Toxicogenomics in predictive toxicology in drug development

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Leveraging a High-Throughput Screening Method to Identify Mechanisms of Individual Susceptibility Differences in a Genetically Diverse Zebrafish Model

Understanding the mechanisms behind chemical susceptibility differences is key to protecting sensitive populations. However, elucidating gene-environment interactions (GxE) presents a daunting challenge. While mammalian models have proven useful, problems with scalability to an enormous chemical exposome and clinical translation faced by all models remain; therefore, alternatives are needed. Zebrafish (Danio rerio) have emerged as an excellent model for investigating GxE. This study used a combined bioinformatic and experimental approach to probe the mechanisms underlying chemical susceptibility differences in a genetically diverse zebrafish population. Starting from high-throughput screening (HTS) data, a genome-wide association study (GWAS) using embryonic fish exposed to 0.6 μM Abamectin revealed significantly different effects between individuals. A hypervariable region with two distinct alleles–one with G at the SNP locus (GG) and one with a T and the 16 bp deletion (TT)–associated with differential susceptibility was found. Sensitive fish had significantly lower sox7 expression. Due to their location and the observed expression differences, we hypothesized that these sequences differentially regulate sox7. A luciferase reporter gene assay was used to test if these sequences, alone, could lead to expression differences. The TT allele showed significantly lower expression than the GG allele in MCF-7 cells. To better understand the mechanism behind these expression differences, predicted transcription factor binding differences between individuals were compared in silico, and several putative binding differences were identified. EMSA was used to test for binding differences in whole embryo protein lysate to investigate these TF binding predictions. We confirmed that the GG sequence is bound to protein in zebrafish. Through a competition EMSA using an untagged oligo titration, we confirmed that the GG oligo had a higher binding affinity than the TT oligo, explaining the observed expression differences. This study identified differential susceptibility to chemical exposure in a genetically diverse population, then identified a plausible mechanism behind those differences from a genetic to molecular level. Thus, an HTS-compatible zebrafish model is valuable and adaptable in identifying GxE mechanisms behind susceptibility differences to chemical exposure.

Epigenetic Alteration Shaped by the Environmental Chemical Bisphenol A

Bisphenol A (BPA) is extensively used in plastic products and epoxy resins. The epigenetic response to the environmental chemical BPA was involved in multiple dysfunctional categories, such as cancer, the reproductive system, metabolism, pubertal development, peripheral arterial disease, infant and childhood growth, and neurodevelopment outcomes. In this mini-review, we described the recent progress of the epigenetic effects of the environmental chemical BPA, including DNA methylation, histone methylation, and toxic epigenomics. Notably, the histone modification changes under BPA exposure are summarized in this review. DNA methylation accompanied by transcriptional changes in key genes affected by BPA exposure is related to various processes, including neural development, cancer pathways, and generational transmission. In addition, BPA could also affect histone modifications in many species, such as humans, rats, and zebrafish. Finally, we reviewed recent studies of the toxico-epigenomics approach to reveal the epigenetic effect of BPA exposure genome-wide.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles

The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic-induced injury. Xenobiotic-induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine-learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

Gene expression profiling is a useful tool to predict and interrogate mechanisms of toxicity. RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for conducting transcriptional profiling. The objective of this work was to compare both transcriptomic platforms to determine whether RNA-Seq offered significant advantages over microarrays for toxicogenomic studies. RNA samples from the livers of rats treated for 5 days with five tool hepatotoxicants (α-naphthylisothiocyanate/ANIT, carbon tetrachloride/CCl₄, methylenedianiline/MDA, acetaminophen/APAP, and diclofenac/DCLF) were analyzed with both gene expression platforms (RNA-Seq and microarray). Data were compared to determine any potential added scientific (i.e., better biological or toxicological insight) value offered by RNA-Seq compared to microarrays. RNA-Seq identified more differentially expressed protein-coding genes and provided a wider quantitative range of expression level changes when compared to microarrays. Both platforms identified a larger number of differentially expressed genes (DEGs) in livers of rats treated with ANIT, MDA, and CCl₄ compared to APAP and DCLF, in agreement with the severity of histopathological findings. Approximately 78% of DEGs identified with microarrays overlapped with RNA-Seq data, with a Spearman’s correlation of 0.7 to 0.83. Consistent with the mechanisms of toxicity of ANIT, APAP, MDA and CCl₄, both platforms identified dysregulation of liver relevant pathways such as Nrf2, cholesterol biosynthesis, eiF2, hepatic cholestasis, glutathione and LPS/IL-1 mediated RXR inhibition. RNA-Seq data showed additional DEGs that not only significantly enriched these pathways, but also suggested modulation of additional liver relevant pathways. In addition, RNA-Seq enabled the identification of non-coding DEGs that offer a potential for improved mechanistic clarity. Overall, these results indicate that RNA-Seq is an acceptable alternative platform to microarrays for rat toxicogenomic studies with several advantages. Because of its wider dynamic range as well as its ability to identify a larger number of DEGs, RNA-Seq may generate more insight into mechanisms of toxicity. However, more extensive reference data will be necessary to fully leverage these additional RNA-Seq data, especially for non-coding sequences.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

