A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats

Mouse Models of Hepatocellular Carcinoma: Classification, Advancement, and Application

Hepatocellular carcinoma (HCC) is the subtype of liver cancer with the highest incidence, which is a heterogeneous malignancy with increasing incidence rate and high mortality. For ethical reasons, it is essential to validate medical clinical trials for HCC in animal models before further consideration on humans. Therefore, appropriate models for the study of the pathogenesis of the disease and related treatment methods are necessary. For tumor research, mouse models are the most commonly used and effective in vivo model, which is closer to the real-life environment, and the repeated experiments performed on it are closer to the real situation. Several mouse models of HCC have been developed with different mouse strains, cell lines, tumor sites, and tumor formation methods. In this review, we mainly introduce some mouse HCC models, including induced model, gene-edited model, HCC transplantation model, and other mouse HCC models, and discuss how to choose the appropriate model according to the purpose of the experiments.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

In Vivo and In Vitro Models of Hepatocellular Carcinoma: Current Strategies for Translational Modeling

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related death globally. HCC is a complex multistep disease and usually emerges in the setting of chronic liver diseases. The molecular pathogenesis of HCC varies according to the etiology, mainly caused by chronic hepatitis B and C virus infections, chronic alcohol consumption, aflatoxin-contaminated food, and non-alcoholic fatty liver disease associated with metabolic syndrome or diabetes mellitus. The establishment of HCC models has become essential for both basic and translational research to improve our understanding of the pathophysiology and unravel new molecular drivers of this disease. The ideal model should recapitulate key events observed during hepatocarcinogenesis and HCC progression in view of establishing effective diagnostic and therapeutic strategies to be translated into clinical practice. Despite considerable efforts currently devoted to liver cancer research, only a few anti-HCC drugs are available, and patient prognosis and survival are still poor. The present paper provides a state-of-the-art overview of in vivo and in vitro models used for translational modeling of HCC with a specific focus on their key molecular hallmarks.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

Metabolite Patterns in Human Myeloid Hematopoiesis Result from Lineage-Dependent Active Metabolic Pathways

Assessment of hematotoxicity from environmental or xenobiotic compounds is of notable interest and is frequently assessed via the colony forming unit (CFU) assay. Identification of the mode of action of single compounds is of further interest, as this often enables transfer of results across different tissues and compounds. Metabolomics displays one promising approach for such identification, nevertheless, suitability with current protocols is restricted. Here, we combined a hematopoietic stem and progenitor cell (HSPC) expansion approach with distinct lineage differentiations, resulting in formation of erythrocytes, dendritic cells and neutrophils. We examined the unique combination of pathway activity in glycolysis, glutaminolysis, polyamine synthesis, fatty acid oxidation and synthesis, as well as glycerophospholipid and sphingolipid metabolism. We further assessed their interconnections and essentialness for each lineage formation. By this, we provide further insights into active metabolic pathways during the differentiation of HSPC into different lineages, enabling profound understanding of possible metabolic changes in each lineage caused by exogenous compounds.

Extraction of peroxisome proliferator-activated receptor α agonist-induced lipid metabolism-related and unrelated genes in rat liver and analysis of their genomic location

Although peroxisome proliferator-activated receptor α (PPARα) agonists are obviously hepatocarcinogenic in rodents, they have been widely used for dyslipidemia and proven to be safe for clinical use without respect to the species difference. It is established that PPARα acts as a part of the transcription factor complex, but its precise mechanism is still unknown. Using the data of Toxicogenomics Database, reliable genes responsive to PPARα agonists, clofibrate, fenofibrate and WY-14,643, in rat liver, were extracted from both in vivo and in vitro data, and sorted by their fold increase. It was found that there were many genes responding to fibrates exclusively in vivo. Most of the in vivo specific genes appear to be unrelated to lipid metabolism and are not upregulated in the kidney. Fifty-seven genes directly related to cell proliferation were extracted from in vivo data, but they were not induced in vitro at all. Analysis of PPAR-responsive elements could not explain the observed difference in induction. To evaluate possible interaction between neighboring genes in gene expression, the correlation of the fold changes of neighboring genes for 22 drugs with various PPARα agonistic potencies were calculated for the genes showing more than 2.5 fold induction by 3 fibrates in vivo, and their genomic location was compared with that of the human orthologue. In the present study, many candidates of genes other than lipid metabolism were selected, and these could be good starting points to elucidate the mechanism of PPARα agonist-induced rodent-specific toxicity.

