The Japanese toxicogenomics project: application of toxicogenomics

Comparative gene and protein expression analyses of a panel of cytokines in acute and chronic drug-induced liver injury in rats

Drug-induced liver injury (DILI) is a significant safety issue associated with medication use, and is the major cause of failures in drug development and withdrawal in post marketing. Cytokines are signaling molecules produced and secreted by immune cells and play crucial roles in the progression of DILI. Although there are numerous reports of cytokine changes in several DILI models, a comprehensive analysis of cytokine expression changes in rat liver injury induced by various compounds has, to the best of our knowledge, not been performed. In the past several years, we have built a public, free, large-scale toxicogenomics database, called Open TG-GATEs, containing microarray data and toxicity data of the liver of rats treated with various hepatotoxic compounds. In this study, we measured the protein expression levels of a panel of 24 cytokines in frozen liver of rats treated with a total of 20 compounds, obtained in the original study that formed the basis of the Open TG-GATEs database and analyzed protein expression profiles combined with mRNA expression profiles to investigate the correlation between mRNA and protein expression levels. As a result, we demonstrated significant correlations between mRNA and protein expression changes for interleukin (IL)-1β, IL-1α, monocyte chemo-attractant protein (MCP)-1/CC-chemokine ligand (Ccl)2, vascular endothelial growth factor A (VEGF-A), and regulated upon activation normal T cell expressed and secreted (RANTES)/Ccl5 in several different types of DILI. We also demonstrated that IL-1β protein and MCP-1/Ccl2 mRNA were commonly up-regulated in the liver of rats treated with different classes of hepatotoxicants and exhibited the highest accuracy in the detection of hepatotoxicity. The results also demonstrate that hepatic mRNA changes do not always correlate with protein changes of cytokines in the liver. This is the first study to provide a comprehensive analysis of mRNA-protein correlations of factors involved in various types of DILI, as well as additional insights into the importance of understanding complex cytokine expression changes in assessing DILI.

Cancer biology, toxicology and alternative methods development go hand-in-hand

Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals- and materials-testing data to information relevant to human health and environmental safety.

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.

Evaluation of toxicogenomics approaches for assessing the risk of nongenotoxic carcinogenicity in rat liver

The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3, Cdkn1a, Akr7a3, Ccng1 and Abcb4.

Mechanisms of amiodarone and valproic acid induced liver steatosis in mouse in vivo act as a template for other hepatotoxicity models

Liver injury is the leading cause of drug-induced toxicity. For the evaluation of a chemical compound to induce toxicity, in this case steatosis or fatty liver, it is imperative to identify markers reflective of mechanisms and processes induced upon exposure, as these will be the earliest changes reflective of disease. Therefore, an in vivo mouse toxicogenomics study was completed to identify common pathways, nuclear receptor (NR) binding sites, and genes regulated by three known human steatosis-inducing compounds, amiodarone (AMD), valproic acid (VPA), and tetracycline (TET). Over 1, 4, and 11 days of treatment, AMD induced changes in clinical chemistry parameters and histopathology consistent with steatosis. Common processes and NR binding sites involved in lipid, retinol, and drug metabolism were found for AMD and VPA, but not for TET, which showed no response. Interestingly, the pattern of enrichment of these common pathways and NR binding sites over time was unique to each compound. Eleven biomarkers of steatosis were identified as dose responsive and time sensitive to toxicity for AMD and VPA. Finally, this in vivo mouse study was compared to an AMD rat in vivo, an AMD mouse primary hepatocyte, and a VPA human primary hepatocyte study to identify concordance for steatosis. We conclude that concordance is found on the process level independent of species, model or dose*time point.

Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment

Over the past decade, major leaps forward have been made on the mechanistic understanding and identification of adaptive stress response landscapes underlying toxic insult using transcriptomics approaches. However, for predictive purposes of adverse outcome several major limitations in these approaches exist. First, the limited number of samples that can be analyzed reduces the in depth analysis of concentration-time course relationships for toxic stress responses. Second these transcriptomics analysis have been based on the whole cell population, thereby inevitably preventing single cell analysis. Third, transcriptomics is based on the transcript level, totally ignoring (post)translational regulation. We believe these limitations are circumvented with the application of high content analysis of relevant toxicant-induced adaptive stress signaling pathways using bacterial artificial chromosome (BAC) green fluorescent protein (GFP) reporter cell-based assays. The goal is to establish a platform that incorporates all adaptive stress pathways that are relevant for toxicity, with a focus on drug-induced liver injury. In addition, cellular stress responses typically follow cell perturbations at the subcellular organelle level. Therefore, we complement our reporter line panel with reporters for specific organelle morphometry and function. Here, we review the approaches of high content imaging of cellular adaptive stress responses to chemicals and the application in the mechanistic understanding and prediction of chemical toxicity at a systems toxicology level.

Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity

Current testing models for predicting drug-induced liver injury are inadequate, as they basically under-report human health risks. We present here an approach towards developing pathways based on hepatotoxicity-associated gene groups derived from two types of publicly accessible hepatotoxicity databases, in order to develop drug-induced liver injury biomarker profiles. One human liver ‘omics-based and four text-mining-based databases were explored for hepatotoxicity-associated gene lists. Over-representation analysis of these gene lists with a hepatotoxicant-exposed primary human hepatocytes data set showed that human liver ‘omics gene lists performed better than text-mining gene lists and the results of the latter differed strongly between databases. However, both types of databases contained gene lists demonstrating biomarker potential. Visualizing those in pathway format may aid in interpreting the biomolecular background. We conclude that exploiting existing and openly accessible databases in a dedicated manner seems promising in providing venues for translational research in toxicology and biomarker development.

Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

The ChEMBL bioactivity database: an update

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.

A systems approach for analysis of high content screening assay data with topic modeling

Background

High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements.

Method

In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment.

Results

We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration.

Conclusions

The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.

Serum acute phase reactants hallmark healthy individuals at risk for acetaminophen-induced liver injury

Background

Acetaminophen (APAP) is a commonly used analgesic. However, its use is associated with drug-induced liver injury (DILI). It is a prominent cause of acute liver failure, with APAP hepatotoxicity far exceeding other causes of acute liver failure in the United States. In order to improve its safe use this study aimed to identify individuals at risk for DILI prior to drug treatment by searching for non-genetic serum markers in healthy subjects susceptible to APAP-induced liver injury (AILI).

Methods

Healthy volunteers (n = 36) received either placebo or acetaminophen at the maximum daily dose of 4 g for 7 days. Blood samples were taken prior to and after APAP treatment. Serum proteomic profiling was done by 2D SDS-PAGE and matrix-assisted laser desorption/ionization-time of flight-mass spectrometry. Additionally, the proteins C-reactive protein, haptoglobin and hemopexin were studied by quantitative immunoassays.

Results

One-third of study subjects presented more than four-fold increased alanine transaminase activity to evidence liver injury, while serum proteomics informed on 20 proteins as significantly regulated. These function primarily in acute phase and immune response. Pre-treatment associations included C-reactive protein, haptoglobin isoforms and retinol binding protein being up to six-fold higher in AILI susceptible individuals, whereas alpha1-antitrypsin, serum amyloid A, kininogen and transtyretin were regulated by nearly five-fold in AILI responders. When compared with published findings for steatohepatitis and cases of hepatocellular, cholestatic and mixed DILI, 10 proteins were identified as uniquely associated with risk for AILI, including plasminogen. Notably, this zymogen facilitates macrophage chemotactic migration and inflammatory response as reported for plasminogen-deficient mice shown to be resistant to APAP hepatotoxicity. Finally, analysis of a publicly available database of gene expression profiles of cultures of human hepatocytes treated with drugs labeled as no- (n = 8), low- (n = 45) or most-DILI-concern (n = 39) confirmed regulation of the identified biomarkers to demonstrate utility in predicting risk for liver injury.

Conclusions

The significant regulation of acute phase reactants points to an important link between AILI and the immune system. Monitoring of serum acute phase reactants prior to drug treatment may contribute to prevention and management of AILI, and may also be of utility for other drugs with known liver liabilities.

Human skin-derived stem cells as a novel cell source for in vitro hepatotoxicity screening of pharmaceuticals

Human skin-derived precursors (hSKP) are postnatal stem cells with neural crest properties that reside in the dermis of human skin. These cells can be easily isolated from small (fore) skin segments and have the capacity to differentiate into multiple cell types. In this study, we show that upon exposure to hepatogenic growth factors and cytokines, hSKP acquire sufficient hepatic features that could make these cells suitable in vitro tools for hepatotoxicity screening of new chemical entities and already existing pharmaceutical compounds. Indeed, hepatic differentiated hSKP [hSKP-derived hepatic progenitor cells (hSKP-HPC)] express hepatic progenitor cell markers (EPCAM, NCAM2, PROM1) and adult hepatocyte markers (ALB), as well as key biotransformation enzymes (CYP1B1, FMO1, GSTA4, GSTM3) and influx and efflux drug transporters (ABCC4, ABCA1, SLC2A5). Using a toxicogenomics approach, we could demonstrate that hSKP-HPC respond to acetaminophen exposure in a comparable way to primary human hepatocytes in culture. The toxicological responses "liver damage", "liver proliferation", "liver necrosis" and "liver steatosis" were found to be significantly enriched in both in vitro models. Also genes associated with either cytotoxic responses or induction of apoptosis (BCL2L11, FOS, HMOX1, TIMP3, and AHR) were commonly upregulated and might represent future molecular biomarkers for hepatotoxicity. In conclusion, our data gives a first indication that hSKP-HPC might represent a suitable preclinical model for in vitro screening of hepatotoxicity. To the best of our knowledge, this is the first report in which human postnatal stem cells derived from skin are described as a potentially relevant cell source for in vitro hepatotoxicity testing of pharmaceutical compounds.

DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions

Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci. The DEXUS R package is publicly available from Bioconductor and the scripts for all experiments are available at http://www.bioinf.jku.at/software/dexus/.

LTMap: a web server for assessing the potential liver toxicity by genome-wide transcriptional expression data

Toxicogenomics (TGx) has played a significant role in mechanistic research related with hepatotoxicity as well as liver toxicity prediction. Currently, several large-scale preclinical TGx data sets were made freely accessible to the public, such as Open TG-GATEs. With the availability of a sufficient amount of microarray data, it is important to integrate this information to provide new insights into the risk assessment of potential drug-induced liver toxicity. Here we developed a web server for evaluating the potential liver toxicity based on genome-wide transcriptomics data, namely LTMap. In LTMap, researchers could compare signatures of query compounds against a pregenerated signature database of 20 123 Affymetrix arrays associated with about 170 compounds retrieved from the largest public toxicogenomics data set Open TG-GATEs. Results from this comparison may lead to the unexpected discovery of similar toxicological responses between chemicals. We validated our computational approach for similarity comparison using three example drugs. Our successful applications of LTMap in these case studies demonstrated its utility in revealing the connection of chemicals according to similar toxicological behaviors. Furthermore, a user-friendly web interface is provided by LTMap to browse and search toxicogenomics data (http://tcm.zju.edu.cn/ltmap).

A toxicogenomic approach for the prediction of murine hepatocarcinogenesis using ensemble feature selection

The current strategy for identifying the carcinogenicity of drugs involves the 2-year bioassay in male and female rats and mice. As this assay is cost-intensive and time-consuming there is a high interest in developing approaches for the screening and prioritization of drug candidates in preclinical safety evaluations. Predictive models based on toxicogenomics investigations after short-term exposure have shown their potential for assessing the carcinogenic risk. In this study, we investigated a novel method for the evaluation of toxicogenomics data based on ensemble feature selection in conjunction with bootstrapping for the purpose to derive reproducible and characteristic multi-gene signatures. This method was evaluated on a microarray dataset containing global gene expression data from liver samples of both male and female mice. The dataset was generated by the IMI MARCAR consortium and included gene expression profiles of genotoxic and nongenotoxic hepatocarcinogens obtained after treatment of CD-1 mice for 3 or 14 days. We developed predictive models based on gene expression data of both sexes and the models were employed for predicting the carcinogenic class of diverse compounds. Comparing the predictivity of our multi-gene signatures against signatures from literature, we demonstrated that by incorporating our gene sets as features slightly higher accuracy is on average achieved by a representative set of state-of-the art supervised learning methods. The constructed models were also used for the classification of Cyproterone acetate (CPA), Wy-14643 (WY) and Thioacetamid (TAA), whose primary mechanism of carcinogenicity is controversially discussed. Based on the extracted mouse liver gene expression patterns, CPA would be predicted as a nongenotoxic compound. In contrast, both WY and TAA would be classified as genotoxic mouse hepatocarcinogens.

Enhanced QSAR model performance by integrating structural and gene expression information

Despite decades of intensive research and a number of demonstrable successes, quantitative structure-activity relationship (QSAR) models still fail to yield predictions with reasonable accuracy in some circumstances, especially when the QSAR paradox occurs. In this study, to avoid the QSAR paradox, we proposed a novel integrated approach to improve the model performance through using both structural and biological information from compounds. As a proof-of-concept, the integrated models were built on a toxicological dataset to predict non-genotoxic carcinogenicity of compounds, using not only the conventional molecular descriptors but also expression profiles of significant genes selected from microarray data. For test set data, our results demonstrated that the prediction accuracy of QSAR model was dramatically increased from 0.57 to 0.67 with incorporation of expression data of just one selected signature gene. Our successful integration of biological information into classic QSAR model provided a new insight and methodology for building predictive models especially when QSAR paradox occurred.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

Integrative chemical-biological read-across approach for chemical hazard classification

Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here, we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays ("biological" similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from both chemical and biological analogues whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models.

