[Genetic research: chance or risk for health?].
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Toxicogenomics in drug discovery and drug development: potential applications and future challenges.
Despite the massive investments made by pharmaceutical companies on drug research and development, the number of new drug approvals has remained stagnant in the past decades. It is well known that developing safe and effective new drugs is a long, difficult, and expensive process. While the cost of developing new drugs is increasing rapidly, withdrawals of drugs from the marketplace due to adverse drug reactions (ADR) and/or toxicity is increasing concurrently. The recent advent of high-throughput in silico (computer softwares) and in vitro (cell cultures) screenings have somewhat alleviated some, but not all, of these challenges by providing an efficient and effective way for developing safer and better drugs. This emerging technology, known as toxicogenomics, has great potential to facilitate the development of methodologies that could predict the long-term toxic effects of compounds using relatively short-term bioassays. This review is aimed at discussing the potential applications and future challenges of toxicogenomics in drug discovery and drug development.
Animals↗
Toxicogenomics of A375 human malignant melanoma cells treated with arbutin.
Although arbutin is a natural product and widely used as an ingredient in skin care products, its effect on the gene expression level of human skin with malignant melanoma cells is rarely reported. We aim to investigate the genotoxic effect of arbutin on the differential gene expression profiling in A375 human malignant melanoma cells through its effect on tumorigenesis and related side-effect. The DNA microarray analysis provided the differential gene expression pattern of arbutin-treated A375 cells with the significant changes of 324 differentially expressed genes, containing 88 up-regulated genes and 236 down-regulated genes. The gene ontology of differentially expressed genes was classified as belonging to cellular component, molecular function and biological process. In addition, four down-regulated genes of AKT1, CLECSF7, FGFR3, and LRP6 served as candidate genes and correlated to suppress the biological processes in the cell cycle of cancer progression and in the downstream signaling pathways of malignancy of melanocytic tumorigenesis.
Arbutin↗
Polymer genomics: an insight into pharmacology and toxicology of nanomedicines.
Synthetic polymers and nanomaterials display selective phenotypic effects in cells and in the body signal transduction mechanisms involved in inflammation, differentiation, proliferation, and apoptosis. When physically mixed or covalently conjugated with cytotoxic agents, bacterial DNA or antigens, polymers can drastically alter specific genetically controlled responses to these agents. These effects, in part, result from cooperative interactions of polymers and nanomaterials with plasma cell membranes and trafficking of polymers and nanomaterials to intracellular organelles. Cells and whole organism responses to these materials can be phenotype or genotype dependent. In selected cases, polymer agents can bypass limitations to biological responses imposed by the genotype, for example, phenotypic correction of immune response by polyelectrolytes. Overall, these effects are relatively benign as they do not result in cytotoxicity or major toxicities in the body. Collectively, however, these studies support the need for assessing pharmacogenomic effects of polymer materials to maximize clinical outcomes and understand the pharmacological and toxicological effects of polymer formulations of biological agents, i.e. polymer genomics.
Animals↗
Ecotoxicogenomics: the challenge of integrating genomics into aquatic and terrestrial ecotoxicology.
Rapid progress in the field of genomics (the study of how an individual's entire genetic make-up, the genome, translates into biological functions) is beginning to provide tools that may assist our understanding of how chemicals can impact on human and ecosystem health. In many ways, if scientific and regulatory efforts in the 20th century have sought to establish which chemicals cause damage to ecosystems, then the challenge in ecotoxicology for the 21st century is to understand the mechanisms of toxicity to different wildlife species. In the human context, 'toxicogenomics' is the study of expression of genes important in adaptive responses to toxic exposures and a reflection of the toxic processes per se. Given the parallel implications for ecological (environmental) risk assessment, we propose the term 'ecotoxicogenomics' to describe the integration of genomics (transcriptomics, proteomics and metabolomics) into ecotoxicology. Ecotoxicogenomics is defined as the study of gene and protein expression in non-target organisms that is important in responses to environmental toxicant exposures. The potential of ecotoxicogenomic tools in ecological risk assessment seems great. Many of the standardized methods used to assess potential impact of chemicals on aquatic organisms rely on measuring whole-organism responses (e.g. mortality, growth, reproduction) of generally sensitive indicator species at maintained concentrations, and deriving 'endpoints' based on these phenomena (e.g. median lethal concentrations, no observed effect concentrations, etc.). Whilst such phenomenological approaches are useful for identifying chemicals of potential concern they provide little understanding of the mechanism of chemical toxicity. Without this understanding, it will be difficult to address some of the key challenges that currently face aquatic ecotoxicology, e.g. predicting toxicant responses across the very broad diversity of the phylogenetic groups present in aquatic ecosystems; estimating how changes at one ecological level or organisation will affect other levels (e.g. predicting population-level effects); predicting the influence of time-varying exposure on toxicant responses. Ecotoxicogenomic tools may provide us with a better mechanistic understanding of aquatic ecotoxicology. For ecotoxicogenomics to fulfil its potential, collaborative efforts are necessary through the parallel use of model microorganisms (e.g. Saccharomyces cerevisiae) together with aquatic (e.g. Danio rerio, Daphnia magna, Lemna minor and Xenopus tropicalis) and terrestrial (e.g. Arabidopsis thailiana, Caenorhabdites elegans and Eisenia foetida) plants, animals and microorganisms.