The Current Status of Drug Discovery and Development as Originated in United States Academia: The Influence of Industrial and Academic Collaboration on Drug Discovery and Development

Academic drug discovery is a vital component to current drug discovery and development environments. In this study, we investigated 798 drug discovery projects that took place between 1991 and 2015 at 36 academic institutions in the United States. The observed success rates of academic drug discovery and development were 75% at phase I, 50% at phase II, 59% at phase III, and 88% at the new drug application/biologics license application (NDA/BLA) phase. These results were similar to the corresponding success rates of the pharmaceutical industry. Collaboration between academic institutions and the pharmaceutical industry seemed more important at later stages than earlier ones; all projects that succeeded at phase III or the NDA/BLA stage involved academic‐industrial collaboration. Many academic research projects involved neoplasms and infectious diseases, and were focused on small molecules and biologics. The success rates and possible effects of academic‐industrial collaboration seemed to vary depending on disease domains and drug modalities.

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.

Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

A review of toxicity and mechanisms of individual and mixtures of heavy metals in the environment

The rational for the study was to review the literature on the toxicity and corresponding mechanisms associated with lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As), individually and as mixtures, in the environment. Heavy metals are ubiquitous and generally persist in the environment, enabling them to biomagnify in the food chain. Living systems most often interact with a cocktail of heavy metals in the environment. Heavy metal exposure to biological systems may lead to oxidation stress which may induce DNA damage, protein modification, lipid peroxidation, and others. In this review, the major mechanism associated with toxicities of individual metals was the generation of reactive oxygen species (ROS). Additionally, toxicities were expressed through depletion of glutathione and bonding to sulfhydryl groups of proteins. Interestingly, a metal like Pb becomes toxic to organisms through the depletion of antioxidants while Cd indirectly generates ROS by its ability to replace iron and copper. ROS generated through exposure to arsenic were associated with many modes of action, and heavy metal mixtures were found to have varied effects on organisms. Many models based on concentration addition (CA) and independent action (IA) have been introduced to help predict toxicities and mechanisms associated with metal mixtures. An integrated model which combines CA and IA was further proposed for evaluating toxicities of non-interactive mixtures. In cases where there are molecular interactions, the toxicogenomic approach was used to predict toxicities. The high-throughput toxicogenomics combines studies in genetics, genome-scale expression, cell and tissue expression, metabolite profiling, and bioinformatics.

Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data

BACKGROUND

The potential for a compound to cause hepatotoxicity and nephrotoxicity is a matter of extreme interest for human health risk assessment. To assess liver and kidney toxicity, repeated-dose toxicity (RDT) studies are conducted mainly on rodents. However, these tests are expensive, time-consuming and require large numbers of animals. For early toxicity screening, in silico models can be applied, reducing the costs, time and animals used. Among in silico approaches, structure-activity relationship (SAR) methods, based on the identification of chemical substructures (structural alerts, SAs) related to a particular activity (toxicity), are widely employed.

RESULTS

We identified and evaluated some SAs related to liver and kidney toxicity, using RDT data on rats taken from the hazard evaluation support system (HESS) database. We considered only SAs that gave the best percentages of true positives (TP).

CONCLUSIONS

It was not possible to assign an unambiguous mode of action for all the SAs, but a mechanistic explanation is provided for some of them. Such achievements may help in the early identification of liver and renal toxicity of substances.