Analysis of the transcriptome in molecular epidemiology studies

The human transcriptome is complex, comprising multiple transcript types, mostly in the form of non-coding RNA (ncRNA). The majority of ncRNA is of the long form (lncRNA, ≥ 200 bp), which plays an important role in gene regulation through multiple mechanisms including epigenetics, chromatin modification, control of transcription factor binding, and regulation of alternative splicing. Both mRNA and ncRNA exhibit additional variability in the form of alternative splicing and RNA editing. All aspects of the human transcriptome can potentially be dysregulated by environmental exposures. Next-generation RNA sequencing (RNA-Seq) is the best available methodology to measure this although it has limitations, including experimental bias. The third phase of the MicroArray Quality Control Consortium project (MAQC-III), also called Sequencing Quality Control (SeQC), aims to address these limitations through standardization of experimental and bioinformatic methodologies. A limited number of toxicogenomic studies have been conducted to date using RNA-Seq. This review describes the complexity of the human transcriptome, the application of transcriptomics by RNA-Seq or microarray in molecular epidemiology studies, and limitations of these approaches including the type of cell or tissue analyzed, experimental variation, and confounding. By using good study designs with precise, individual exposure measurements, sufficient power and incorporation of phenotypic anchors, studies in human populations can identify biomarkers of exposure and/or early effect and elucidate mechanisms of action underlying associated diseases, even at low doses. Analysis of datasets at the pathway level can compensate for some of the limitations of RNA-Seq and, as more datasets become available, will increasingly elucidate the exposure-disease continuum.

Valproic acid-induced gene expression responses in rat whole embryo culture and comparison across in vitro developmental and non-developmental models

Transcriptomic evaluations may improve toxicity prediction of in vitro-based developmental models. In this study, transcriptomics was used to identify VPA-induced gene expression changes in rat whole embryo culture (WEC). Furthermore, VPA-induced responses were compared across in vitro-based developmental models, such as the cardiac and neural embryonic stem cells (ESTc and ESTn, respectively) and the zebrafish embryotoxicity model. VPA-induced gene regulation in WEC corresponded with observed morphological effects and previously suggested mechanisms of toxicity. Gene Ontology term-directed analysis showed conservation of VPA-induced gene expression changes across in vitro-based developmental models, with ESTc and ESTn exhibiting complementary responses. Furthermore, comparison of in vitro-based developmental and non-developmental models revealed that more generalized VPA-induced effects can be detected using non-developmental models whereas developmental models provide added value when assessing developmental-specific effects. These analyses can be used to optimize test batteries for the detection of developmental toxicants in vitro.

Strategic applications of gene expression: from drug discovery/development to bedside

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.

The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing

The aim of the SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1) research cluster, comprised of seven EU FP7 Health projects co-financed by Cosmetics Europe, is to generate a proof-of-concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT-1 strategy is to adopt a mode-of-action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross-cluster infrastructure project whose activities include the development of a data warehouse to provide a web-accessible shared repository of research data and protocols, a physical compounds repository, reference or "gold compounds" for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA-Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds.

Toxicologic Pathology in the 21st Century

Toxicology is and will be heavily influenced by advances in many scientific disciplines. For toxicologic pathology, particularly relevant are the increasing array of molecular methods providing deeper insights into toxicity pathways, in vivo imaging techniques visualizing toxicodynamics and more powerful computers anticipated to allow (partly) automated morphological diagnoses. It appears unlikely that, in a foreseeable future, animal studies can be replaced by in silico and in vitro studies or longer term in vivo studies by investigations of biomarkers including toxicogenomics of shorter term studies, though the importance of such approaches will continue to increase. In addition to changes based on scientific progress, the work of toxicopathologists is and will be affected by social and financial factors, among them stagnating budgets, globalization, and outsourcing. The number of toxicopathologists in North America, Europe, and the Far East is not expected to grow. Many toxicopathologists will likely spend less time at the microscope but will be more heavily involved in early research activities, imaging, and as generalists with a broad biological understanding in evaluation and management of toxicity. Toxicologic pathology will remain important and is indispensable for validation of new methods, quality assurance of established methods, and for areas without good alternative methods.

Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR-like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage.

In silico models for drug-induced liver injury--current status

INTRODUCTION

Drug-induced liver injury (DILI) is one of the most important reasons for drug attrition at both pre-approval and post-approval stages. Therefore, it is crucial to develop methods that will detect potential hepatotoxicity among drug candidates as early and quickly as possible. However, the complexity of hepatotoxicity endpoint makes it very difficult to predict. In addition, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data, which are the key to any modelling approach.

