OâBrien and Haskins 2007), skin-corrosivity tests, and assays measuring vascu- lar injury using human endothelial cells (Schleger et al. 2004). Many tests have been validated by the European Centre for the Validation of Alternative Methods. The committee notes that the current in vitro tests originated as alternatives to or replacements of other toxicity tests. In the committeeâs vision, in vitro assays will evaluate biologically significant perturbations in toxicity pathways and thus are not intended to serve as direct replace- ments of existing toxicity tests. The committee envisions the use of human cell lines for the in vitro assays. Cell lines have been used for a long time in ex- perimental toxicology and pharmacology. Human cell lines are readily available from tissue-culture banks and laboratories and are particularly attractive because they offer the possibility of working with a system that maintains several phenotypic and genotypic characteristics of the human cells in vivo (Suemori 2006). Differentiated functions, functional markers, and metabolic capacities may be altered or preserved in cell lines, depending on culture conditions, thereby allowing testing of a wide array of agents in different experimental settings. Other possibilities in- clude using animal cells that are transfected to express human genes and proteins. For example, various cell linesâsuch as V79, CHO, COS-7, NIH3T3, and HEPG2âhave been transfected with complementary DNA (cDNA, DNA synthesized from mature mRNA) coding for human enzymes and used in mutagenesis and
104 Toxicity Testing in the 21st Century drug-metabolism studies (Potier et al. 1995). Individual enzymes have also been stably expressed to identify the major human isoenzymes, such as cytochromes P-450 and UDP-glucu- ronosyltransferases, responsible for the metabolism of potential therapeutic and environmental agents. The metabolic in vitro screens with human enzymes are usually conducted as a prelude to clinical studies. A major limitation of using human cell lines is the difficulty of extrapolating data from the simple biologic system of single cells to the complex interactions in whole animals. Questions have also been raised concerning the stability of cell lines over time, the reproducibility of responses over time, and the ability of cell lines to account for genetic diversity of the human population. None- theless, cell lines have been used as key tools in the initial screen- ing and evaluation of toxic agents and the characterization of properties of cancer cells (Suzuki et al. 2005) and in gene profiling with microarrays (Wang et al. 2006). The high-throughput meth- ods now becoming more common will allow the expansion of the methods to larger numbers of end points, wider dose ranges, and mixtures of agents (Inglese 2002; Inglese et al. 2006). High-Throughput Methods A critical feature of the committeeâs vision is the use of high- throughput methods that will allow economical screening of large numbers of chemicals in a short period. The pharmaceutical industry provides an example of the successful use of high- throughput methods. Optimizing drug-candidate screening is essential for timely and cost-effective development of new pharmaceuticals. Without effective screening methods, poor drug candidates might not be identified until the preclinical or clinical phase of the drug-development process, and this could lead to high costs and low productivity for the pharmaceutical industry
Tools and Technologies 105 (Lee and Dordick 2006). Pharmaceutical companies have turned to high-throughput screening, which allows automated simul- taneous testing of thousands of chemical compounds under conditions that model key biologic mechanisms (Fischer 2005). Such technologies as hybridization, microarrays, real-time poly- merase chain reaction, and large-scale sequencing are some of the high-throughput methods that have been developed (Waring and Ulrich 2000). High-throughput assays are useful for predicting several important characteristics related to the absorption, distribution, metabolism, excretion, and toxicity of a compound (Gombar et al. 2003). They can predict the interaction of a compound with enzymes, the metabolic degradation of the compound, the enzymes involved in its biotransformation, and the metabolites formed (Masimirembwa et al. 2001). That information is integral for selecting compounds to advance to the next phase of drug development, especially when many compounds may have comparable pharmacologic properties but differing toxicity profiles (Pallardy et al. 1998). High-throughput assays are also useful for rapid and accurate detection of genetic polymorphisms that could dramatically influence individual differences in drug response (Shi et al. 1999). Microarrays Microarray technologies have allowed the development of the field of toxicogenomics, which evaluates changes in genetic response to environmental agents or toxicants. These technologies permit genomewide assessments of changes in gene expression associated with exposure to environmental agents. The identifica- tion of responding genes can provide valuable information on cel- lular response and some information on toxicity pathways that might be affected by environmental agents. Some of the tools and technologies are described below.
