The Japanese toxicogenomics project: application of toxicogenomics

Role of CD44 expressed in renal tubules during maladaptive repair in renal fibrogenesis in an allopurinol-induced rat model of chronic kidney disease

The kidney is a major target organ for the adverse effects of pharmaceuticals; renal tubular epithelial cells (TECs) are particularly vulnerable to drug-induced toxicity. TECs have regenerative capacity; however, maladaptive repair of TECs after injury leads to renal fibrosis, resulting in chronic kidney disease (CKD). We previously reported the specific expression of CD44 in failed-repair TECs of rat CKD model induced by ischemia reperfusion injury. Here, we investigated the pathophysiological role of CD44 in renal fibrogenesis in allopurinol-treated rat CKD model. Dilated or atrophic TECs expressing CD44 in fibrotic areas were collected by laser microdissection and subjected to microarray analysis. Gene ontology showed that extracellular matrix (ECM)-related genes were upregulated and differentiation-related genes were downregulated in dilated/atrophic TECs. Ingenuity Pathway Analysis identified CD44 as an upstream regulator of fibrosis-related genes, including Fn1, which encodes fibronectin. Immunohistochemistry demonstrated that dilated/atrophic TECs expressing CD44 showed decreases in differentiation markers of TECs and clear expression of mesenchymal markers during basement membrane attachment. In situ hybridization revealed an increase in Fn1 mRNA in the cytoplasm of dilated/atrophic TECs, whereas fibronectin was localized in the stroma around these TECs, supporting the production/secretion of ECM by dilated/atrophic TECs. Overall, these data indicated that dilated/atrophic TECs underwent a partial epithelial-mesenchymal transition (pEMT) and that CD44 promoted renal fibrogenesis via induction of ECM production in failed-repair TECs exhibiting pEMT. CD44 was detected in the urine and serum of APL-treated rats, which may reflect the expression of CD44 in the kidney.

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

Ultra-high-field pharmacological functional MRI of dopamine D1 receptor-related interventions in anesthetized rats

The dopamine D1 receptor (D1R) is associated with schizophrenia, Parkinson's disease, and attention deficit hyperactivity disorder. Although the receptor is considered a therapeutic target for these diseases, its neurophysiological function has not been fully elucidated. Pharmacological functional MRI (phfMRI) has been used to evaluate regional brain hemodynamic changes induced by neurovascular coupling resulting from pharmacological interventions, thus phfMRI studies can be used to help understand the neurophysiological function of specific receptors. Herein, the blood oxygenation level-dependent (BOLD) signal changes associated with D1R action in anesthetized rats was investigated by using a preclinical ultra-high-field 11.7-T MRI scanner. PhfMRI was performed before and after administration of the D1-like receptor agonist (SKF82958), antagonist (SCH39166), or physiological saline subcutaneously. Compared to saline, the D1-agonist induced a BOLD signal increase in the striatum, thalamus, prefrontal cortex, and cerebellum. At the same time, the D1-antagonist reduced the BOLD signal in the striatum, thalamus, and cerebellum by evaluating temporal profiles. PhfMRI detected D1R-related BOLD signal changes in the brain regions associated with high expression of D1R. We also measured the early expression of c-fos at the mRNA level to evaluate the effects of SKF82958 and isoflurane anesthesia on neuronal activity. Regardless of the presence of isoflurane anesthesia, c-fos expression level was increased in the region where positive BOLD responses were observed with administration of SKF82958. These findings demonstrated that phfMRI could be used to identify the effects of direct D1 blockade on physiological brain functions and also for neurophysiological assessment of dopamine receptor functions in living animals.