Development of novel tools for the in vitro investigation of drug-induced liver injury

INTRODUCTION

Due to its complex mechanisms and unpredictable occurrence, drug-induced liver injury (DILI) complicates drug identification and classification. Since species-specific differences in metabolism and pharmacokinetics exist, data obtained from animal studies may not be sufficient to predict DILI in humans.

AREAS COVERED

Over the last few decades, numerous in vitro models have been developed to replace animal testing. The advantages and disadvantages of commonly used liver-derived in vitro models (e.g., cell lines, hepatocyte models, liver slices, three-dimensional (3D) hepatospheres, etc.) are discussed. Toxicogenomics-based methodologies (genomics, epigenomics, transcriptomics, proteomics and metabolomics) and next-generation sequencing have also been used to enhance the reliability of DILI prediction. This review presents an overview of the currently used alternative toxicological models and of the most advanced approaches in the field of DILI research.

EXPERT OPINION

It seems unlikely that a single in vitro system will be able to mimic the complex interactions in the human liver. Three-dimensional multicellular systems may bridge the gap between conventional 2D models and in vivo clinical studies in humans and provide a reliable basis for hepatic toxicity assay development. Next-generation sequencing technologies, in comparison to microarray-based technologies, may overcome the current limitations and are promising for the development of predictive models in the near future.

Approaches and perspectives to toxicogenetics and toxicogenomics

Toxicology is one of the scientific disciplines that has most<br />evolved in recent years due to scientific and technological<br />advances that have created a deeper understanding of the<br />genetic and molecular basis for appreciative variability in<br />toxic response from one person to another. The application<br />of this knowledge in toxicology is known as toxicogenetics<br />and toxicogenomics. The latter is the discipline that studies<br />the genomic response of organisms exposed to chemical<br />agents, including drugs, environmental pollutants, food<br />additives, and other commonly used chemical products.<br />The use of emerging omic technologies, such as genomics,<br />transcriptomics, proteomics, metabolomics and bioinformatics<br />techniques, permits the analysis of many variants of genes<br />simultaneously in an organism exposed to toxic agents in order<br />to search for genes susceptible to damage, to detect patterns<br />and mechanisms of toxicity, and determine specific profiles<br />of gene expression that give origin to biomarkers of exposure<br />and risk. This constitutes predictive toxicology.

Identification of drug-induced toxicity biomarkers for treatment determination

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

The acute hepatotoxicity of tacrine explained by 1H NMR based metabolomic profiling

NMR based metabolomics approach was applied to study the mechanism of tacrine-induced acute hepatotoxicity and had found significant disturbances.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

Cross-species toxicogenomic analyses and phenotypic anchoring in response to groundwater low-level pollution

Background

Comparison of toxicogenomic data facilitates the identification of deregulated gene patterns and maximizes health risk prediction in human.

Results

Here, we performed phenotypic anchoring on the effects of acute exposure to low-grade polluted groundwater using mouse and zebrafish. Also, we evaluated two windows of chronic exposure in mouse, starting in utero and at the end of lactation. Bioinformatic analysis of livers microarray data showed that the number of deregulated biofunctions and pathways is higher after acute exposure, compared to the chronic one. It also revealed specific profiles of altered gene expression in all treatments, pointing to stress response/mitochondrial pathways as major players of environmental toxicity. Of note, dysfunction of steroid hormones was also predicted by bioinformatic analysis and verified in both models by traditional approaches, serum estrogens measurement and vitellogenin mRNA determination in mice and zebrafish, respectively.

Conclusions

In our report, phenotypic anchoring in two vertebrate model organisms highlights the toxicity of low-grade pollution, with varying susceptibility based on exposure window. The overlay of zebrafish and mice deregulated pathways, more than single genes, is useful in risk identification from chemicals implicated in the observed effects.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1067) contains supplementary material, which is available to authorized users.

Toxicity prediction from toxicogenomic data based on class association rule mining

While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the class association rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.