106 Toxicity Testing in the 21st Century Microarrays are high-throughput analytic devices that pro- vide comprehensive genome-scale expression analysis by simul- taneously monitoring quantitative transcription of thousands of genes in parallel (Hoheisel 2006). The Affymetrix GeneChip Hu- man Genome U133 Plus 2.0 Array provides comprehensive analy- sis of genomewide expression of the entire transcribed human ge- nome on a single microarray (Affymetrix Corporation 2007). Whole-genome arrays are also available for the rat and mouse. The use of the rat arrays will probably increase as the relation- ships between specific genes and markers on the arrays become better understood. Protein microarrays potentially offer the ability to evaluate all expressed proteins in cells or tissues. Protein-expression profil- ing would allow some understanding of the relationship between transcription (the suite of mRNAs in the cell) and the translational readout of the transcripts (the proteins). Protein microarrays have diverse applications in biomedical research, including profiling of disease markers and understanding of molecular pathways, pro- tein modifications, and protein activities (Zangar et al. 2005). However, whole-cell or tissue profiling of expressed proteins is still in the developmental stage. These techniques remain expen- sive, and the technology is in flux. Differential gene-expression experiments use comparative microarray analysis to identify genes that are upregulated or downregulated in response to experimental conditions. The large- scale investigation of differential gene expression attaches func- tional activity to structural genomics. Whole-genome-expression experiments involve hundreds of experimental conditions in which patterns of global gene expression are used to classify dis- ease specimens and discover gene functions and toxicogenomic targets (Peeters and Van der Spek 2005). Gene-expression profil- ing will have a role in identifying toxicity pathways in whole- animal studies but is not expected to be the staple technology for identifying and mapping the pathways.
Tools and Technologies 107 High-Throughput Functional Genomics1 Large-scale evaluations of the status of gene expression and protein concentrations in cells allow understanding of the inte- grated biologic activities in tissues and can be used to catalog changes after in vivo or in vitro treatment with environmental agents. However, evaluation of the organization and interactions among genes in toxicity pathways requires approaches referred to as functional genomics, which encompass a different suite of mo- lecular tools (Brent 2000). The tools are designed to catalog the full suite of genes that are required for optimal activity of a toxicity pathway. The evaluation of the readout of those functional screens with bioinformatic analysis provides key data about the organiza- tion of toxicity pathways and guides computational methods that model the consequences of perturbation of the pathways by envi- ronmental agents. Functional analysis requires a cell-based assay that provides a convenient, automated cell-based measure of functioning of a toxicity pathway (Akutsu et al. 1998; Michiels et al. 2002; Chanda et al. 2003; Lum et al. 2003; Berns et al. 2004; Huang et al. 2004) and requires the ability to automate treatment of the cells with individual cDNAs or small interfering RNAs (siRNAs), which are relatively short RNA oligomers that appear to play important roles in inhibiting gene expression (Hannon 2002; Meister and Tuschl 2004; Mello and Conte 2004; Hammond 2005). Treatment of the cells with a particular cDNA causes overexpression of the gene (and presumably the protein) that is coded by it. In contrast, treatment with gene-specific siRNA causes knockdown of specific proteins by enhancing degradation of the mRNA from the gene. 1 Functional genomics should be distinguished from toxicogenomics. Toxico- genomics is a broad field combining expertise in toxicology, genetics, molecular biology, and environmental health and includes genomics, proteomics, and me- tabonomics, whereas functional genomics as described here is a specialized dis- cipline that attempts to understand the functions of genes within cellular net- works.
108 Toxicity Testing in the 21st Century High-throughput methods permit automation of such cell-based assays by the use of robots and libraries of cDNAs and siRNAs. The screens show which genes increase and which decrease activ- ity of the toxicity pathway. Computational Biology Computational biology uses computer techniques and mathematical modeling to understand biologic processes. It is a powerful tool to cope with the ever-increasing quantity and qual- ity of biologic information on genomics, proteomics, gene expres- sion, gene varieties, genotyping techniques, and protein and cell arrays (Kriete and Eils 2006). Computational tools are used in data analysis, data mining, data integration, network analysis, and multiscale modeling (Kitano 2005). Computational biology is par- ticularly useful for systems biology in understanding structural, regulatory, and kinetic models (Barabasi and Oltvai 2004); in modeling signal transduction (Eungdamrong and Iyengar 2004); and in analyzing genome information and its structural and func- tional properties (Snitkin et