Ecology↗
Toxicogenomics in drug discovery: from preclinical studies to clinical trials.
Gene expression analysis applied to toxicology studies, also referred to as toxicogenomics, is rapidly being embraced by the pharmaceutical industry as a useful tool to identify safer drugs in a quicker, more cost-effective manner. Studies have already demonstrated the benefits of applying gene expression profiling towards drug safety evaluation, both for identifying mechanisms underlying toxicity, as well as for providing a means to identify safety liabilities early in the drug discovery process. Furthermore, toxicogenomics has the potential to better identify and assess adverse drug reactions of new drug candidates or marketed products in humans. While much still remains to be learned about the relevance and the application of gene expression changes in human toxicology, the next few years should see gene expression technologies applied to more stages and more programs of the drug discovery and development process. This review will focus on how toxicogenomics can or has been applied in drug discovery and development, and will discuss some of the challenges that still remain.
Animals↗
DNA microarray technology in toxicogenomics of aquatic models: methods and applications.
Toxicogenomics represents the merging of toxicology with genomics and bioinformatics to investigate biological functions of genome in response to environmental contaminants. Aquatic species have traditionally been used as models in toxicology to characterize the actions of environmental stresses. Recent completion of the DNA sequencing for several fish species has spurred the development of DNA microarrays allowing investigators access to toxicogenomic approaches. However, since microarray technology is thus far limited to only a few aquatic species and derivation of biological meaning from microarray data is highly dependent on statistical arguments, the full potential of microarray in aquatic species research has yet to be realized. Herein we review some of the issues related to construction, probe design, statistical and bioinformatical data analyses, and current applications of DNA microarrays. As a model a recently developed medaka (Oryzias latipes) oligonucleotide microarray was described to highlight some of the issues related to array technology and its application in aquatic species exposed to hypoxia. Although there are known non-biological variations present in microarray data, it remains unquestionable that array technology will have a great impact on aquatic toxicology. Microarray applications in aquatic toxicogenomics will range from the discovery of diagnostic biomarkers, to establishment of stress-specific signatures and molecular pathways hallmarking the adaptation to new environmental conditions.
Animals↗
Toxicogenomics in predictive toxicology in drug development.
The goal of toxicology is the assessment of possible risk to man. An emerging technology with the potential to have a major impact on risk assessment is toxicogenomics. In this review, we provide an overview of the many possibilities for toxicogenomics including technology platforms, data interpretation, and regulatory perspective and we give examples of toxicogenomics investigations. Toxicogenomics is a powerful tool for compound classification, for mechanistic studies, and for the detection of toxicity markers. Thus, toxicogenomics helps in the extrapolation of findings across species and increases predictability. Biomarkers are valuable in the evaluation of compounds at earlier development phases, improving clinical candidate selection. Caution regarding the interpretation of the results is still necessary. Nevertheless, toxicogenomics will accelerate preclinical safety assessments and improve the prediction of toxic liabilities, as well as of potential risk accumulation for drug-drug or drug-disease interactions.
Biomarkers↗
A method for diagnosis of plant environmental stresses by gene expression profiling using a cDNA macroarray.