NMR-based metabolomics approach to study the chronic toxicity of crude ricin from castor bean kernels on rats

Ricin, a large, water soluble toxic glycoprotein, is distributed majorly in the kernels of castor beans (the seeds of Ricinus communis L.) and has been used in traditional Chinese medicine (TCM) or other folk remedies throughout the world. The toxicity of crude ricin (CR) from castor bean kernels was investigated for the first time using an NMR-based metabolomic approach complemented with histopathological inspection and clinical chemistry. The chronic administration of CR could cause kidney and lung impairment, spleen and thymus dysfunction and diminished nutrient intake in rats. An orthogonal signal correction partial least-squares discriminant analysis (OSC-PLSDA) of metabolomic profiles of rat biofluids highlighted a number of metabolic disturbances induced by CR. Long-term CR treatment produced perturbations on energy metabolism, nitrogen metabolism, amino acid metabolism and kynurenine pathway, and evoked oxidative stress. These findings could explain well the CR induced nephrotoxicity and pulmonary toxicity, and provided several potential biomarkers for diagnostics of these toxicities. Such a (1)H NMR based metabolomics approach showed its ability to give a systematic and holistic view of the response of an organism to drugs and is suitable for dynamic studies on the toxicological effects of TCM.

Time-series pattern of gene expression profile in gentamycin-induced nephrotoxicity

There have been many studies investigating the genomic biomarker and/or molecular mechanism of nephrotoxicity using microarray. However, most of these researches were carried out by studying gene expression changes at one specific time point. As gene expression varies with time and disease stage, the current study investigated the time-series pattern of gene expression in a rat model using a typical nephrotoxic compound. Rats were administrated with 80 mg/kg gentamycin or saline by intramuscular injection for 14 consecutive days followed by a 28-d recovery. Rats were scarified on D2, D4, D8, D15 and Recovery Day (R29), when kidneys were obtained for whole-genome microarray analysis and histological examination. Urine was collected at each necropsy for kidney injury molecular-1 (KIM-1) analysis. The KIM-1 detection and histological examination confirmed the nephrotoxicity. After differentially expression genes (DEGs) identification, there were 4360 and 4323 regulated genes for females and males, respectively. However, few overlapping expression genes co-regluated at each time point were found. By principle component analysis (PCA) and hierarchical cluster, the gene expression patterns were observed to be apparently associated with the disease stage. GO Annotation showed (1) immune response and related process, response to wounding, cell locomotion on D2; (2) cell death and apoptosis was also noted on D4; (3) processes of organic acid or carboxylic acid, apoptosis or cell death on D8 and D15; (4) processes of cell cycle, mitosis, division cell cycle on R29. In conclusion, the authors mapped the time-series gene expression patterns at the initiation, development and recovery stage of gentamycin-induced nephrotoxicity.

Toxicology testing in drug discovery and development

The primary objective of toxicology studies in the drug discovery process is to evaluate the safety of potential drug candidates. This is accomplished using relevant animal models and validated procedures. The ultimate goal is to translate the animal responses into an understanding of the risk for human subjects. To this end the toxicologist must be aware of the international guidelines for safety evaluation as well as traditional and nontraditional toxicology models. As described in this unit, the typical toxicology profile consists of safety pharmacology, genetic toxicology, acute and subchronic toxicology, absorption, distribution, metabolism, and excretion (ADME) studies, reproductive and developmental toxicity, and an evaluation of carcinogenic potential.

Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure

New approaches are urgently needed to evaluate potential hazards posed by exposure to nanomaterials. Gene expression profiling provides information on potential modes of action and human relevance, and tools have recently become available for pathway-based quantitative risk assessment. The objective of this study was to use toxicogenomics in the context of human health risk assessment. We explore the utility of toxicogenomics in risk assessment, using published gene expression data from C57BL/6 mice exposed to 18, 54 and 162 μg Printex 90 carbon black nanoparticles (CBNP). Analysis of CBNP-perturbed pathways, networks and transcription factors revealed concomitant changes in predicted phenotypes (e.g., pulmonary inflammation and genotoxicity), that correlated with dose and time. Benchmark doses (BMDs) for apical endpoints were comparable to minimum BMDs for relevant pathway-specific expression changes. Comparison to inflammatory lung disease models (i.e., allergic airway inflammation, bacterial infection and tissue injury and fibrosis) and human disease profiles revealed that induced gene expression changes in Printex 90 exposed mice were similar to those typical for pulmonary injury and fibrosis. Very similar fibrotic pathways were perturbed in CBNP-exposed mice and human fibrosis disease models. Our synthesis demonstrates how toxicogenomic profiles may be used in human health risk assessment of nanoparticles and constitutes an important step forward in the ultimate recognition of toxicogenomic endpoints in human health risk. As our knowledge of molecular pathways, dose-response characteristics and relevance to human disease continues to grow, we anticipate that toxicogenomics will become increasingly useful in assessing chemical toxicities and in human health risk assessment.