al. 2006). Furthermore, computational biology is used to predict toxic effects of chemical substances (Simon-Hettich et al. 2006), to understand the toxicokinetics and toxicodynamics of xenobiotics (Ekins 2006), to determine gene- expression profiling of cancer cells (Katoh and Katoh 2006), to help in the development of genomic biomarkers (Ginsburg and Haga 2006), and to design virtual experiments to replace or reduce animal testing (Vedani 1999). In drug design and discovery, novel computational technologies help to create chemical libraries of structural motifs relevant to target proteins and their small mo- lecular ligands (Balakin et al. 2006; OâDonoghue et al. 2006). Cellular signaling circuits handle an enormous variety of functions. Apart from replication and other functions of individ- ual cells, signaling circuits must implement the complex logic of
Tools and Technologies 109 development and function of multicellular organisms. Computer models are helpful in understanding that complexity (Bhalla et al. 2002). Recent studies have extended such models to include elec- trical, mechanical, and spatial details of signaling (Bhalla 2004a,b). The mitogen-activated protein kinase (MAPK) pathway is one of the most important and extensively studied signaling pathways; it governs growth, proliferation, differentiation, and survival of cells. A wide variety of mathematical models of the MAPK path- way have led to novel insights and predictions as to how it func- tions (Orton et al. 2005; Santos et al. 2007). Predictive computational models derived from experimental studies have been developed to describe receptor-mediated cell communication and intracellular signal transduction (Sachs et al. 2005). Physicochemical models attempt to describe biomolecular transformations, such as covalent modification and intermolecular association, with physicochemical equations. The models make specific predictions and work mostly with pathways that are bet- ter understood. They can be viewed as translations of familiar pathway maps into mathematical forms (Aldridge et al. 2006). In- tegrated mechanistic and data-driven modeling for multivariate analysis of signaling pathways is a novel approach to understand- ing multivariate dependence among molecules in complex net- works and potentially can be used to identify combinatorial tar- gets for therapeutic interventions and toxicity-pathway targets that lead to adverse responses (Hua et al. 2006). In Vivo Tests As discussed in Chapters 2 and 3, in vivo tests will most likely be used in the foreseeable future to evaluate the formation of metabolites and some mechanistic aspects of target-organ responses to environmental agents, including genomewide
110 Toxicity Testing in the 21st Century evaluation of gene expression. Chapter 3 noted that careful design of those studies could substantially increase the value of information obtained. For example, evaluation of cellular transcriptomic patterns from tissues of animals receiving short- term exposures may provide clues to cellular targets of environmental agents and assist in target-tissue identification. (See Chapter 3 for further discussion of protocol changes that could increase the value of toxicity tests.) Moreover, technologic advances in detection and imaging have the potential for improving in vivo testing. For example, positron-emission tomography (PET) is an imaging tool that can determine biochemical and physiologic processes in vivo by monitoring the activity of radiolabeled compounds (Paans and Vaalburg 2000). Because PET can detect the activity of an administered compound at the cellular level, its use in animal models can result in the incorporation of mechanistic processes and an understanding of the pathologic effects of a candidate compound (Rehmann and Jayson 2005). TOOLS AND TECHNOLOGIES FOR DOSE-RESPONSE AND EXTRAPOLATION MODELING As discussed in Chapters 2 and 3, two types of modeling will be critical for implementing the committeeâs vision: physiologi- cally based pharmacokinetic (PBPK) models and dose-response models of perturbations of toxicity pathways. PBPK models will allow dose extrapolation from in vitro conditions used for assess- ing toxicity-pathway perturbations to projected human exposures in vivo. Mechanistic models of perturbations of toxicity pathways should aid in developing low-dose extrapolation models that con- sider the biologic structure of the cellular circuitry controlling pathway activation.
Tools and Technologies 111 Physiologically Based Pharmacokinetic Models Assessing the risk associated with human chemical exposure has traditionally relied on the extrapolation of data from animal models to humans, from one route of exposure to another, and from high doses to low doses. Such extrapolation attempts to re- late the extent of external exposure to a toxicant to the internal dose in the target tissue of interest. However, differences in bio- transformation and other pharmacokinetic processes can intro- duce error and uncertainty into the extrapolation of toxicity from animals to humans (Kedderis and Lipscomb 2001). PBPK models provide a physiologic basis for extrapolating between species and routes of exposure and thus allow estimation of the active form of a toxicant that reaches the target tissue after absorption, distribution, and