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PubMedDate not supplied

Toxicogenomics in drug development.

Toxicogenomics represents the merging of toxicology with technologies that have been developed, together with bioinformatics, to identify and quantify global gene expression changes. It represents a new paradigm in drug development and risk assessment, which promises to generate a wealth of information towards an increased understanding of the molecular mechanisms that lead to drug toxicity and efficacy, and of DNA polymorphisms responsible for individual susceptibility to toxicity. Gene expression profiling, through the use of DNA microarray and proteomic technologies will aid in establishing links between expression profiles, mode of action and traditional toxic endpoints. Such patterns of gene expression, or 'molecular fingerprints' could be used as diagnostic or predictive markers of exposure, that is characteristic of a specific mechanism of induction of that toxic or efficacious effect. It is anticipated that toxicogenomics will be increasingly integrated into all phases of the drug development process particularly in mechanistic and predictive toxicology, and biomarker discovery. This review provides an overview of the expression profiling technologies applied in toxicogenomics. and discusses the promises as well as the future challenges of applying this discipline to the drug development process.

Animals↗

PubMedDate not supplied

Application of toxicogenomics to toxicology: basic concepts in the analysis of microarray data.

Toxicology and the practice of pathology are rapidly evolving in the postgenomic era. Observable treatment related changes have been the hallmark of toxicology studies. Toxicogenomics is a powerful new tool that may show gene and protein changes earlier and at treatment levels below the limits of detection of traditional measures of toxicity. It may also aid in the understanding of toxic mechanisms. It is important to remember that it is only a tool and will provide meaningful results only when properly applied. As is often the case with new experimental tools, the initial utilization is driven more by the technology than application to problem solving. Toxicogenomics is interdisciplinary in nature including at a minimum, pathology, toxicology, and genomics. Most studies will require the input from the disciplines of toxicology, pathology, molecular biology, bioinformatics, biochemistry, and others depending on the types of questions being asked.

Acetaminophen↗

PubMed2005

Prediction of nephrotoxicant action and identification of candidate toxicity-related biomarkers.

A vast majority of pharmacological compounds and their metabolites are excreted via the urine, and within the complex structure of the kidney,the proximal tubules are a main target site of nephrotoxic compounds. We used the model nephrotoxicants mercuric chloride, 2-bromoethylamine hydrobromide, hexachlorobutadiene, mitomycin, amphotericin, and puromycin to elucidate time- and dose-dependent global gene expression changes associated with proximal tubular toxicity. Male Sprague-Dawley rats were dosed via intraperitoneal injection once daily for mercuric chloride and amphotericin (up to 7 doses), while a single dose was given for all other compounds. Animals were exposed to 2 different doses of these compounds and kidney tissues were collected on day 1, 3, and 7 postdosing. Gene expression profiles were generated from kidney RNA using 17K rat cDNA dual dye microarray and analyzed in conjunction with histopathology. Analysis of gene expression profiles showed that the profiles clustered based on similarities in the severity and type of pathology of individual animals. Further, the expression changes were indicative of tubular toxicity showing hallmarks of tubular degeneration/regeneration and necrosis. Use of gene expression data in predicting the type of nephrotoxicity was then tested with a support vector machine (SVM)-based approach. A SVM prediction module was trained using 120 profiles of total profiles divided into four classes based on the severity of pathology and clustering. Although mitomycin C and amphotericin B treatments did not cause toxicity, their expression profiles were included in the SVM prediction module to increase the sample size. Using this classifier, the SVM predicted the type of pathology of 28 test profiles with 100% selectivity and 82% sensitivity. These data indicate that valid predictions could be made based on gene expression changes from a small set of expression profiles. A set of potential biomarkers showing a time- and dose-response with respect to the progression of proximal tubular toxicity were identified. These include several transporters (Slc21a2, Slc15, Slc34a2), Kim 1, IGFbp-1, osteopontin, alpha-fibrinogen, and Gstalpha.

Animals↗

PubMed2006

Molecular biology of the nasal airways: how do we assess cellular and molecular responses in the nose?