Cost-effectiveness analysis of microdose clinical trials in drug development

Microdose (MD) clinical trials have been introduced to obtain human pharmacokinetic data early in drug development. Here we assessed the cost-effectiveness of microdose integrated drug development in a hypothetical model, as there was no such quantitative research that weighed the additional effectiveness against the additional time and/or cost. First, we calculated the cost and effectiveness (i.e., success rate) of 3 types of MD integrated drug development strategies: liquid chromatography-tandem mass spectrometry, accelerator mass spectrometry, and positron emission tomography. Then, we analyzed the cost-effectiveness of 9 hypothetical scenarios where 100 drug candidates entering into a non-clinical toxicity study were selected by different methods as the conventional scenario without MD. In the base-case, where 70 drug candidates were selected without MD and 30 selected evenly by one of the three MD methods, incremental cost-effectiveness ratio per one additional drug approved was JPY 12.7 billion (US$ 0.159 billion), whereas the average cost-effectiveness ratio of the conventional strategy was JPY 24.4 billion, which we set as a threshold. Integrating MD in the conventional drug development was cost-effective in this model. This quantitative analytical model which allows various modifications according to each company's conditions, would be helpful for guiding decisions early in clinical development.

Potential new targets involved in 1,3-dinitrobenzene induced testicular toxicity

1,3-Dinitrobenzene (DNB) causes testicular injury, particularly to Sertoli cells, and induces apoptosis in the surrounding germinal cells in rodents; however, the mechanisms causing this toxicity are poorly understood. Our studies, using standard and molecular tools, were conducted to better understand the pathogenesis of the testicular effects. Four daily oral doses of 0.1-8mg/kg/day caused marked testicular lesions in rats from 4mg/kg/day. Global transcriptomics revealed cell cycle and cell death as the major biological processes affected with the expression of genes associated with cell cycle progression ("mitotic roles of polo-like kinase") being particularly altered. In a single dose time course study (4mg/kg), no adverse changes were recorded; however, in contrast to the data from the multiple dose study, plasma testosterone and testicular steroidogenesis-related gene expression were affected. These steroid hormone effects were confirmed in vitro using the H295R steroidogenesis assay. With this global approach we show that DNB not only induces apoptosis and interferes with cell cycle in the testes but that DNB can also modulate steroid hormone biosynthesis, suggesting an interference with the endocrine system. However, the contribution of the endocrine changes to the severe testicular lesions is presently unknown and requires further investigation.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.

Identification of novel mechanisms of silymarin on the carbon tetrachloride-induced liver fibrosis in mice by nuclear factor-κB bioluminescent imaging-guided transcriptomic analysis

In this study, we applied bioluminescent imaging-guided transcriptomic analysis to evaluate and identify the therapeutic potentials and novel mechanisms of silymarin on carbon tetrachloride (CCl(4))-induced liver fibrosis. Transgenic mice, carrying the luciferase genes driven by nuclear factor-κB (NF-κB), were given with CCl(4) and/or silymarin. In vivo NF-κB activity was evaluated by bioluminescent imaging, liver fibrosis was judged by Sirius red staining and immunohistochemistry, and gene expression profiles of silymarin-treated livers were analyzed by DNA microarray. CCl(4) enhanced the NF-κB-dependent hepatic luminescence and induced hepatic fibrosis, while silymarin reduced the CCl(4)-induced hepatic luminescence and improved CCl(4)-induced liver fibrosis. Microarray analysis showed that silymarin altered the transforming growth factor-β-mediated pathways, which play pivotal roles in the progression of liver fibrosis. Moreover, we newly identified that silymarin downregulated the expression levels of cytoskeleton organization genes and mitochondrion electron-transfer chain genes, such as cytochrome c oxidase Cox6a2, Cox7a1, and Cox8b genes. In conclusion, the correlation of NF-κB-dependent luminescence and liver fibrosis suggested the feasibility of NF-κB bioluminescent imaging for the evaluation of liver fibrosis progression and therapeutic potentials. Moreover, our findings suggested that silymarin might exhibit anti-fibrotic effects in vivo via altering the expression of genes involved in cytoskeleton organization and mitochondrion electron-transfer chain.