biotransformation (Watanabe et al. 1988). However, PBPK results can differ significantly in the hands of different modelers (Hattis et al. 1990), and improved modeling approaches for parameter selection and uncertainty analysis are under discussion. PBPK models might also be useful for estimat- ing the effect of exposure at different life stages, such as preg- nancy, critical periods of development, and childhood growth (Barton 2005). Interindividual differences can be incorporated into PBPK models by integrating quantitative information from in vi- tro biotransformation studies (Bois et al. 1995; Kedderis and Lipscomb 2001). The more pervasive use of PBPK approaches in the new strategy for toxicity testing will be in basing dosimetry extrapola- tions on estimates of partitioning, metabolism, and interactions among chemicals derived from in vitro measurements or perhaps even from SAR or QSAR techniques. Those extrapolations will require some level of validation that might require data from ki- netic studies in volunteers or from biomonitoring studies in hu- man populations. In the committeeâs vision for toxicity testing, the development of PBPK models from SAR predictions of partition-
112 Toxicity Testing in the 21st Century ing and metabolism would decrease animal use, and continued improvements in in vitro to in vivo extrapolations of kinetics will support the translation from test-tube studies of perturbations to predictions. Dose-Response Models of Toxicity Pathways Dose-response modeling of toxicity pathways involves the integration of mechanistic and dosimetric information about the toxicity of a chemical into descriptive mathematical terms to pro- vide a quantitative model that allows dose and interspecies ex- trapolation (Conolly 2002). New techniques in molecular biology, such as functional genomics, will play a key role in the develop- ment of such models because they provide more detailed informa- tion about the organization of toxicity pathways and the dose- response relationships of perturbations of toxicity pathways by environmental agents. Dose-response models have been devel- oped for cell-signaling pathways and used in risk assessment (Andersen et al. 2002). They have found important applications in studying chemical carcinogenesis (Park and Stayner 2006). In par- ticular, models of cancer formation have been developed to de- scribe the induction of squamous-cell carcinomas of the nasal pas- sage in rats exposed to formaldehyde by inhalation, taking into account both tissue dosimetry and the nonlinear effects of cellular proliferation and formation of DNA-protein cross-links (Slikker et al. 2004a, 2004b; Conolly et al. 2004). However, alternative imple- mentations of the formaldehyde model gave substantially differ- ent results (Subramaniam et al. 2006). Emerging developments in systems biology allow modeling of cellular and molecular signal- ing networks affected by chemical exposures and thereby produce an integrated modeling approach capable of predicting dose- response relationships of pathway perturbations by developmen- tal and reproductive toxicants (Andersen et al. 2005b).
Tools and Technologies 113 In the next decades, the dose-response modeling tools for perturbations should progress relatively rapidly to guide low- dose extrapolations of initial interactions of toxic compounds with biologic systems. The quantitative lineage of early perturbations with apical responses is likely to develop more slowly. For the foreseeable future, the continued refinement of biologic models of signaling circuitry should guide the extrapolation approaches necessary for conducting risk assessment with the toxicity- pathway tests as the cornerstone of toxicity-testing methods. REFERENCES Affymetrix Corporation. 2007. GeneChip Arrays. Affymetrix Corporation. [online]. Available: http://www.affymetrix.com/products/arrays/specific/ hgu133plus.affx [accessed March 27, 2007]. Akutsu, T., S. Kuhara, O. Maruyama, and S. Miyano. 1998. A system for identify- ing genetic networks from gene expression patterns produced by gene dis- ruption and overexpressions. Genome Inform. Ser. Workshop Genome In- form. 9:151-160. Aldridge, B.B., J.M. Burke, D.A. Lauffenburger, and P.K. Sorger. 2006. Physico- chemical modeling of cell signaling pathways. Nat. Cell Biol. 8(11):1195- 1203. Andersen, M.E., R.S. Yang, C.T. French, L.S. Chubb, and J.E. Dennison. 2002. Molecular circuits, biological switches, and nonlinear dose-response rela- tionships. Environ. Health Perspect. 110(Suppl. 6):971-978. Andersen, M.E., J.E. Dennison, R.S. Thomas, and R.B. Conolly. 2005a. New direc- tions in incidence dose-response modeling. Trends Biotechnol. 23(3):122- 127. Andersen, M.E., R.S. Thomas, K.W. Gaido, and R.B. Conolly. 2005b. Dose- response modeling in reproductive toxicology in the systems biology era. Reprod. Toxicol. 19(3):327-337. Anderson, S. 2002. The state of the world's pharmacy: A portrait of the pharmacy profession. J. Interprof. Care 16(4):391-404. Balakin, K.V., A.V. Kozintsev, A.S. Kiselyov, and N.P. Savchuk. 2006. Rational design approaches to chemical libraries for hit identification. Curr. Drug Discov. Technol. 3(1):49-65. Barabasi, A.L., and Z.N. Oltvai. 2004. Network biology: Understanding the cellâs functional organization. Nat. Rev. Genet. 5(2):101-113.