Prediction of drug-induced hepatotoxicity based on histopathological whole slide images

Liver is an important metabolic organ in human body and is sensitive to toxic chemicals or drugs. Adverse reactions caused by drug hepatotoxicity will damage the liver and hepatotoxicity is the leading cause of removal of approved drugs from the market. Therefore, it is of great significance to identify liver toxicity as early as possible in the drug development process. In this study, we developed a predictive model for drug hepatotoxicity based on histopathological whole slide images (WSI) which are the by-product of drug experiments and have received little attention. To better represent the WSIs, we constructed a graph representation for each WSI by dividing it into small patches, taking sampled patches as nodes and calculating the correlation coefficients between node features as the edges of the graph structure. Then a WSI-level graph convolutional network (GCN) was built to effectively extract the node information of the graph and predict the toxicity. In addition, we introduced a gated attention global context vector (gaGCV) to combine the global context to make node features to contain more comprehensive information. The results validated on rat liver in vivo data from the Open TG-GATES show that the use of WSI for the prediction of toxicity is feasible and effective.

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays

The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.

In Silico Models for Hepatotoxicity

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

Tox-GAN: An AI Approach Alternative to Animal Studies-a Case Study with Toxicogenomics

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology towards "reducing, refining and replacing" (3Rs) animal use. Here, we proposed a deep generative adversarial network (GAN)-based framework capable of deriving new animal results from existing animal studies without additional experiments. To prove the concept, we employed this Tox-GAN framework to generate both gene activities and expression profiles for multiple doses and treatment durations in toxicogenomics (TGx). Using the pre-existing rat liver TGx data from the Open TG-GATEs, we generated Tox-GAN transcriptomic profiles with high similarity (0.997 ± 0.002 in intensity and 0.740 ± 0.082 in fold change) to the corresponding real gene expression profiles. Consequently, Tox-GAN showed an outstanding performance in two critical TGx applications, gaining a molecular understanding of underlying toxicological mechanisms and gene expression-based biomarker development. For the former, over 87% agreement in Gene Ontology was found between Tox-GAN results and real gene expression data. For the latter, the concordance of biomarkers between real and generated data was high in both predictive performance and biomarker genes. We also demonstrated that the Tox-GAN models constructed with TG-GATEs data were capable of generating transcriptomic profiles reported in DrugMatrix. Finally, we demonstrated potential utility for Tox-GAN in aiding chemical-based read-across. To the best of our knowledge, the proposed Tox-GAN model is novel in its ability to generate in vivo transcriptomic profiles at different treatment conditions from chemical structures. Overall, Tox-GAN holds great promise for generating high-quality toxicogenomic profiles without animal experimentation.

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases.

The Promise of AI for DILI Prediction

Drug-induced liver injury (DILI) is a common reason for the withdrawal of a drug from the market. Early assessment of DILI risk is an essential part of drug development, but it is rendered challenging prior to clinical trials by the complex factors that give rise to liver damage. Artificial intelligence (AI) approaches, particularly those building on machine learning, range from random forests to more recent techniques such as deep learning, and provide tools that can analyze chemical compounds and accurately predict some of their properties based purely on their structure. This article reviews existing AI approaches to predicting DILI and elaborates on the challenges that arise from the as yet limited availability of data. Future directions are discussed focusing on rich data modalities, such as 3D spheroids, and the slow but steady increase in drugs annotated with DILI risk labels.

Identification of Translational microRNA Biomarker Candidates for Ketoconazole-Induced Liver Injury Using Next-Generation Sequencing

Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Reliable and translational biomarkers are needed for early detection of DILI. microRNAs (miRNAs) have received wide attention as a novel class of potential DILI biomarkers. However, it is unclear how DILI drugs other than acetaminophen may influence miRNA expression or which miRNAs could serve as useful biomarkers in humans. We selected ketoconazole (KCZ), a classic hepatotoxin, to study miRNA biomarkers for DILI as a proof of concept for a workflow that integrated in vivo, in vitro, and bioinformatics analyses. We examined hepatic miRNA expression in KCZ-treated rats at multiple doses and durations using miRNA-sequencing and correlated our results with conventional DILI biomarkers such as liver histology. Significant dysregulation of rno-miR-34a-5p, rno-miR-331-3p, rno-miR-15b-3p, and rno-miR-676 was associated with cytoplasmic vacuolization, a phenotype in rat livers with KCZ-induced injury, which preceded the elevation of serum liver transaminases (ALT and AST). Between rats and humans, miR-34a-5p, miR-331-3p, and miR-15b-3p were evolutionarily conserved with identical sequences, whereas miR-676 showed 73% sequence similarity. Using quantitative PCR, we found that the levels of hsa-miR-34a-5p, hsa-miR-331-3p, and hsa-miR-15b-3p were significantly elevated in the culture media of HepaRG cells treated with 100 µM KCZ (a concentration that induced cytotoxicity). Additionally, we computationally characterized the miRNA candidates for their gene targeting, target functions, and miRNA/target evolutionary conservation. In conclusion, we identified miR-34a-5p, miR-331-3p, and miR-15b-3p as translational biomarker candidates for early detection of KCZ-induced liver injury with a workflow applicable to computational toxicology studies.