Transcriptional response of zebrafish embryos exposed to neurotoxic compounds reveals a muscle activity dependent hspb11 expression

Acetylcholinesterase (AChE) inhibitors are widely used as pesticides and drugs. Their primary effect is the overstimulation of cholinergic receptors which results in an improper muscular function. During vertebrate embryonic development nerve activity and intracellular downstream events are critical for the regulation of muscle fiber formation. Whether AChE inhibitors and related neurotoxic compounds also provoke specific changes in gene transcription patterns during vertebrate development that allow them to establish a mechanistic link useful for identification of developmental toxicity pathways has, however, yet not been investigated. Therefore we examined the transcriptomic response of a known AChE inhibitor, the organophosphate azinphos-methyl (APM), in zebrafish embryos and compared the response with two non-AChE inhibiting unspecific control compounds, 1,4-dimethoxybenzene (DMB) and 2,4-dinitrophenol (DNP). A highly specific cluster of APM induced gene transcripts was identified and a subset of strongly regulated genes was analyzed in more detail. The small heat shock protein hspb11 was found to be the most sensitive induced gene in response to AChE inhibitors. Comparison of expression in wildtype, ache and sop(fixe) mutant embryos revealed that hspb11 expression was dependent on the nicotinic acetylcholine receptor (nAChR) activity. Furthermore, modulators of intracellular calcium levels within the whole embryo led to a transcriptional up-regulation of hspb11 which suggests that elevated intracellular calcium levels may regulate the expression of this gene. During early zebrafish development, hspb11 was specifically expressed in muscle pioneer cells and Hspb11 morpholino-knockdown resulted in effects on slow muscle myosin organization. Our findings imply that a comparative toxicogenomic approach and functional analysis can lead to the identification of molecular mechanisms and specific marker genes for potential neurotoxic compounds.

A molecular and phenotypic integrative approach to identify a no-effect dose level for antiandrogen-induced testicular toxicity

The safety assessment of chemicals for humans relies on identifying no-observed adverse effect levels (NOAELs) in animal toxicity studies using standard methods. With the advent of high information content technologies, especially microarrays, it is pertinent to determine the impact of molecular data on the NOAELs. Consequently, we conducted an integrative study to identify a no-transcriptomic effect dose using microarray analyses coupled with quantitative reverse transcriptase PCR (RT-qPCR) and determined how this correlated with the NOAEL. We assessed the testicular effects of the antiandrogen, flutamide (FM), in a rat 28-day toxicity study using doses of 0.2-30 mg/kg/day. Plasma testosterone levels and testicular histopathology indicated a NOAEL of 1 mg/kg/day. A no-effect dose of 0.2 mg/kg/day was established based on molecular data relevant to the phenotypic changes. We observed differential gene expression starting from 1 mg/kg/day and a deregulation of more than 1500 genes at 30 mg/kg/day. Dose-related changes were identified for the major pathways (e.g., fatty acid metabolism) associated with the testicular lesion (Leydig cell hyperplasia) that were confirmed by RT-qPCR. These data, along with protein accumulation profiles and FM metabolite concentrations in testis, supported the no-effect dose of 0.2 mg/kg/day. Furthermore, the microarray data indicated a dose-dependent change in the fatty acid catabolism pathway, a biological process described for the first time to be affected by FM in testicular tissue. In conclusion, the present data indicate the existence of a transcriptomic threshold, which must be exceeded to progress from a normal state to an adaptative state and subsequently to adverse toxicity.

Clinical relevance of liquid chromatography tandem mass spectrometry as an analytical method in microdose clinical studies

PURPOSE

To investigate the potency of LC-MS/MS by means of sensitivity and the applicability for cassette dosing in microdose clinical trials.

METHODS

Thirty one top-selling 31 drugs were spiked to human plasma, extracted, and analyzed by LC-MS/MS.

RESULTS

The lower limits of quantification for each drug varied from 0.08 to 50 pg/mL, and were lower than one eighth of the assumed maximum plasma concentration at microdose in all drugs except for losartan, indicating the high performance in acquisition of full pharmacokinetic profiles at microdose. We also succeeded in simultaneous analysis of multiple compounds, assuming a situation of cassette dosing in which multiple drug candidates would be administrated simultaneously.