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

Application of transcriptomic and microRNA profiling in the evaluation of potential liver carcinogens

Hepatocarcinogens are agents that increase the incidence of liver cancer in exposed animals or humans. It is now established that carcinogenic exposures have a widespread impact on the transcriptome, inducing both adaptive and adverse changes in the activities of genes and pathways. Chemical hepatocarcinogens have also been shown to affect expression of microRNA (miRNA), the evolutionarily conserved noncoding RNA that regulates gene expression posttranscriptionally. Considerable effort has been invested into examining the involvement of mRNA in chemical hepatocarcinogenesis and their potential usage for the classification and prediction of new chemical entities. For miRNA, there has been an increasing number of studies reported over the past decade, although not to the same degree as for transcriptomic studies. Current data suggest that it is unlikely that any gene or miRNA signature associated with short-term carcinogen exposure can replace the rodent bioassay. In this review, we discuss the application of transcriptomic and miRNA profiles to increase mechanistic understanding of chemical carcinogens and to aid in their classification.

Cancer Hazard Identification Integrating Human Variability: The Case of Coumarin

Coumarin is a naturally occurring sweet-smelling benzopyrone that may be extracted from plants or synthesized for commercial uses. Its uses include as a flavoring agent, fragrance enhancer, and odor-masking additive. We reviewed and evaluated the scientific evidence on the carcinogenicity of coumarin, integrating information from carcinogenicity studies in animals with mechanistic and other relevant data, including data from toxicogenomic, genotoxicity, and metabolism studies, and studies of human variability of a key enzyme, CYP2A6. Increases in tumors were observed in multiple studies in rats and mice in multiple tissues. Our functional pathway analysis identified several common cancer-related biological processes/pathways affected by coumarin in rat liver following in vivo exposure and in human primary hepatocytes exposed in vitro. When coumarin 7-hydroxylation by CYP2A6 is compromised, this can lead to a shift in metabolism to the 3,4-epoxidation pathway and increased generation of electrophilic metabolites. Mechanistic data align with 3 key characteristics of carcinogens, namely formation of electrophilic metabolites, genotoxicity, and induction of oxidative stress. Considerations of metabolism, human variability in CYP2A6 activity, and coumarin hepatotoxicity in susceptible individuals provide additional support for carcinogenicity concern. Our analysis illustrates the importance of integrating information on human variability in the cancer hazard identification process.

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering

Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word’s HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.

Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model

Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs. Also it is sensitive to outlying observations. Thus, this platform may produce misleading results in some cases. The probabilistic hidden variable model (PHVM) is a more effective co-clustering approach that share two-way information simultaneously, but it is also sensitive to outlying observations. Therefore, in this paper we have proposed logistic probabilistic hidden variable model (LPHVM) for robust co-clustering between genes and DCCs, since gene expression data are often contaminated by outlying observations. We have investigated the performance of the proposed LPHVM co-clustering approach in a comparison with the conventional PHVM and Toxygates co-clustering approaches using simulated and real life TGP gene expression datasets, respectively. Simulation results show that the proposed method improved the performance over the conventional PHVM in presence of outliers; otherwise, it keeps equal performance. In the case of real life TGP data analysis, three DCCs (glibenclamide-low, perhexilline-low, and hexachlorobenzene-medium) for glutathione metabolism pathway dataset as well as two DCCs (acetaminophen-medium and methapyrilene-low) for PPAR signaling pathway dataset were incorrectly co-clustered by the Toxygates online platform, while only one DCC (hexachlorobenzene-low) for glutathione metabolism pathway was incorrectly co-clustered by the proposed LPHVM approach. Our findings from the real data analysis are also supported by the other findings in the literature.

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70-90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens' adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention.

A Pipeline for High-Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics

Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose–response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration–response points of departure. The methods are extensible to other forms of concentration–response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state.

Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology

Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.