Relevance of In Vitro Transcriptomics for In Vivo Mode of Action Assessment

Recently, we reported an in vitro toxicogenomics comparison approach to categorize chemical substances according to similarities in their proposed toxicological modes of action. Use of such an approach for regulatory purposes requires, among others, insight into the extent of biological concordance between in vitro and in vivo findings. To that end, we applied the comparison approach to transcriptomics data from the Open TG-GATEs database for 137 substances with diverging modes of action and evaluated the outcomes obtained for rat primary hepatocytes and for rat liver. The results showed that a relatively small number of matches observed in vitro were also observed in vivo, whereas quite a large number of matches between substances were found to be relevant solely in vivo or in vitro. The latter could not be explained by physicochemical properties, leading to insufficient bioavailability or poor water solubility. Nevertheless, pathway analyses indicated that for relevant matches the mechanisms perturbed in vitro are consistent with those perturbed in vivo. These findings support the utility of the comparison approach as tool in mechanism-based risk assessment.

Effects of serial cervical or tail blood sampling on toxicity and toxicokinetic evaluation in rats

To assess the influences of blood sampling volumes or sites on toxicological and toxicokinetic (TK) evaluations, 4-week duration animal studies and a single-dose TK study of imipramine were conducted. In the toxicological evaluation, six-week-old Sprague-Dawley rats were divided into no blood and blood sampling groups. Fifty microliters (microsampling) or 100 μL (larger sampling) of blood/time point was collected from the jugular vein (50 μL of data was reported previously as Yokoyama et al., 2020) or the tail vein 6 to 7 times on days 1/2 and in week 4. Although no parameters were affected by the 100 μL sample from the tail vein, the 100 μL jugular vein sampling decreased the red blood cell parameters in females, possibly due to hemorrhage at the sampling site. Regarding the TK assessment, 50 μL of blood/site/time point was collected at 6 time points from the tail and jugular vein of the same male rats after single oral administration of 10 or 100 mg/kg imipramine, which was selected as a representative drug with high distribution volume. Although there were no differences in the AUC_0-24hr and C_max values between the sites, the plasma concentrations at the early time points were significantly lower from the tail vein than the jugular vein. From our studies, 50 μL of jugular and tail vein microsampling did not affect the toxicity parameters or AUC/C_max. However, appropriate toxicity considerations and/or selection of the blood sampling site may be important in the case of larger sampling volumes or blood concentration assessment.

Effect of common medications on the expression of SARS-CoV-2 entry receptors in liver tissue

Besides lung drastic involvement, SARS-CoV-2 severely affected other systems including liver. Emerging epidemiological studies brought the attentions towards liver injury and impairment as a potential outcome of COVID19. Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease (TMPRSS2) are the main cell entry receptors of SARS-CoV-2. We have tested the ability of medications to regulate expression of SARS-CoV-2 receptors. Understanding that may reflect how such medications may affect the level of infectivity and permissibility of the liver following COVID-19. Using transcriptomic datasets, Toxicogenomic Project-Genomics Assisted Toxicity Evaluation System (Open TG-GATEs) and GSE30351, we have tested the ability of ninety common medications to regulate COVID-19 receptors expression in human primary hepatocytes. Most medications displayed a dose-dependent change in expression of receptors which could hint at a potentially more pronounced change with chronic use. The expression level of TMPRSS2 was increased noticeably with a number of medications such as metformin. Within the analgesics, acetaminophen revealed a dose-dependent reduction in expression of ACE2, while non-steroidal anti-inflammatory drugs had mixed effect on receptors expression. To confirm the observed effects on primary human hepatocytes, rat hepatocyte treatments data was obtained from DrugMatrix toxicogenomic database (GSE57805), which showed a similar ACE2 and TMPRSS2 expression pattern. Treatment of common co-morbidities often require chronic use of multiple medications, which may result in an additive increase in the expression of ACE2 and TMPRSS2. More research is needed to determine the effect of different medications on COVID-19 receptors.