CONCLUSIONS

Together with the features of LC-MS/MS, such as immediate verification, the utilization of non-radiolabeled drugs and no special facilities, we suppose that LC-MS/MS analysis would be widely applicable in conducting microdose clinical studies.

Toxins and stress in fish: proteomic analyses and response network

Fish models are increasingly used in toxicological studies in the laboratory as well as in the field. In addition to contributing to the analysis of toxicity mechanisms, one major aim is to select biomarkers from among the metabolic responses to toxic agents observed that could be useful for surveying the aquatic environment. Since proteomics is a developing field in toxicological research, it seems opportune to explore the data obtained using this approach. This article proposes an overview of proteomic studies of fish exposed to environmental stressors comprising a cyanotoxin and the response networks observed. We tend to take a broad view of how proteins communicate and function within the cell, often encompassing large numbers of proteins that operate in pathways. We start by presenting and discussing the data from four experiments in which the medaka fish was treated under the same conditions with the cyanotoxin, microcystin-LR (MC-LR). Liver proteins were analyzed using two techniques: 2D electrophoresis and LCMSMS. In the second and main part of our paper, the proteomic data obtained from fish contaminated with chemicals, including those reported above concerning the medaka fish intoxicated with MC-LR, are considered in the round in order to identify fish responses to chemical stress. A tentative general overview of how groups of proteins work together depending on exposure and/or subcellular location is proposed, with the inclusion of MC-LR data obtained in mice for comparison.

Developing predictive CSF biomarkers-a challenge critical to success in Alzheimer's disease and neuropsychiatric translational medicine

The need to develop effective treatments for Alzheimer's disease has been confounded by repeated clinical failures where promising new chemical entities that have been extensively characterized in preclinical models of Alzheimer's disease have failed to show efficacy in the human disease state. This has been attributed to: the selection of drug targets that have yet to be shown as causal to the disease as distinct from being the result of the disease process, a lack of congruence in the animal models of Alzheimer's disease, wild-type and transgenic, to the human disease, and the enrollment of patients in proof of concept clinical trials who are at too advanced a stage of the disease to respond to any therapeutic. The development of validated biomarkers that can be used for disease diagnosis and progression is anticipated to improve patient enrollment in clinical trials, to develop new animal models and to identify new disease targets for drug discovery. The present review assesses the status of current efforts in developing CSF biomarkers for Alzheimer's disease and briefly discusses the status of CSF biomarker efforts in schizophrenia, depression, Parkinson's disease and multiple sclerosis.

Use of SELDI MS to discover and identify potential biomarkers of toxicity in InnoMed PredTox: a multi-site, multi-compound study

A serious bottleneck in the drug development pipeline is the inability of current pre-clinical toxicology evaluation methods to predict early on, and with good accuracy, that a drug candidate will have to be removed from development due to toxicology/safety issues. The InnoMed PredTox consortium attempted to address this issue by assessing the value of using molecular profiling techniques (proteomics, transcriptomics, and metabonomics), in combination with conventional toxicology measurements, on decision making earlier in pre-clinical safety evaluation. In this study, we report on the SELDI-TOF-MS proteomics component of the InnoMed PredTox project. In this large scale, multi-site, multi-compound study, tissue and plasma samples from 14-day in vivo rat experiments conducted for 16 hepato- and nephro-toxicants with known toxicology endpoints (including 14 proprietary compounds and 2 reference compounds) were analyzed by SELDI-TOF-MS. We have identified seven plasma proteins and four liver proteins which were shown to be modulated by treatment, and correlated with histopathological evaluations and can be considered potential biomarker candidates for the given toxicology endpoints. In addition, we report on the intra- and inter-site variations observed based on measurements from a reference sample, and steps that can be taken to minimize this variation.

Mercury-induced hepatotoxicity in zebrafish: in vivo mechanistic insights from transcriptome analysis, phenotype anchoring and targeted gene expression validation

Background

Mercury is a prominent environmental contaminant that causes detrimental effects to human health. Although the liver has been known to be a main target organ, there is limited information on in vivo molecular mechanism of mercury-induced toxicity in the liver. By using transcriptome analysis, phenotypic anchoring and validation of targeted gene expression in zebrafish, mercury-induced hepatotoxicity was investigated and a number of perturbed cellular processes were identified and compared with those captured in the in vitro human cell line studies.