Detection of non-genotoxic hepatocarcinogens and prediction of their mechanism of action in rats using gene marker sets

Several studies have successfully detected hepatocarcinogenicity in rats based on gene expression data. However, prediction of hepatocarcinogens with certain mechanisms of action (MOAs), such as enzyme inducers and peroxisome proliferator-activated receptor α (PPARα) agonists, can prove difficult using a single model and requires a highly toxic dose. Here, we constructed a model for detecting non-genotoxic (NGTX) hepatocarcinogens and predicted their MOAs in rats. Gene expression data deposited in the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) was used to investigate gene marker sets. Principal component analysis (PCA) was applied to discriminate different MOAs, and a support vector machine algorithm was applied to construct the prediction model. This approach identified 106 probe sets as gene marker sets for PCA and enabled the prediction model to be constructed. In PCA, NGTX hepatocarcinogens were classified as follows based on their MOAs: cytotoxicants, PPARα agonists, or enzyme inducers. The prediction model detected hepatocarcinogenicity with an accuracy of more than 90% in 14- and 28-day repeated-dose studies. In addition, the doses capable of predicting NGTX hepatocarcinogenicity were close to those required in rat carcinogenicity assays. In conclusion, our PCA and prediction model using gene marker sets will help assess the risk of hepatocarcinogenicity in humans based on MOAs and reduce the number of two-year rodent bioassays.

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity

The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

Multilevel evaluations of potential liver injury of bifenthrin

The widespread use of pesticides, such as pyrethroids, increases health risks to non-target organisms. The potential toxicity of pyrethroids to the liver remains unclear and could be easily overlooked if only the common clinical indicators of liver disease are examined. In the present study, BALB/c mice were given intraperitoneal injections of 0, 2, 4, or 8 mg/kg bifenthrin (BF) for 7 days. The potential liver injury of BF and its underlying mechanism were then investigated through multilevel evaluations. Histological analyses and serum enzyme activities showed no obvious clinical evidence of liver damage. Oxidative stress was induced and caspases were activated in response to increased BF concentrations. Exposure to BF also significantly altered the expression levels of mitochondrial apoptosis-related genes in dose-dependent relationships. The microarray results showed that BF could disturb the metabolic profile and extensively induce genes related to oxidative stress, including the cytochrome P450 family, glutathione peroxidases, glutathione s-transferases and kinases. In the in vivo model, BF induced liver injury through caspase-mediated mitochondrial-dependent cell death, a process that is closely related to oxidative stress, even in the absence of classical clinical biomarkers of liver dysfunction. The results of this study suggest that classical evaluations are not adequate for liver toxicity of pyrethroids, and highlight the need for more comprehensive assessment of health risks of these widely used pesticides.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database

AIM

We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds.

MATERIALS & METHODS

The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays.

RESULTS

The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches.

CONCLUSION

Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

Comprehensive analysis of DNA methylation and gene expression of rat liver in a 2-stage hepatocarcinogenesis model

Recent studies have shown that epigenetic alterations correlate with carcinogenesis in various tissues. Identification of these alterations might help characterize the early stages of carcinogenesis. We comprehensively analyzed DNA methylation and gene expression in livers obtained from rats exposed to nitrosodiethylamine (DEN) followed by a promoter of hepatic carcinogenesis, phenobarbital (PB). The combination of DEN and PB induced marked increases in number and area of glutathione S-transferase-placental form (GST-P)-positive foci in the liver. In the liver of rats that received 30 mg/kg of DEN, pathway analysis revealed alterations of common genes in terms of gene expression and DNA methylation, and that these alterations were related to immune responses. Hierarchical clustering analysis of the expression of common genes from public data obtained through the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system (TG-GATEs) showed that carcinogenic compounds clustered together. MBD-seq and GeneChip analysis indicated that major histocompatibility complex class Ib gene RT1-CE5, which has an important role in antigen presentation, was hypomethylated around the promoter region and specifically induced in the livers of DEN-treated rats. Further, immunohistochemical analysis indicated that the co-localization of GST-P and protein homologous to RT1-CE5 was present at the foci of some regions. These results suggest that common genes were altered in terms of both DNA methylation and expression in livers, with preneoplastic foci indicating carcinogenic potential, and that immune responses are involved in early carcinogenesis. In conclusion, the present study identified a specific profile of DNA methylation and gene expression in livers with preneoplastic foci. Early epigenetic perturbations of immune responses might correlate with the early stages of hepatocarcinogenesis.

MicroRNA responses to environmental liver carcinogens: Biological and clinical significance

A large number of biological, chemical, and dietary factors have been implicated in the development of liver cancer. These involve complex and protracted interactions between genetic, epigenetic, and environmental factors. The survival rate for patients diagnosed with late-stage liver cancer is currently low due to the aggressive nature of the disease and resistance to therapy. An increasing body of evidence has offered support for the crucial role of non-coding microRNA (miRNA) in directing hepatic responses to environmental risk factors for liver cancer. In this review we focus on miRNA responses to environmental liver cancer risk factors and their potential biological and clinical significance.