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.

[Transcriptomics-driven Evaluation on Liver Toxicity Using Adverse Outcome Pathways (AOP)]

Because the liver is the primary target organ for chemicals and pharmaceuticals, evaluation of these substances' liver toxicity is of critical importance. New evaluation methods without animal testing (i.e., in vitro and/or in silico) are eagerly anticipated, both for animal welfare and for decreasing cost. Also, the importance of mechanistic interpretation of the output derived from non-animal testing has been increasing. Accordingly, we investigated the potential for evaluating liver toxicity by applying the adverse outcome pathway (AOP) concept using gene set enrichment analysis (GSEA) from gene expression (GEx) data. A case study targeting hepatocellular fatty degeneration (HFD) is reported and discussed. We first identified the events detectable in an in vitro system by comparing the GEx data from the rat primary hepatocyte (in vitro) and rat liver (in vivo) treated with a chemical with the ability to induce HFD as one of the phenotypes in a 28-day repeated-dose toxicity test. Then, the scores based on GSEA were calculated after establishing the gene sets for each event leading to HFD. As a result, the mechanistic information leading to HFD was obtained from the score calculated based on the GSEA and the usefulness of the transcriptome-driven evaluation using AOP was demonstrated.

Mass spectrometry imaging reveals lipid upregulation and bile acid changes indicating amitriptyline induced steatosis in a rat model

As a consequence of the detoxification process, drugs and drug related metabolites can accumulate in the liver, resulting in drug induced liver injury (DILI), which is the major cause for dose limitation. Amitriptyline, a commonly used tricyclic anti-depressant, is known to cause DILI. The mechanism of Amitriptyline induced liver injury is not yet completely understood. However, as it undergoes extensive hepatic metabolism, unraveling the molecular changes in the liver upon Amitriptyline treatment can help understand Amitriptyline's mode of toxicity. In this study, Amitriptyline treated male rat liver tissue was analyzed using Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) to investigate the spatial abundances of Amitriptyline, lipids, and bile acids. The metabolism of Amitriptyline in liver tissue was successfully demonstrated, as the spatial distribution of Amitriptyline and its metabolites localize throughout treatment group liver samples. Several lipids appear upregulated, from which nine were identified as distinct phosphatidylcholine (PC) species. The detected bile acids were found to be lower in Amitriptyline treatment group. The combined results from histological findings, Oil Red O staining, and lipid zonation by MSI revealed lipid upregulation in the periportal area indicating drug induced macrovesicular steatosis (DIS).

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

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

Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies

Identifying hepatotoxicity as early as possible is significant in drug development. In this study, we developed a drug-induced hepatotoxicity prediction model taking account of both the biological context and the computational efficacy based on toxicogenomics data. Specifically, we proposed a novel gene selection algorithm considering gene's participation, named BioCB, to choose the discriminative genes and make more efficient prediction. Then instead of using the raw gene expression levels to characterize each drug, we developed a two-dimensional biological process feature pattern map to represent each drug. Then we employed two strategies to handle the maps and identify the hepatotoxicity, the direct use of maps, named Two-dim branch, and vectorization of maps, named One-dim branch. The two strategies subsequently used the deep convolutional neural networks and LightGBM as predictors, respectively. Additionally, we here for the first time proposed a stacked vectorized gene matrix, which was more predictive than the raw gene matrix. Results validated on both in vivo and in vitro data from two public data sets, the TG-GATES and DrugMatrix, show that the proposed One-dim branch outperforms the deep framework, the Two-dim branch, and has achieved high accuracy and efficiency. The implementation of the proposed method is available at https://github.com/RanSuLab/Hepatotoxicity.

Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.

A feature selection strategy for gene expression time series experiments with hidden Markov models

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.

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.

Assessment of the performance of the TGx-DDI biomarker to detect DNA damage-inducing agents using quantitative RT-PCR in TK6 cells

Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx-DDI transcriptomic biomarker can accurately distinguish DNA damage-inducing (DDI) from non-DDI exposures based on changes in the expression of 64 biomarker genes. The 64 genes were previously derived from whole transcriptome DNA microarray profiles of 28 reference agents (14 DDI and 14 non-DDI) after 4 h treatments of TK6 human lymphoblastoid cells. To broaden the applicability of TGx-DDI, we tested the biomarker using quantitative RT-PCR (qPCR), which is accessible to most molecular biology laboratories. First, we selectively profiled the expression of the 64 biomarker genes using TaqMan qPCR assays in 96-well arrays after exposing TK6 cells to the 28 reference agents for 4 h. To evaluate the classification capability of the qPCR profiles, we used the reference qPCR signature to classify 24 external validation chemicals using two different methods-a combination of three statistical analyses and an alternative, the Running Fisher test. The qPCR results for the reference set were comparable to the original microarray biomarker; 27 of the 28 reference agents (96%) were accurately classified. Moreover, the two classification approaches supported the conservation of TGx-DDI classification capability using qPCR; the combination of the two approaches accurately classified 21 of the 24 external validation chemicals, demonstrating 100% sensitivity, 81% specificity, and 91% balanced accuracy. This study demonstrates that qPCR can be used when applying the TGx-DDI biomarker and will improve the accessibility of TGx-DDI for genotoxicity screening. Environ. Mol. Mutagen. 60: 122-133, 2019. © 2018 Her Majesty the Queen in Right of Canada Environmental and Molecular Mutagenesis.

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets

The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.

Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart

Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds. Therefore, in this paper, we have discussed two approaches namely moving range chart (MRC) and logistic moving range chart (LMRC) for the identification of significant up-regulatory (UpR) and down-regulatory (DnR) gene-compound interactions as well as toxicogenomic biomarkers and their regulatory chemical compounds/drugs. We have investigated the performance of both MRC and LMRC approaches using simulated datasets. Simulation results show that both approaches perform almost equally in absence of outliers. However, in presence of outliers, the LMRC shows much better performance than the MRC. In case of real life toxicogenomic data analysis, the proposed LMRC approach detected some important down-regulated biomarker genes those were not detected by other approaches. Therefore, in this paper, our proposal is to use LMRC for robust identification of significant interactions between genes and chemical compounds/drugs.

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.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Models of Drug Induced Liver Injury (DILI) - Current Issues and Future Perspectives

Background:

Drug-induced Liver Injury (DILI) is an important cause of acute liver failure cases in the United States, and remains a common cause of withdrawal of drugs in both preclinical and clinical phases.

Methods:

A structured search of bibliographic databases – Web of Science Core Collection, Scopus and Medline for peer-reviewed articles on models of DILI was performed. The reference lists of relevant studies was prepared and a citation search for the included studies was carried out. In addition, the characteristics of screened studies were described.

Results:

One hundred and six articles about the existing knowledge of appropriate models to study DILI in vitro and in vivo with special focus on hepatic cell models, variations of 3D co-cultures, animal models, databases and predictive modeling and translational biomarkers developed to understand the mechanisms and pathophysiology of DILI are described.

Conclusion:

Besides descriptions of current applications of existing modeling systems, associated advantages and limitations of each modeling system and future directions for research development are discussed as well.