114 Toxicity Testing in the 21st Century Barton, H.A. 2005. Computational pharmacokinetics during developmental win- dows of susceptibility. J. Toxicol. Environ. Health A 68(11-12):889-900. Benigni, R. 2004. Chemical structure of mutagens and carcinogens and the rela- tionship with biological activity. J. Exp. Clin. Cancer Res. 23(1):5-8. Benigni, R., and C. Bossa. 2006. Structure-activity models of chemical carcino- gens: State of the art, and new directions. Ann. Ist Super Sanita. 42(2):118- 126. Berns, K., E.M. Hijmans, J. Mullenders, T.R. Brummelkamp, A. Velds, M. Heimerikx, R.M. Kerkhoven, M. Madiredjo, W. Nijkamp, B. Weigelt, R. Agami, W. Ge, G. Cavet, P.S. Linsley, R.L. Beijersbergen, and R. Bernards. 2004. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428(6981):431-437. Bhalla, U.S. 2004a. Signaling in small subcellular volumes. I. Stochastic and diffu- sion effects on individual pathways. Biophys J. 87(2):733-744. Bhalla, U.S. 2004b. Signaling in small subcellular volumes. II. Stochastic and dif- fusion effects on synaptic network properties. Biophys. J. 87(2):745-753. Bhalla, U.S., P.T. Ram, and R. Iyengar. 2002. MAP kinase phosphatase as a locus of flexibility in a mitogen activated protein kinase signaling network. Sci- ence 297(5583):1018-1023. Bodor, N. 1999. Recent advances in retrometabolic design approaches. J. Control. Release 62(1-2):209-222. Bois, F.Y., G. Krowech, and L. Zeise. 1995. Modeling human interindividual vari- ability in metabolism and risk: The example of 4-aminobiphenyl. Risk Anal. 15(2):205-213. Brent, R. 2000. Genomic biology. Cell 100(1):169-183. Bugrim, A., T. Nikolskaya, and Y. Nikolsky. 2004. Early prediction of drug me- tabolism and toxicity: Systems biology approach and modeling. Drug Dis- cov. Today 9(3):127-135. Chanda, S.K., S. White, A.P. Orth, R. Reisdorph, L. Miraglia, R.S. Thomas, P. DeJesus, D.E. Mason, Q. Huang, R. Vega, D.H. Yu, C.G. Nelson, B.M. Smith, R. Terry, A.S. Linford, Y. Yu, G.W. Chirn, C. Song, M.A. Labow, D. Cohen, F.J. King, E.C. Peters, P.G. Schultz, P.K. Vogt, J.B. Hogenesch, and J.S. Caldwell. 2003. Genome-scale functional profiling of the mammalian AP-1signaling pathway. Proc. Natl. Acad. Sci. U.S.A. 100(21):12153-12158. Congiu, A., D. Pozzi, C. Esposito, C. Castellano, and G. Mossa. 2004. Correlation between structure and transfection efficiency: A study of DC-Chol-- DOPE/DNA complexes. Colloids Surf. B Biointerfaces. 36(1):43-48. Conolly, R.B. 2002. The use of biologically based modeling in risk assessment. Toxicology 27:181-182; 275-279. Conolly, R.B., J.S. Kimbell, D. Janszen, P.M. Schlosser, D. Kalisak, J. Preston, and F.J. Miller. 2004. Human respiratory tract cancer risks of inhaled formalde- hyde: Dose-response predictions derived from biologically-motivated
Tools and Technologies 115 computational modeling of a combined rodent and human dataset. Toxi- col. Sci. 82(1):279-296. Cronin, M.T. 2002. The current status and future applicability of quantitative structure-activity relationships (QSARs) in predicting toxicity. Altern. Lab. Anim. 30(Suppl. 2):81-84. Cummings, A., and R. Kavlock. 2005. A systems biology approach to develop- mental toxicology. Reprod. Toxicol. 19(3):281-290. Daston, G.P. 1997. Advances in understanding mechanisms of toxicity and impli- cations for risk assessment. Reprod. Toxicol. 11(2-3):389-396. Ekins, S. 2006. Systems-ADME/Tox: Resources and network applications. J. Pharmacol. Toxicol. Methods 53(1):38-66. Ekins, S., Y. Nikolsky, and T. Nikolskaya. 2005. Techniques: Applications of sys- tems biology to absorption, distribution, metabolism, excretion and toxic- ity. Trends Pharmacol. Sci. 26(4):202-209. Eungdamrong, N.J., and R. Iyengar. 2004. Computational approaches for model- ing regulatory cellular networks. Trends Cell Biol. 14(12):661-669. Feher, M., E. Sourial, and J.M. Schmidt. 2000. A simple model for the prediction of blood-brain partitioning. Int. J. Pharm. 201(2):239-247. Fernandis, A.Z, and M.R. Wenk. 2007. Membrane lipids as signaling molecules. Curr. Opin. Lipidol. 18(2):121-128. Fischer, H.P. 2005. Towards quantitative biology: Integration of biological infor- mation to elucidate disease pathways and to guide drug discovery. Bio- technol. Annu. Rev. 11:1-68. Ginsburg, G.S., and S.B. Haga. 2006. Translating genomic biomarkers into clini- cally useful diagnostics. Expert Rev. Mol. Diagn. 6(2):179-191. Goldberg, A.M., and T. Hartung. 2006. Protecting more than animals. Sci Am. 294(1):84-91. Gombar, V.K., I.S. Silver, and Z. Zhao. 2003. Role of ADME characteristics in drug discovery and their in silico evaluation: In silico screening of chemi- cals for their metabolic stability. Curr. Top. Med. Chem. 3(11):1205-1225. Hammond, S.M. 2005. Dicing and slicing: The core machinery of the RNA inter- ference pathway. FEBS Lett. 579(26):5822-5829. Hannon, G.J. 2002. RNA interference. Nature 418(6894):244-251. Hattis, D., P. White, L. Marmorstein, and P. Koch. 1990. Uncertainties in pharma- cokinetic modeling for perchloroethylene. I. Comparison of model struc- ture, parameters, and predictions for low-dose metabolism rates for mod- els derived by different authors. Risk Anal. 10(3):449-458. Hoheisel, J.D. 2006. Microarray technology: Beyond transcript profiling and genotype analysis. Nat. Rev. Genet. 7(3):200-210. Hua, F., S. Hautaniemi, R. Yokoo, and D.A. Lauffenburger. 2006. Integrated mechanistic and data driven modeling for multivariate analysis of signal- ing pathways. J. R. Soc. Interface. 3(9):515-526.