Hepatoprotective agent tethered isoniazid for the treatment of drug-induced hepatotoxicity: Synthesis, biochemical and histopathological evaluation

The aim of the study was to investigate the protective effect of isoniazid–curcumin conjugate (INH–CRM) in INH-induced hepatic injury by biochemical analysis and histology examination of liver in Wistar rats. The biochemical analysis included determination of the levels of plasma cholesterol, triglycerides (TG), albumin content, and lipid peroxidation (MDA). INH–CRM administration resulted in a significant decrease in plasma cholesterol, TG, and MDA levels in the liver tissue homogenate with an elevation in albumin level indicating its hepatoprotective activity. Histology of the liver further confirmed the reduction in hepatic injury. The hepatoprotective with INH–CRM can be attributed to the antioxidant activity of curcumin. The conjugate probably stabilizes the curcumin molecule, preventing its presystemic metabolism thereby enhancing its bioavailability and therefore, its hepatoprotective activity. Thus, the novel INH–CRM has the potential to alleviate INH-induced liver toxicity in antitubercular treatment.

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.

Utilization of CDKN1A/p21 gene for class discrimination of DNA damage-induced clastogenicity

The in vitro mammalian cytogenetic tests monitor chromosomal aberrations in cultured mammalian cells to test the mutagenicity of compounds. Although these tests are especially useful for evaluating the potential clastogenic effects of chemicals, false positives associated with excessive toxicity occur frequently. There is a growing demand for mechanism-based assays to confirm positive results from cytogenetic tests. We hypothesized that a toxicogenomic approach that is based on gene expression profiles could be used to investigate mechanisms of genotoxicity. Human lymphoblastoid TK6 cells were treated with each of eight different genotoxins that included six DNA damaging compounds-mitomycin C, methyl methanesulfonate, ethyl methanesulfonate, cisplatin, etoposide, hydroxyurea-and two compounds that do not damage DNA-colchicine and adenine. Cells were exposed to each compound for 4h, and Affymetrix U133A microarrays were then used to comprehensively examine gene expression. A statistical analysis was used to select biomarker candidates, and 103 probes met our statistical criteria. Expression of cyclin-dependent kinase inhibitor 1A (CDKN1A)/p21 was ranked highest for discriminating DNA-damaging compounds. To further characterize the biological significance of alterations in gene expression, functional network analysis was performed with the 103 selected probes. Interestingly, a CDKN1A-centered interactome was identified as the most significant network. Together, these findings indicated that DNA-damaging compounds often induced changes in the expression of a large number of these 103 probes and that upregulation of CDKN1A was a common key feature of DNA damage stimuli. The utility of CDKN1A as a biomarker for assessing the genotoxicity of drug candidates was further evaluated; specifically, quantitative RT-PCR was used to assess the effects of 14 additional compounds-including DNA damaging genotoxins and genotoxins that do not damage DNA and five newly-synthesized drug candidates-on CDKN1A expression. In these assays, DNA damage-positive clastogens were clearly separated from DNA damage-negative compounds based on CDKN1A expression. In conclusion, CDKN1A may be a valuable biomarker for identifying DNA damage-inducing clastogens and as a follow-up assay for mammalian cytogenetic tests.

Distinguishing between genotoxic and non-genotoxic hepatocarcinogens by gene expression profiling and bioinformatic pathway analysis

A rapid and sensitive method to determine the characteristics of carcinogens is needed. In this study, we used a microarray-based genomics approach, with a short-term in vivo model, in combination with insights from statistical and mechanistic analyses to determine the characteristics of carcinogens. Carcinogens were evaluated based on the different mechanisms involved in the responses to genotoxic carcinogens and non-genotoxic carcinogens. Gene profiling was performed at two time points after treatment with six training and four test carcinogens. We mapped the DEG (differentially expressed gene)-related pathways to analyze cellular processes, and we discovered significant mechanisms that involve critical cellular components. Classification results were further supported by Comet and Micronucleus assays. Mechanistic studies based on gene expression profiling enhanced our understanding of the characteristics of different carcinogens. Moreover, the efficiency of this study was demonstrated by the short-term nature of the animal experiments that were conducted.