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

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

Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse

Studies of gene expression are common in toxicology and provide important clues to mechanistic understanding of adverse effects of chemicals. Most prior studies have been performed in a single strain or cell line; however, gene expression is heavily influenced by the genetic background, and these genotype-expression differences may be key drivers of inter-individual variation in response to chemical toxicity. In this study, we hypothesized that the genetically diverse Collaborative Cross mouse population can be used to gain insight and suggest mechanistic hypotheses for the dose- and genetic background-dependent effects of chemical exposure. This hypothesis was tested using a model liver toxicant trichloroethylene (TCE). Liver transcriptional responses to TCE exposure were evaluated 24 h after dosing. Transcriptomic dose-responses were examined for both TCE and its major oxidative metabolite trichloroacetic acid (TCA). As expected, peroxisome- and fatty acid metabolism-related pathways were among the most dose-responsive enriched pathways in all strains. However, nearly half of the TCE-induced liver transcriptional perturbation was strain-dependent, with abundant evidence of strain/dose interaction, including in the peroxisomal signaling-associated pathways. These effects were highly concordant between the administered TCE dose and liver levels of TCA. Dose-response analysis of gene expression at the pathway level yielded points of departure similar to those derived from the traditional toxicology studies for both non-cancer and cancer effects. Mapping of expression-genotype-dose relationships revealed some significant associations; however, the effects of TCE on gene expression in liver appear to be highly polygenic traits that are challenging to positionally map. This study highlights the usefulness of mouse population-based studies in assessing inter-individual variation in toxicological responses, but cautions that genetic mapping may be challenging because of the complexity in gene exposure-dose relationships.

Computational Network Analysis for Drug Toxicity Prediction

The computational prediction of compound effects from molecular data is an important task in hazard and risk assessment and pivotal for judging the safety of any drug, chemical or cosmetic compound. In particular, the identification of such compound effects at the level of molecular interaction networks can be helpful for the construction of adverse outcome pathways (AOPs). AOPs emerged as a guiding concept for toxicity prediction, because of the inherent mechanistic information of such networks. In fact, integrating molecular interactions in transcriptome analysis and observing expression changes in closely interacting genes might allow identifying the key molecular initiating events of compound toxicity.In this work we describe a computational approach that is suitable for the identification of such network modules from transcriptomics data, which is the major molecular readout of toxicogenomics studies. The approach is composed of different tools (1) for primary data analysis, i.e., the biostatistical quantification of the gene expression changes, (2) for functional annotation and prioritization of genes using literature mining, as well as (3) for the construction of an interaction network that consists of interactions with high confidence and the identification of predictive modules from these networks. We describe the different steps of the approach and demonstrate its performance with public data on drugs that induce hepatic and cardiac toxicity.

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury

Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can be used to assess and predict DILI. Among much information available, several reference drug lists with annotated human DILI risk are of important. The LTKB DILI classification data include DILI severity concern determined by the FDA drug labeling, DILI severity score from the NIH LiverTox database, and other DILI classification schemes from the literature. Overall, ~1000 drugs were annotated with at least one classification scheme, of which around 750 drugs were flagged for some degree of DILI risk. Expert commentary: The LTKB provides a centralized repository of information for DILI study and predictive model development. The DILI classification data in LTKB could be a useful resource for developing biomarkers, predictive models and assessing data from emerging technologies such as in silico, high-throughput and high-content screening methodologies. In coming years, streamlining the prediction process by including DILI predictive models for both DILI severity and types in LTKB would enhance the identification of compounds with the DILI potential earlier in drug development and risk assessment.

Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology

Background

Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual’s response to therapy is complex, and may be best approached through a systems approach.

Methods

We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body.

Results

To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome.

Conclusions

We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0520-3) contains supplementary material, which is available to authorized users.

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.

BARHL1 Is Downregulated in Alzheimer's Disease and May Regulate Cognitive Functions through ESR1 and Multiple Pathways