Plants in the field are subjected to numerous environmental stresses. Lengthy continuation of such environmental stresses or a rapid increase in their intensity is harmful to vegetation. Assessments of the phytotoxicity of various stresses have been performed in many countries, although they have largely been based on estimates of leaf injury. We developed a novel method of detecting plant stresses that is more sensitive and specific than those previously available. This method is based on the detection of mRNA expression changes in 205 ozone-responsive Arabidopsis expressed sequence tags (ESTs) by cDNA macroarray analysis. By using this method, we illustrated shifts in gene expression in response to stressors such as drought, salinity, UV-B, low temperature, high temperature, and acid rain, as distinct from those in response to ozone. We also made a mini-scale macroarray with 12 ESTs for diagnosis of the above environmental stresses in plants. These results illustrate the potential of our cDNA macroarray for diagnosis of various stresses in plants.
Air Pollution↗
A biomarker model of sublethal genotoxicity (DNA single-strand breaks and adducts) using the sentinel organism Aporrectodea longa in spiked soil.
There is a need to develop risk biomarkers during the remediation of contaminated land. We employed the earthworm, Aporrectodea longa (Ude), to determine whether genotoxicity measures could be applied to this organism's intestinal tissues. Earthworms were added, for 24h or 7 days, to soil samples spiked with benzo[a]pyrene (B[a]P) and/or lindane. After exposure, intestinal tissues (crop/gizzard or intestine) were removed prior to the measurement in disaggregated cells of DNA single-strand breaks (SSBs) by the alkaline comet assay. Damage was quantified by comet tail length (CTL, microm). B[a]P 24-h exposure induced dose-related increases (P<0.0001) in SSBs. Earthworm intestine was significantly (P<0.0001) more susceptible than crop/gizzard to B[a]P and/or lindane. However, both tissues appeared to acquire resistance following 7-day exposure. B[a]P-DNA adducts, measured by (32)P-postlabelling, showed a two-adduct-spot pattern. This preliminary investigation suggests that earthworm tissues may be incorporated into genotoxicity assays to facilitate hazard identification within terrestrial ecosystems.
Animals↗
Effect of the difference in vehicles on gene expression in the rat liver--analysis of the control data in the Toxicogenomics Project Database.
The Toxicogenomics Project is a 5-year collaborative project by the Japanese government and pharmaceutical companies in 2002. Its aim is to construct a large-scale toxicology database of 150 compounds orally administered to rats. The test consists of a single administration test (3, 6, 9 and 24 h) and a repeated administration test (3, 7, 14 and 28 days), and the conventional toxicology data together with the gene expression data in liver as analyzed by using Affymetrix GeneChip are being accumulated. In the project, either methylcellulose or corn oil is employed as vehicle. We examined whether the vehicle itself affects the analysis of gene expression and found that corn oil alone affected the food consumption and biochemical parameters mainly related to lipid metabolism, and this accompanied typical changes in the gene expression. Most of the genes modulated by corn oil were related to cholesterol or fatty acid metabolism (e.g., CYP7A1, CYP8B1, 3-hydroxy-3-methylglutaryl-Coenzyme A reductase, squalene epoxidase, angiopoietin-like protein 4, fatty acid synthase, fatty acid binding proteins), suggesting that the response was physiologic to the oil intake. Many of the lipid-related genes showed circadian rhythm within a day, but the expression pattern of general clock genes (e.g., period 2, arylhydrocarbon nuclear receptor translocator-like, D site albumin promoter binding protein) were unaffected by corn oil, suggesting that the effects are specific for lipid metabolism. These results would be useful for usage of the database especially when drugs with different vehicle control are compared.
Animals↗
Development of a coral cDNA array to examine gene expression profiles in Montastraea faveolata exposed to environmental stress.
The development of a cDNA array of coral genes and its application to investigate changes in coral gene expression associated with stressful conditions is described. The array includes both well-characterized and previously unidentified coral genes from Acropora cervicornis and Montastraea faveolata. Corals were exposed to either natural or anthropogenic stressors to elicit the expression of stress genes for isolation and incorporation onto the array. A total of 32 genes involved in protein synthesis, apoptosis, cell signaling, metabolism, cellular defense and inflammation were included on the array. Labeled cDNA from coral (Montastraea faveolata) exposed to elevated seawater temperature, salinity and ultraviolet light was tested against the microarray to determine patterns of gene expression associated with each stressor. Carbonic anhydrase, thioredoxin, a urokinase plasminogen activator receptor (uPAR) and three ribosomal genes demonstrated differential expression across all replicates on the array and between replicate colonies. Specific gene expression patterns produced in response to different stressors demonstrate the potential for gene expression profiling in characterizing the coral stress response.