We summarize studies herein that relate to use of molecular techniques to assess mechanism of toxicant and carcinogen action on the nasal mucosa. Specifically, we present the results of an in vivo mutagenesis assay with the herbicide alachlor, which causes olfactory mucosal tumors in rats following dietary administration. A positive response was found in olfactory mucosa after 3 mo of treatment. There was no increase in mutant frequency in the adjacent nasal respiratory mucosa or in liver, which are both non-target tissues for alachlor carcinogenesis. We also summarize previous findings of gene expression studies. One on these was a GeneChip experiment aimed at elucidating the mechanism of alachlor olfactory carcinogenesis, wherein we found that oxidative stress and gelatinase genes were upregulated early in the carcinogenic process, while genes consistent with activation of Wnt signaling were activated later in the carcinogenic process. The final example presented summarizes the results of a microarray experiment designed to identify novel olfactory genes involved in the plasticity of the olfactory mucosa. Those studies identified novel olfactory mucosal genes including Sgpl1 and Pon1. In each instance, precise sampling is emphasized and proper controls are discussed, and examples of independent means of validation of genomics experiments are presented.

Acetamides↗

PubMed2006

Optimal sampling of rat liver tissue for toxicogenomic studies.

Different degrees of a toxic response between and within the various lobes of the liver have been observed in rodents following treatment with acetaminophen. This study was designed to compare 2 sampling methods of the rat liver for gene-expression analysis. Ten male Fischer 344/N rats, 12-14 weeks of age, were treated with vehicle (0.5% aqueous ethyl cellulose) or acetaminophen (APAP, 1500 mg/kg) and sacrificed 24 hours following dose administration. Two representative sections were collected from the left liver lobe, stained with hematoxylin and eosin (H&E), and evaluated independently by 2 pathologists. The central core of the left lobe was cubed and frozen. Five random cubes were conserved, while the remaining left lobe core was pulverized. From each of the 10 animals, 2 random cubes and 2 samples from the homogeneous, pulverized samples were prepared for microarray analysis. Histopathologic evaluation revealed a variable response of centrilobular necrosis within the left lobe. Multiple methods used to analyze the microarray data indicated that sampling technique was not a major contributor to the variability observed in the gene expression data; however, only the powdered samples clustered for all animals, even those with disparate histopathologic results. Additionally, a powdered sample provided the advantages of a homogenous sample pool and the ability to use sample aliquots for other analyses to include proteomics, metabonomics, and other molecular techniques.

Acetaminophen↗

PubMed2006

Application of visualization tools to the analysis of histopathological data enhances biological insight and interpretation.

Gene expression profiling, metabolomic screens, and other high-dimensional methods have become an integral part of many biological investigations. To facilitate interpretation of these data, it is important to have detailed phenotypic data--including histopathology--to which these data can be associated, or anchored. However, as the amount of phenotypic data increases, associations within and across these data can be difficult to visualize and interpret. We have developed an approach for categorizing and clustering biologically related histopathological diagnoses to facilitate their visualization, thereby increasing the possibility of identifying associations and facilitating the comparison with other data streams. In this study, we utilize histopathological data generated as part of a standardized toxicogenomics compendium study to generate composite histopathological scores and to develop visualizations that facilitate biological insight. The validity of this approach is illustrated by the identification of transcripts that correlate with the pathology diagnoses that comprise the categories of "response to hepatocellular injury" and "repair." This approach is broadly applicable to studies in which histopathology is used to phenotypically anchor other data, and results in visualizations that facilitate biological interpretation and the identification of associations and relationships within the data.

Animals↗

PubMed2006

Molecular mechanisms of 2,3,7,8-tetrachlorodibenzo-p-dioxin cardiovascular embryotoxicity.

2,3,7,8 Tetrachlorodibenzo-p-dioxin (TCDD) and related planar halogenated aromatic hydrocarbons are widespread environmental contaminants and potent developmental toxicants. Hallmarks of embryonic exposure include edema, hemorrhage, and mortality. Recent studies in zebrafish and chicken have revealed direct impairment of cardiac muscle growth that may underlie these overt symptoms. TCDD toxicity is mediated by the aryl hydrocarbon receptor, but downstream targets remain unclear. Oxidative stress and growth factor modulation have been implicated in TCDD cardiovascular toxicity. Gene expression profiling is elucidating additional pathways by which TCDD might act. We review our understanding of the mechanism of TCDD embryotoxicity at morphological and molecular levels.