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.

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.

Is Phenylbutazone a Genotoxic Carcinogen? A Weight-of-Evidence Assessment

Published in silico, in vitro, in vivo laboratory animal and human data, together with information on biotransformation and data from structure-activity analyses with two decision-tree systems (ACToR and Toxtree), have been used in a weight-of-evidence (WoE) assessment to determine whether phenylbutazone (PBZ) is a genotoxic or a non-genotoxic carcinogen. This was undertaken to facilitate the risk assessment of human exposure to this veterinary drug via the consumption of horsemeat from treated animals. Despite problems with data interpretation at all tiers of the database, it was concluded that PBZ behaves like a genotoxic carcinogen with a threshold dose. This conclusion is based mainly on the results of a definitive rodent bioassay, and on the following observations: a) that PBZ has weak in vitro activity only at high concentrations in some genotoxicity assays, accompanied by high levels of cytotoxicity; b) that it (and a major metabolite) is able to cause sister chromatid exchanges in vivo in rodents; and c) that it can induce cytogenetic effects in vivo in humans. It also takes into account the known and predicted activities of the parent drug, some of its metabolites and two structural analogues, and, importantly, several of the drug's other biochemical effects that are unrelated to toxicity. However, this conclusion is not fully supported by all the evidence, and much of the information is based on old papers. Therefore, more studies are required to establish whether the concentration thresholds seen in vitro would translate to dose thresholds for carcinogenicity, such that a safe dose-level could be defined for the purposes of assessing risk. It was disappointing that a WoE approach to evaluating all of the available hazard data, as is increasingly being advocated to improve the hazard identification paradigm, was unable to provide definitive answers in this case, particularly in view of the large numbers of animals that had been used to provide much of the information.

Involvement of multiple cell cycle aberrations in early preneoplastic liver cell lesions by tumor promotion with thioacetamide in a two-stage rat hepatocarcinogenesis model

Thioacetamide (TAA) induces oxidative stress and hepatocarcinogenicity in rats. We previously reported that TAA promotion caused various disruptions in cell cycle protein expression in rats, including downregulation of p16(Ink4a), which is associated with intraexonic hypermethylation in hepatocellular proliferative lesions. This study further investigated the contribution of cell cycle aberrations associated with early hepatocarcinogenic processes induced by TAA using antioxidants, enzymatically modified isoquercitrin (EMIQ) and α-lipoic acid (ALA), in a two-stage rat hepatocarcinogenesis model. TAA-promotion after initiation with N-diethylnitrosamine increased the number and area of hepatocellular foci immunoreactive for glutathione S-transferase placental form (GST-P) and the numbers of proliferating and apoptotic cells. Co-treatment with EMIQ and ALA suppressed these increases. TAA-induced formation of p16(Ink4a-) foci in concordance with GST-P(+) foci was not suppressed by co-treatment with EMIQ or ALA. TAA-promotion increased cellular distributions of cell proliferation marker Ki-67, G2/M and spindle checkpoint proteins (phosphorylated checkpoint kinase 1 and Mad2), the DNA damage-related protein phosphorylated histone H2AX, and G2-M phase-related proteins (topoisomerase IIα, phosphorylated histone H3 and Cdc2) within GST-P(+) foci, and co-treatment with EMIQ or ALA suppressed these increases. These results suggest that downregulation of p16(Ink4a) may allow selective proliferation of preneoplastic cells by TAA promotion. However, antioxidants did not counteract this gene control. Moreover, effective suppression of TAA-induced cellular population changes within preneoplastic lesions by antioxidants may reflect facilitation of cell cycling and accumulation of DNA damage causing the activation of cell cycle checkpoints, leading to G2 and M phase arrest at the early stages of hepatocarcinogenesis promoted by TAA.

Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan

An important technology used in toxicogenomic drug discovery research is the microarray, which enables researchers to simultaneously analyze the expression of a large number of genes. To build a database and data analysis system for use in assessing the safety of drugs and drug candidates, in 2002 we conducted a 5-year collaborative study in the Toxicogenomics Project (TGP1) in Japan. Experimental data generated by such studies must be validated by different laboratories for robust and accurate analysis. For this purpose, we conducted intra- and inter-laboratory validation studies with participating companies in the second collaborative study in the Toxicogenomics Project (TGP2). Gene expression in the liver of rats treated with acetaminophen (APAP) was independently examined by the participating companies using Affymetrix GeneChip microarrays. The intra- and inter-laboratory reproducibility of the data was evaluated using hierarchical clustering analysis. The toxicogenomics results were highly reproducible, indicating that the gene expression data generated in our TGP1 project is reliable and compatible with the data generated by the participating laboratories.