116 Toxicity Testing in the 21st Century Huang, Q., A. Raya, P. DeJesus, S.H. Chao, K.C. Quon, J.S. Caldwell, S.K. Chanda, J.C. Izpisua-Belmonte, and P.G. Schultz. 2004. Identification of p53 regulators by genome-wide functional analysis. Proc. Natl. Acad. Sci. USA 101(10):3456-3461. Inglese, J. 2002. Expanding the HTS paradigm. Drug Discov. Today 7(Suppl. 18):S105-S106. Inglese, J., D.S. Auld, A. Jadhav, R.L. Johnson, A. Simeonov, A. Yasgar, W. Zheng, and C.P. Austin. 2006. Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc. Natl. Acad. Sci. U.S.A. 103(31):11473-11478. Katoh, M., and M. Katoh. 2006. Bioinformatics for cancer management in the post-genome era. Technol. Cancer Res. Treat. 5(2):169-175. Kedderis, G.L, and J.C. Lipscomb. 2001. Application of in vitro biotransformation data and pharmacokinetic modeling to risk assessment. Toxicol. Ind. Health 17(5-10):315-321. Kitano, H. 2005. International alliance for quantitative modeling in systems biol- ogy. Mol. Syst. Biol. 1(1):2005.0007 [online]. Available: http://www.nature. com/msb/journal/v1/n1/pdf/msb4100011.pdf [accessed March 27, 2007] Kriete, A., and R. Eils. 2006. Introducing computational systems biology. Pp. 1-14 in: Computational System Biology. Boston: Elsevier Academic Press. Lander, E.S., and R.A. Weinberg. 2000. Genomics: Journey to the center of biol- ogy. Science 287(5459):1777-1782. Lee, M.Y., and J.S. Dordick. 2006. High-throughput human metabolism and tox- icity analysis. Curr. Opin. Biotechnol. 17(6):619-627. Lewin, B., L. Cassimeris, V.R. Lingappa, and G. Plopper. 2007. Cells. Sudbury, MA: Jones and Bartlett Pub. Lum, L., S. Yao, B. Mozer, A. Rovescalli, D. Von Kessler, M. Nirenberg, and P.A. Beachy. 2003. Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 299(5615): 2039-2045. Masimirembwa, C.M., R. Thompson, and T.B. Andersson. 2001. In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery. Comb. Chem. High Throughput Screen. 4(3):245-263. McKinney, J.D., A. Richard, C. Waller, M.C. Newman, and F. Gerberick. 2000. The practice of structure activity relationships (SAR) in toxicology. Toxicol. Sci. 56(1):8-17. Meister, G., and T. Tuschl. 2004. Mechanisms of gene slicing by double-stranded RNA. Nature 431(7006):343-349. Mello, C.C., and D. Conte, Jr. 2004. Revealing the world of RNA interference. Nature 431(7006):338-342. Michiels, F., H. van Es, L. van Rompaey, P. Merchiers, B. Francken, K. Pittois, J. van der Schueren, R. Brys, J. Vandersmissen, F. Beirinckx, S. Herman, K. Dokic, H. Klaassen, E. Narinx, A. Hagers, W. Laenen, I. Piest, H. Pavliska,
Tools and Technologies 117 Y. Rombout, E. Langemeijer, L. Ma, C. Schipper, M.D. Raeymaeker, S. Schweicher, M. Jans, K. van Beeck, I.R. Tsang, O. van de Stolpe, P. Tomme, G.J. Arts, and J. Donker. 2002. Arrayed adenoviral expression libraries for functional screening. Nat. Biotechnol. 20(11):1154-1157. OâBrien, P., and J.R. Haskins. 2007. In vitro cytotoxicity assessment. Methods Mol. Biol. 356: 415-425. OâDonoghue, S.I., R.B. Russell, and A. Schafferhans. 2006. Three-dimensional structures in target drug discovery and validation. Pp. 285-308 in In Silico Technologies in Drug Target Identification and Validation, 6th Ed, D. Leon, and S. Markel, eds. Boca Raton, FL: CRC Press. Orton, R.J., O.E. Sturm, V. Vyshemirsky, M. Calder, D.R. Gilbert, and W. Kolch. 