Animals↗

PubMedDate not supplied

Ethics and research assessing the relative roles of genes and the environment.

Research on the interaction of genes and the environment is revealing that many human diseases have both genetic and environmental components. Even traditional "environmental" diseases, such as infections, appear to interact with genetic components in the human host. Environmental genetics research will inevitably increase understanding of individual susceptibilities to toxic exposures in the environment and harmful side effects of medications; therefore, it has great promise for improving the prevention and treatment of human diseases. However, realizing the benefits of this research requires careful attention to ethical issues that are particularly relevant in this context. This article reviews some of the most pressing issues related to research design and methods, as well as from the application of research results (e.g., workplace genetic screening and legal toxic torts, personal medical responsibility, and the relationship between genetics and public health measures).

Environmental Exposure↗

PubMed2005

Polypropylenimine dendrimer-induced gene expression changes: the effect of complexation with DNA, dendrimer generation and cell type.

Polypropylenimine (PPI) dendrimers appear attractive non-viral vectors for the delivery of genes, antisense oligonucleotides, and small interfering RNA (siRNA). However, the effects of these synthetic gene delivery vectors on global gene expression are poorly understood. Here we have examined the toxicogenomics of generation 2 (DAB-8) and generation 3 (DAB-16) PPI dendrimers in two human cell lines. At concentrations and treatment protocols routinely used for gene and oligonucleotide transfection, PPI dendrimers alone elicited marked changes in endogenous gene expression in A431 epithelial cells. The extent of PPI-induced gene changes appeared to be dependent on the dendrimer generation as the number of genes affected was greater with G3 compared to G2 PPI dendrimers in A431 cells. The signature of DAB16-induced gene changes in A549 cells was different to those elicited in A431 cells implying a strong dependence on cell type. The DAB-16 polymer complexed with DNA (dendriplexes) also elicited marked gene expression changes in A549 cells but with a signature that was different from the polymer alone implying that dendriplexes are "recognised" by cells as chemical entities that are distinct from the polymer alone. Alterations in expression of a variety of gene ontologies were observed including those involved in defence responses, cell proliferation and apoptosis. Although there was a tendency for increased DNA damage in cells treated with DAB16 alone or its DNA dendriplexes as detected by the COMET assay, these differences were not statistically significant. These data show for the first time that PPI-dendrimers, separate from their capability as transfection reagents, can intrinsically alter the expression of many endogenous genes that could potentially lead to them exerting multiple biological effects in cells. The impact and consequences of polymer-induced gene changes should guide their rational use as delivery systems for gene-based therapeutics.

Cell Line↗

PubMedDate not supplied

Application of emerging technologies in toxicology and safety assessment: regulatory perspectives.

Emerging technologies applied in the regulatory field encompass a group of technologies that are used in addition to or in replacement of the standard toxicology studies conducted to support an Investigational New Drug Application (IND) or New Drug Application (NDA). The standard package includes general toxicology studies of various duration, safety pharmacology studies, genetic toxicology studies, and reproductive toxicology studies. New and emerging technologies applied to the regulation of new drugs include the use of novel biomarkers, transfected cells and transgenic animals, and the "omics" technologies (toxicogenomics, proteomics, and metabonomics). These technologies are at various stages of regulatory development and acceptance. For example, the use of transgenic animals have gained acceptance by regulatory authorities to replace a 2-year carcinogenicity assay. Alternatively, the "omics" technologies are not sufficiently advanced to achieve regulatory acceptance as replacements, although these assays have a role early in drug development and they may prove useful as supplements to standard studies. Data from these assays have been used to address specific mechanistic questions in combination with standard toxicology assays.

Consumer Product Safety↗

PubMedDate not supplied

Genomics and the search for novel biomarkers in toxicology.