Spectral analysis of thioacetamide-induced electroencephalographic changes in rats

Thioacetamide (TAA) is widely used as a model of hepatic encephalopathy (HE). The aim of our study was to investigate the effects of TAA on electroencephalographic (EEG) changes in rats and to compare them with human HE. Male Wistar rats were divided into groups: (1) saline-treated group and (2) TAA-treated groups: TAA300 (300 mg/kg), TAA600 (600 mg/kg), and TAA900 (900 mg/kg). Daily dose of TAA (300 mg/kg) was administered intraperitoneally once (TAA300), twice (TAA600), or thrice (TAA900) in subsequent days. EEG changes were recorded about 24 h after the last dose of TAA. Absolute and relative power density in alpha bands were significantly higher in TAA300 versus control group. In TAA300, absolute beta power density was higher and relative beta power density was lower versus control group. Absolute alpha, theta, delta, and relative theta power were significantly lower, while relative power in delta band was significantly higher in TAA900 versus control group ( p < 0.01). In conclusion, decrease in EEG voltage with an increase in delta relative power, which correspond to the EEG manifestations of severe HE in humans, was observed in TAA900 group. Electrical activity in TAA300 group correlates with mild HE in humans.

Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver

Long-term carcinogenicity testing of a compound is exceedingly time-consuming and costly, and requires many test animals, whereas the Ames test, which is based on the assumption that any substance that is mutagenic may also exert carcinogenic potential, is useful as a short-term screening assay but has major drawbacks. Although, in fact, 90% of compounds that give a positive Ames test cause cancer in laboratory animals, a good proportion of compounds that give a negative Ames test are also carcinogens; that is, there is no good correlation between carcinogenicity and negative Ames test results. As an alternative to these two approaches, we have tried applying toxicogenomics to predict the carcinogenicity of a compound from the gene expression profile induced in vivo. To establish our model, male Sprague-Dawley rats were orally administered test compounds (12 hepatocarcinogens and 26 non-hepatocarcinogens) for 28 days. Analysis of liver gene expression data by Support Vector Machines (SVM) dividing compounds into 'for training' and 'for test' (20 cases assigned randomly) allowed a set of marker genes to be tested for prediction of hepatocarcinogenicity. The developed prediction model was then validated with reference to the concordance rate with training data and test data, and a good performance was obtained. We will have new gene expression data and continue the validation of our model.

Comparison of hepatocarcinogen-induced gene expression profiles in conventional primary rat hepatocytes with in vivo rat liver

At present, substantial efforts are focused on the development of in vitro assays coupled with "omics" technologies for the identification of carcinogenic substances as an alternative to the classical 2-year rodent carcinogenicity bioassay. A prerequisite for the eventual regulatory acceptance of such assays, however, is the in vivo relevance of the observed in vitro findings. In the current study, hepatocarcinogen-induced gene expression profiles generated after the exposure of conventional cultures of primary rat hepatocytes to three non-genotoxic carcinogens (methapyrilene hydrochloride, piperonyl butoxide, and Wy-14643), three genotoxic carcinogens (aflatoxin B1, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, and 2-nitrofluorene), and two non-carcinogens (nifedipine and clonidine) are compared with previously obtained in vivo data after oral administration for up to 14 days of the same hepatocarcinogens to rats. In addition to the comparison of deregulated genes and functions per compound between in vivo and in vitro models, the major discriminating cellular pathways found in vivo in livers of exposed rats were examined for deregulation in vitro. Further, in vivo-derived gene signatures for the identification of genotoxic versus non-genotoxic carcinogens are used to classify in vitro-tested hepatocarcinogens and non-carcinogens. In the primary hepatocyte cultures, two out of the three tested genotoxic carcinogens mimicked the in vivo-relevant DNA damage response and were correctly assessed. Exposure to the non-genotoxic hepatocarcinogens, however, triggered a relatively weak response in the in vitro system, with no clear similarities to in vivo. This study contributes to the further optimization of toxicogenomics predictive tools when applied in in vitro settings.

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.