Animals↗
Multi-class clustering and prediction in the analysis of microarray data.
DNA microarray technology provides tools for studying the expression profiles of a large number of distinct genes simultaneously. This technology has been applied to sample clustering and sample prediction. Because of a large number of genes measured, many of the genes in the original data set are irrelevant to the analysis. Selection of discriminatory genes is critical to the accuracy of clustering and prediction. This paper considers statistical significance testing approach to selecting discriminatory gene sets for multi-class clustering and prediction of experimental samples. A toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV with a total of 55 samples) is used to illustrate a general framework of the approach. Among four selected gene sets, a gene set omega(I) formed by the intersection of the F-test and the set of the union of one-versus-all t-tests performs the best in terms of clustering as well as prediction. Hierarchical and two modified partition (k-means) methods all show that the set omega(I) is able to group the 55 samples into seven clusters reasonably well, in which the As and AsV samples are considered as one cluster (the same group) as are the Cd and Cu samples. With respect to prediction, the overall accuracy for the gene set omega(I) using the nearest neighbors algorithm to predict 55 samples into one of the nine treatments is 85%.
Algorithms↗
In pursuit of effective toxicogenomics.
Biological systems exhibit complex responses to xenobiotics varying from generic stress responses to very specific changes closely associated with the mechanism of toxicity. Until recently our view of this complexity was obscured by the simplicity of available analysis tools which allowed determination of only a few genes in any one study. Then genome sequencing and high throughput library screening projects delivered data on the genome sequence of many organisms, and clones were collected and made available to researchers in a previously unparalleled quantity. To exploit this new resource the microarray was developed from its predecessor the dot blot. Further development has expanded the number of clones contained on any one microarray to a point where the expression of many tens of thousands of genes in a biological system can be determined in a short period of time. What these data are revealing is the full complexity of the gene expression response to stimuli such as xenobiotic exposure. Toxicogenomics seeks to use the complexity of this response as a fingerprint or signature characteristic of that xenobiotic exposure. There are though two major experimental challenges that need to be dealt with for toxicogenomics to be successful. The first is technical and relates to the intrinsic difficulties associated with the accurate measurement of gene expression. For microarrays, this problem is multiplied by the number of genes on the microarray itself. To overcome this technical variability correct experimental design is critical. The second challenge concerns the biological system used. What genetic background, time point and dose of xenobiotic should be chosen? For in vitro systems should cell lines or primary cells be used? These factors, and more, could affect the gene expression profile obtained in response to the same xenobiotic exposure. Using both our data and data from public databases these issues are explored in this paper.
Animals↗
Toxicogenomics in genetic toxicology and hazard determination--concluding remarks.
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Animals↗
Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?
Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.
Animals↗
Toxicogenomics in genetic toxicology and hazard determination: introduction and overview.
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Animals↗
Mapping molecular responses to xenoestrogens through Gene Ontology and pathway analysis of toxicogenomic data.
The recent sequencing of mammalian genomes has driven the development of genomic technologies, including microarray-based gene expression profiling, that allow simultaneous measurement of the expression levels of thousands of genes. Gene expression profiling applied to toxicology (toxicogenomics) has the potential to reveal, holistically, the molecular pathways and cellular processes that mediate the adverse responses to a toxicant. However, the initial output of a toxicogenomics experiment consists of a list of genes whose expression is altered upon toxicant exposure. In order to interpret these data in a biological context, new bioinformatic methods must be developed to place gene expression changes in the context of the underlying pathways and processes affected. One emerging approach is the application of Gene Ontology (GO) mapping and pathway analysis to gene expression profiling data. The utility of this in mechanistic toxicology will be illustrated using examples in which GO mapping of toxicogenomic data has provided novel insights into the molecular mechanisms induced by exposure to xenoestrogens.
Animals↗