The advent of 'genomics' technology, in particular transcript profiling, has already had a measurable impact on the drug discovery process in the areas of target identification and validation. This review is concerned with the potential application of this technology to toxicology and drug safety assessment, with particular emphasis on biomarker discovery and characterization. An advantage (or possibly a drawback!) of transcript profiling is that candidate biomarkers of toxicity can be speedily identified, with the caveat that a significant amount of subsequent experimental and bioinformatic effort needs to be expended in order to evaluate and validate them. Attention is also drawn to the critical need for robust experimental design with studies of this type and to issues associated with the analysis of large data sets. In summary, while genomics technology undoubtedly offers much that can assist drug safety assessment, its potential has yet to be realized fully in this area. However, a large amount of resource continues to be applied to 'toxicogenomics'. Tangible benefits, in terms of new biomarkers of toxicity and reduced numbers of adverse drug effects, remain realistic objectives.

Animals↗

PubMed2004

Gene expression profiling in spleens of deoxynivalenol-exposed mice: immediate early genes as primary targets.

Exposure to the trichothecene mycotoxin deoxynivalenol (DON) alters immune functions in vitro and in vivo. To gain further insight into DON's immunotoxic effects, microarrays were used to determine how acute exposure to this mycotoxin modulates gene expression profiles in murine spleen. B6C3F1 mice were treated orally with 25mg/kg body weight DON, and 2h later spleens were collected for macroarray analysis. Following normalization using a local linear regression model, expression of 116 out of 1176 genes was significantly altered compared to average expression levels in all treatment groups. When genes were arranged into an ontology tree to facilitate comparison of expression profiles between treatment groups, DON was found primarily to modulate genes associated with immunity, inflammation, and chemotaxis. Real-time polymerase chain reaction was used to confirm modulation for selected genes. DON was found to induce the cytokines interleukin (IL)-1alpha, IL-1beta, IL-6 and IL-11. In analogous fashion, DON upregulated expression of the chemokines macrophage inhibitory protein-2 (MIP-2), cytokine-induced chemoattractant protein-1 (CINC-1), monocyte chemoattractant protein (MCP)-1, MCP-3, and cytokine-responsive gene-2 (CRG-2). c-Fos, Fra-, c-Jun, and JunB, components of the activator protein-1 (AP-1) transcription factor complex, were induced by DON as well as another transcription factor, NR4A1. Four hydrolases were found to be upregulated by DON, including mitogen-activated protein kinase phosphatase 1 (MKP1), catalytic subunit beta isoform (CnAbeta), protein tyrosine phosphatase receptor type J (Ptprj), and protein tyrosine phosphatase nonreceptor type 8 (Ptpn8), whereas three other hydrolases, microsomal epoxide hydrolase (Eph) 1, histidine triad nucleotide binding protein (Hint), and proteosome subunit beta type 8 (Psmb8) were significantly decreased by the toxin. Finally, cysteine-rich protein 61 (CRP61) and heat-shock protein 40 (Hsp40), genes associated with signaling, were increased, while Jun kinase 2 (JNK2) was decreased. Taken together, data suggest that DON upregulated the expression of multiple immediate early genes, many of which are likely to contribute to the complex immunological effects reported for this and other trichothecenes.

Animals↗

PubMed2006

The potential of ecotoxicoproteomics in environmental monitoring: biomarker profiling in mussel plasma using ProteinChip array technology.