2005. Computational modeling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochem. J. 392(Pt. 2):249-261. Paans, A.M., and W. Vaalburg. 2000. Positron emission tomography in drug de- velopment and drug evaluation. Curr. Pharm. Des. 6(16): 1583-1591. Pallardy, M., S. Kerdine, and H. Lebrec. 1998. Testing strategies in immunotoxi- cology. Toxicol. Lett. 102-103:257-260. Park, R.M., and L.T. Stayner. 2006. A search for thresholds and other nonlineari- ties in the relationship between hexavalent chromium and lung cancer. Risk Anal. 26(1):79-88. Peeters, J.K., and P.J. Van der Spek. 2005. Growing applications and advance- ments in microarray technology and analysis tools. Cell Biochem. Biophys. 43(1):149-166. Potier, M., B. Lakhdar, D. Merlet, and J. Cambar. 1995. Interest and limits of hu- man tissue and cell use in pharmacotoxicology. Cell Biol Toxicol. 11(3- 4):133-139. Rehmann, S., and G.C. Jayson. 2005. Molecular imaging of antiangiogenic agents. Oncologist. 10(2):92-103. Sachs, K., O. Perez, D. Pe'er, D.A. Lauffenburger, and G.P. Nolan. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721):523-529. Santos, S.D., P.J. Verveer, and P.I. Bastiaens. 2007. Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat. Cell Biol. 9(3):324-330. Schleger, C., S.J. Platz, and U. Deschl. 2004. Development of an in vitro model for vascular injury with human endothelial cells. ALTEX 21(Suppl. 3):12-19. Schultz, T.W., and J.R. Seward. 2000. Health effects related structure-toxicity rela- tionships: A paradigm for the first decade of the new millennium. Sci. To- tal Environ. 249(1-3):73-84. Schultz, T.W., G.D. Sinks, and A.P. Bearden. 1998. QSAR in aquatic toxicology: A mechanism of action approach comparing toxic potency to Pimephales pro-
118 Toxicity Testing in the 21st Century melas, Tetrahymena pyriformis, and Vibrio fischeri. Pp. 51-110 in Comparative QSAR, J. Devillers, ed. London: Taylor and Francis. Shi, M.M., M.R. Bleavins, and F.A. de la Iglesia. 1999. Technologies for detecting genetic polymorphisms in pharmacogenomics. Mol. Diagn. 4(4):343-351. Simon-Hettich, B., A. Rothfuss, and T. Steger-Hartmann. 2006. Use of computer- assisted prediction of toxic effects of chemical substances. Toxicology 224(1-2):156-162. Slikker, W., Jr., M.E. Andersen, M.S. Bogdanffy, J.S. Bus, S.D. Cohen, R.B. Co- nolly, R.M. David, N.G. Doerrer, D.C. Dorman, D.W. Gaylor, D. Hattis, J.M. Rogers, R.W. Setzer, J.A. Swenberg, and K. Wallace. 2004a. Dose- dependent transitions in mechanisms of toxicity: Case studies. Toxicol. Appl. Pharmacol. 201(3):226-294. Slikker, W., Jr., M.E. Andersen, M.S. Bogdanffy, J.S. Bus, S.D. Cohen, R.B. Co- nolly, R.M. David, N.G. Doerrer, D.C. Dorman, D.W. Gaylor, D. Hattis, J.M. Rogers, R. Woodrow Setzer, J.A. Swenberg, and K. Wallace. 2004b. Dose-dependent transitions in mechanisms of toxicity. Toxicol. Appl. Pharmacol. 201(3):203-225. Slikker, W., Z. Xu, and C. Wang. 2005. Application of a systems biology approach to developmental neurotoxicology. Reprod. Toxicol. 19(3):305-319. Snitkin, E.S., A.M. Gustafson, J. Mellor, J. Wu, and C. DeLisi. 2006. Comparative assessment of performance and genome dependence among phylogenetic profiling methods. BMC Bioinformatics 7:420. Soffers, A.E., M.G. Boersma, W.H. Vaes, J. Vervoort, B. Tyrakowska, J.L. Her- mens, I.M. Rietjens. 2001. Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity. Toxicol. In Vitro 15(4- 5):539-551. Spielmann, H., and M. Liebsch. 