Results

Hepato-transcriptome analysis of mercury-exposed zebrafish revealed that the earliest deregulated genes were associated with electron transport chain, mitochondrial fatty acid beta-oxidation, nuclear receptor signaling and apoptotic pathway, followed by complement system and proteasome pathway, and thereafter DNA damage, hypoxia, Wnt signaling, fatty acid synthesis, gluconeogenesis, cell cycle and motility. Comparative meta-analysis of microarray data between zebrafish liver and human HepG2 cells exposed to mercury identified some common toxicological effects of mercury-induced hepatotoxicity in both models. Histological analyses of liver from mercury-exposed fish revealed morphological changes of liver parenchyma, decreased nucleated cell count, increased lipid vesicles, glycogen and apoptotic bodies, thus providing phenotypic evidence for anchoring of the transcriptome analysis. Validation of targeted gene expression confirmed deregulated gene-pathways from enrichment analysis. Some of these genes responding to low concentrations of mercury may serve as toxicogenomic-based markers for detection and health risk assessment of environmental mercury contaminations.

Conclusion

Mercury-induced hepatotoxicity was triggered by oxidative stresses, intrinsic apoptotic pathway, deregulation of nuclear receptor and kinase activities including Gsk3 that deregulates Wnt signaling pathway, gluconeogenesis, and adipogenesis, leading to mitochondrial dysfunction, endocrine disruption and metabolic disorders. This study provides important mechanistic insights into mercury-induced liver toxicity in a whole-animal physiology context, which will help in understanding the syndromes caused by mercury poisoning. The molecular conservation of mercury-induced hepatotoxicity between zebrafish and human cell line reveals the feasibility of using zebrafish to model molecular toxicity in human for toxicant risk assessments.

New perspectives for synergy research with the "omic"-technologies

Synergistic effects, understood as true overadditive effects, are often observed in experimental and clinical studies using phytopharmaceuticals. The introduction of the "omic"-technologies is now opening new perspectives in rationalizing these effects and making use of them in the development of a new generation of phytopharmaceuticals. This review describes possible mechanism of synergistic actions of herbal drugs by mono- and multitargeting and by the activation of signal cascades. It examines the possibilities of the standardization of single and multi component plant extracts and the prediction and assessment of the toxicity and safety of plant extracts with the support of the "omic"-technologies. It further discusses the use of phytopharmaceuticals in the context of an "individualized medicine". It makes proposals how to use the "omic"-technologies to rationalize and develop combination therapies of phytopharmaceuticals and synthetic drugs to minimize adverse reactions (ARs) or improve the therapeutic efficacy. Examples of clinical studies are given which explore already the potential of such co-medications. Modern medical therapy has acknowledged for quite some time the usefulness of combination therapies in the treatment of multifactorial diseases like cancer, cardiovascular or rheumatic diseases. The term "synergy" is rarely used in this context, the combinatory mechanisms of actions seldom completely understood and the potentially occurring adverse reactions feared. A systematic exploitation of synergy effects of phytomedical interventions alone or in combination with synthetic drugs should lead in a long term perspective to the discovery and development of more rational evidence-based interventions in the prevention and therapy of multifactorial diseases and should thereby enrich modern pharmacotherapy.

ebTrack: an environmental bioinformatics system built upon ArrayTrack

ebTrack is being developed as an integrated bioinformatics system for environmental research and analysis by addressing the issues of integration, curation, management, first level analysis and interpretation of environmental and toxicological data from diverse sources. It is based on enhancements to the US FDA developed ArrayTrack™ system through additional analysis modules for gene expression data as well as through incorporation and linkages to modules for analysis of proteomic and metabonomic datasets that include tandem mass spectra. ebTrack uses a client-server architecture with the free and open source PostgreSQL as its database engine, and java tools for user interface, analysis, visualization, and web-based deployment. Several predictive tools that are critical for environmental health research are currently supported in ebTrack, including Significance Analysis of Microarray (SAM). Furthermore, new tools are under continuous integration, and interfaces to environmental health risk analysis tools are being developed in order to make ebTrack widely usable. These health risk analysis tools include the Modeling ENvironment for TOtal Risk studies (MENTOR) for source-to-dose exposure modeling and the DOse Response Information ANalysis system (DORIAN) for health outcome modeling. The design of ebTrack is presented in detail and steps involved in its application are summarized through an illustrative application.