New global technologies, allowing simultaneous analysis of thousands of genes, proteins, and metabolites (so-called "omics" technologies), are being adopted rapidly by industry, academia, and regulatory agencies. This study evaluated the potential of proteomics in ecotoxicological research (i.e., ecotoxicoproteomics). Filter-feeding mussels (Mytilus edulis) were exposed continuously for 3 wk to oil, or oil spiked with alkylphenols and extra polycyclic aromatic hydrocarbons. The influence of chronic exposure on mussel plasma protein expression was investigated utilizing ProteinChip array technology in combination with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF MS). Results indicated that exposure to spiked oil had a more significant effect on protein expression in mussels than oil alone. In total, 83 mass peaks (intact or modified proteins/peptides) were significantly altered by spiked oil, while 49 were altered by oil. In exposed organisms, the majority of peaks were upregulated compared to controls (i.e., 69% in oil and 71% in spiked oil). Some peaks (32 in total) were affected by both treatments; however, the degree of response was higher in the spiked oil group for 25 of the 32 commonly affected features. Additionally, certain peaks revealed exposure- or gender-specific responses. Multivariate analysis with regression tree-based methods detected protein patterns associated with exposure that correctly classified masked samples with 90-95% accuracy. Similarly, 92% of females and 85% of males were correctly classified (independent of exposure). Results indicate that proteomics have the potential to make a valuable contribution to environmental monitoring and risk assessment.

Animals↗

PubMed2004

A generalized additive model for microarray gene expression data analysis.

Microarray technology allows the measurement of expression levels of a large number of genes simultaneously. There are inherent biases in microarray data generated from an experiment. Various statistical methods have been proposed for data normalization and data analysis. This paper proposes a generalized additive model for the analysis of gene expression data. This model consists of two sub-models: a non-linear model and a linear model. We propose a two-step normalization algorithm to fit the two sub-models sequentially. The first step involves a non-parametric regression using lowess fits to adjust for non-linear systematic biases. The second step uses a linear ANOVA model to estimate the remaining effects including the interaction effect of genes and treatments, the effect of interest in a study. The proposed model is a generalization of the ANOVA model for microarray data analysis. We show correspondences between the lowess fit and the ANOVA model methods. The normalization procedure does not assume the majority of genes do not change their expression levels, and neither does it assume two channel intensities from the same spot are independent. The procedure can be applied to either one channel or two channel data from the experiments with multiple treatments or multiple nuisance factors. Two toxicogenomic experiment data sets and a simulated data set are used to contrast the proposed method with the commonly known lowess fit and ANOVA methods.

Algorithms↗

PubMed2005

Class prediction in toxicogenomics.

The intent of this article is to discuss some of the complexities of toxicogenomics data and the statistical design and analysis issues that arise in the course of conducting a toxicogenomics study. We also describe a procedure for classifying compounds into various hepatotoxicity classes based on gene expression data. The methodology involves first classifying a compound as toxic or nontoxic and subsequently classifying the toxic compounds into the hepatotoxicity classes, based on votes by binary classifiers. The binary classifiers are constructed by using genes selected to best elicit differences between the two classes. We show that the gene selection strategy improves the misclassification error rates and also delivers gene pathways that exhibit biological relevance.

Algorithms↗

PubMed2004

Where is toxicology headed in the future?

Herbert Remmer was a pioneer in the study of the phenomenon of enzyme induction. He was also a leading figure, if not the foremost, in the development of the discipline of toxicology in Germany during his tenure as Professor of Toxicology at the University of Tübingen. Included here are some brief thoughts about where toxicology came from, where it is today, and what the future is. Toxicology is at a crossroads today, at an interface between basic science and applied projects. From its past as a descriptive discipline, it has incorporated a medley of concepts and technology from basic science. The usefulness of many approaches is now being evaluated. The hope is that toxicology will be able to be much more predictive in the future; a great need exists in the pharmaceutical industry. The shape of academic toxicology is also changing.

Animals↗

PubMed2004

Sample size for identifying differentially expressed genes in microarray experiments.

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.

Animals↗

PubMed2006

On methods for gene function scoring as a means of facilitating the interpretation of microarray results.

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the underlying mechanisms. A few methods have been proposed in the literature to formally convert individual gene results into gene function results. In this paper, we will compare the various methods, propose and examine some new ones, and offer a structured approach to incorporating gene function or pathway information into the analysis of expression data. We study the performance of the various methods and also compare them on real data, using a case study from the toxicogenomics area. Our results show that the approaches based on gene function scores yield a different, and functionally more interpretable, array of genes than methods that rely solely on individual gene scores. They also suggest that functional class scoring methods appear to perform better and more consistently than overrepresentation analysis and distributional score methods.

Databases, Genetic↗