2001. Lessons learned from validation of in vitro toxicity test: From failure to acceptance into regulatory practice. Toxicol. In Vitro 15(4-5):585-590. Subramaniam, R.P., K.S. Crump, C. Chen, P. White, C. Van Landingham, J.F. Fox, P. Schlosser, T.R. Covington, D. DeVoney, J.J. Vandenberg, P. Preuss, and J. Whalan. 2006. The role of mutagenicity in describing formaldehyde- induced carcinogenicity: Possible inferences using the ciit model. Presented at the Society of Risk Analysis Annual Meeting, Dec. 3-6, 2006, Baltimore, MD. Suemori, H. 2006. Establishment and therapeutic use of human embryonic stem cell lines. Hum. Cell. 19(2):65-70. Suzuki, N., A. Higashiguchi, Y. Hasegawa, H. Matsumoto, S. Oie, K. Orikawa, S. Ezawa, N. Susumu, K. Miyashita, and D. Aoki. 2005. Loss of integrin al- pha3 expression is associated with acquisition of invasive potential by ovarian clear cell adenocarcinoma cells. Hum. Cell. 8(3):147-155.
Tools and Technologies 119 Tong, W., W.J. Welsh, L. Shi, H. Fang, and R. Perkins. 2003. Structure-activity relationship approaches and applications. Environ. Toxicol. Chem. 22(8): 1680-1695. van den Broek, L.A., E. Lazaro, Z. Zylicz, P.J. Fennis, F.A. Missler, P. Lelieveld, M. Garzotto, D.J. Wagener, J.P. Ballesta, and H.C. Ottenheijm. 1989. Lipo- philic analogues of sparsomycin as strong inhibitors of protein synthesis and tumor growth: A structure-activity relationship study. J. Med. Chem. 32(8):2002-2015. Van der Berg, M., L. Birnbaum, A.T. Bosveld, B. Brunstrom, P. Cook, M. Feeley, J.P. Giesy, A. Hanberg, R. Hasegawa, S.W. Kennedy, T. Kubiak, J.C. Lar- sen, F.X. van Leeuwen, A.K. Liem, C. Nolt, R.E. Peterson, L. Poellinger, S. Safe, D. Schrenk, D. Tillitt, M. Tysklind, M. Younes, F. Waern, and T. Zacharewski. 1998. Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for human and wildlife. Environ. Health Perspect. 106(12):775-792. Vedani, A. 1999. Replacing animal testing by virtual experiments: A challenge in computational biology. Chimia 53(5):227-228. Walker, J.D., M. Enache, and J.C. Dearden. 2003a. Quantitative cationic-activity relationships for predicting toxicity of metals. Environ. Toxicol. Chem. 22(8):1916-1935. Walker, J.D., J. Jaworska, M.H. Comber, T.W. Schultz, and J.C. Dearden. 2003b. Guidelines for developing and using quantitative structure-activity rela- tionships. Environ. Toxicol. Chem. 22(8):1653-1665. Walker, J.D., ed. 2004. Quantitative StructureâActivity Relationships for Pollu- tion Prevention, Toxicity Screening, Risk Assessment, and Web Applica- tions (QSAR II). Pensacola, FL: SETAC Press. Wang, S.L., F.H. Lan, Y.P. Zhuang, H.Z. Li, L.H. Huang, D.Z. Zheng, J. Zeng, L.H. Dong, Z.Y. Zhu, and J.L. Fu. 2006. Microarray analysis of gene- expression profile in hepatocellular carcinoma cell, BEL-7402, with stable suppression of hLRH-1 via a DNA vector-based RNA interference. Yi Chuan Xue Bao. 33(10):881-891. Waring, J.F., and R.G. Ulrich. 2000. The impact of genomics based technologies on drug safety evaluation. Annu. Rev. Pharmacol. Toxicol. 40:335-352. Watanabe, P.G., A.M. Schumann, and R.H. Reitz. 1988. Toxicokinetics in the evaluation of toxicity data. Regul. Toxicol. Pharmacol. 8(4):408-413. Zangar, R.C., S.M. Varnum, and N. Bollinger. 2005. Studying cellular processes and detecting disease with protein microarrays. Drug Metab. Rev. 37(3): 473-487.