AREAS COVERED

This review explores the current status of existing in silico models predicting hepatotoxicity. Over the past decade, attempts have been made to compile hepatotoxicity data and develop in silico models, which can be used as a first-line screening of drug candidates for further testing.

EXPERT OPINION

Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.

Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs

A critical challenge for environmental chemical risk assessment is the characterization and reduction of uncertainties introduced when extrapolating inferences from one species to another. The purpose of this article is to explore the challenges, opportunities, and research needs surrounding the issue of how genomics data and computational and systems level approaches can be applied to inform differences in response to environmental chemical exposure across species. We propose that the data, tools, and evolutionary framework of comparative genomics be adapted to inform interspecies differences in chemical mechanisms of action. We compare and contrast existing approaches, from disciplines as varied as evolutionary biology, systems biology, mathematics, and computer science, that can be used, modified, and combined in new ways to discover and characterize interspecies differences in chemical mechanism of action which, in turn, can be explored for application to risk assessment. We consider how genetic, protein, pathway, and network information can be interrogated from an evolutionary biology perspective to effectively characterize variations in biological processes of toxicological relevance among organisms. We conclude that comparative genomics approaches show promise for characterizing interspecies differences in mechanisms of action, and further, for improving our understanding of the uncertainties inherent in extrapolating inferences across species in both ecological and human health risk assessment. To achieve long-term relevance and consistent use in environmental chemical risk assessment, improved bioinformatics tools, computational methods robust to data gaps, and quantitative approaches for conducting extrapolations across species are critically needed. Specific areas ripe for research to address these needs are recommended.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches

Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Functional toxicogenomics: mechanism-centered toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21^st century.

Cross-study and cross-omics comparisons of three nephrotoxic compounds reveal mechanistic insights and new candidate biomarkers

The European InnoMed-PredTox project was a collaborative effort between 15 pharmaceutical companies, 2 small and mid-sized enterprises, and 3 universities with the goal of delivering deeper insights into the molecular mechanisms of kidney and liver toxicity and to identify mechanism-linked diagnostic or prognostic safety biomarker candidates by combining conventional toxicological parameters with "omics" data. Mechanistic toxicity studies with 16 different compounds, 2 dose levels, and 3 time points were performed in male Crl: WI(Han) rats. Three of the 16 investigated compounds, BI-3 (FP007SE), Gentamicin (FP009SF), and IMM125 (FP013NO), induced kidney proximal tubule damage (PTD). In addition to histopathology and clinical chemistry, transcriptomics microarray and proteomics 2D-DIGE analysis were performed. Data from the three PTD studies were combined for a cross-study and cross-omics meta-analysis of the target organ. The mechanistic interpretation of kidney PTD-associated deregulated transcripts revealed, in addition to previously described kidney damage transcript biomarkers such as KIM-1, CLU and TIMP-1, a number of additional deregulated pathways congruent with histopathology observations on a single animal basis, including a specific effect on the complement system. The identification of new, more specific biomarker candidates for PTD was most successful when transcriptomics data were used. Combining transcriptomics data with proteomics data added extra value.

Application of toxicogenomics in hepatic systems toxicology for risk assessment: acetaminophen as a case study

Hepatic systems toxicology is the integrative analysis of toxicogenomic technologies, e.g., transcriptomics, proteomics, and metabolomics, in combination with traditional toxicology measures to improve the understanding of mechanisms of hepatotoxic action. Hepatic toxicology studies that have employed toxicogenomic technologies to date have already provided a proof of principle for the value of hepatic systems toxicology in hazard identification. In the present review, acetaminophen is used as a model compound to discuss the application of toxicogenomics in hepatic systems toxicology for its potential role in the risk assessment process, to progress from hazard identification towards hazard characterization. The toxicogenomics-based parallelogram is used to identify current achievements and limitations of acetaminophen toxicogenomic in vivo and in vitro studies for in vitro-to-in vivo and interspecies comparisons, with the ultimate aim to extrapolate animal studies to humans in vivo. This article provides a model for comparison of more species and more in vitro models enhancing the robustness of common toxicogenomic responses and their relevance to human risk assessment. To progress to quantitative dose-response analysis needed for hazard characterization, in hepatic systems toxicology studies, generation of toxicogenomic data of multiple doses/concentrations and time points is required. Newly developed bioinformatics tools for quantitative analysis of toxicogenomic data can aid in the elucidation of dose-responsive effects. The challenge herein is to assess which toxicogenomic responses are relevant for induction of the apical effect and whether perturbations are sufficient for the induction of downstream events, eventually causing toxicity.