The Transcription factor BarH like homeobox 1 (BARHL1) is overexpressed in medulloblastoma and plays a role in neurogenesis. However, much about the BARHL1 regulatory networks and their functions in neurodegenerative and neoplastic disorders is not yet known. In this study, using a tissue microarray (TMA), we report for the first time that BARHL1 is downregulated in hormone-negative breast cancers and Alzheimer’s disease (AD). Furthermore, using an integrative bioinformatics approach and mining knockout mouse data, we show that: (i) BARHL1 and Estrogen Receptor 1 (ESR1) may constitute a network that regulates Neurotrophin 3 (NTF3)- and Brain Derived Neurotrophic Factor (BDNF)-mediated neurogenesis and neural survival; (ii) this is probably linked to AD pathways affecting aberrant post-translational modifications including SUMOylation and ubiquitination; (iii) the BARHL1-ESR1 network possibly regulates β-amyloid metabolism and memory; and (iv) hsa-mir-18a, having common key targets in the BARHL1-ESR1 network and AD pathway, may modulate neuron death, reduce β-amyloid processing and might also be involved in hearing and cognitive decline associated with AD. We have also hypothesized why estrogen replacement therapy improves AD condition. In addition, we have provided a feasible new mechanism to explain the abnormal function of mossy fibers and cerebellar granule cells related to memory and cognitive decline in AD apart from the Tau and amyloid pathogenesis through our BARHL1-ESR1 axis.

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.

Evaluation of in vivo and in vitro models of toxicity by comparison of toxicogenomics data with the literature

Toxicity affecting humans is studied by observing the effects of chemical substances in animal organisms (in vivo) or in animal and human cultivated cell lines (in vitro). Toxicogenomics studies collect gene expression profiles and histopathology assessment data for hundreds of drugs and pollutants in standardized experimental designs using different model systems. These data are an invaluable source for analyzing genome-wide drug response in biological systems. However, a problem remains that is how to evaluate the suitability of heterogeneous in vitro and in vivo systems to model the many different aspects of human toxicity. We propose here that a given model system (cell type or animal organ) is supported to appropriately describe a particular aspect of human toxicity if the set of compounds associated in the literature with that aspect of toxicity causes a change in expression of genes with a particular function in the tested model system. This approach provides candidate genes to explain the toxicity effect (the differentially expressed genes) and the compounds whose effect could be modeled (the ones producing both the change of expression in the model system and that are associated with the human phenotype in the literature). Here we present an application of this approach using a computational pipeline that integrates compound-induced gene expression profiles (from the Open TG-GATEs database) and biomedical literature annotations (from the PubMed database) to evaluate the suitability of (human and rat) in vitro systems as well as rat in vivo systems to model human toxicity.

Crowdsourcing biomedical research: leveraging communities as innovation engines

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.

Interactive Toxicogenomics: Gene set discovery, clustering and analysis in Toxygates

Toxygates was originally released as a user-friendly interface to enhance the accessibility of the large-scale toxicogenomics database, Open TG-GATEs, generated by the Japanese Toxicogenomics Project. Since the original release, significant new functionality has been added to enable users to perform sophisticated computational analysis with only modest bioinformatics skills. The new features include an orthologous mode for data comparison among different species, interactive clustering and heatmap visualisation, enrichment analysis of gene sets, and user data uploading. In a case study, we use these new functions to study the hepatotoxicity of peroxisome proliferator-activated receptor alpha (PPARα) agonist WY-14643. Our findings suggest that WY-14643 caused hypertrophy in the bile duct by intracellular Ca²⁺ dysregulation, which resulted in the induction of genes in a non-canonical WNT/Ca²⁺ signalling pathway. With this new release of Toxygates, we provide a suite of tools that allow anyone to carry out in-depth analysis of toxicogenomics in Open TG-GATEs, and of any other dataset that is uploaded.

A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database

The liver and the kidney are the most common targets of chemical toxicity, due to their major metabolic and excretory functions. However, since the liver is directly involved in biotransformation, compounds in many currently and normally used drugs could affect it adversely. Most chemical compounds are already labeled according to FDA-approved labels using DILI-concern scale. Drug Induced Liver Injury (DILI) scale refers to an adverse drug reaction. Many compounds do not exhibit hepatotoxicity at early stages of development, so it is important to detect anomalies at gene expression level that could predict adverse reactions in later stages. In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity. Using TG-GATEs a large-scale toxicogenomics database, we present a computational strategy to classify compounds by toxicity levels in human and animal models through patterns of gene expression. We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic. The goal is to define gene expression profiles capable of distinguishing the different subtypes of hepatotoxicity. The study illustrates that expression profiling can be used to classify compounds according to different hepatotoxic levels; to label those that are currently labeled as undertemined; and to determine if at the molecular level, animal models are a good proxy to predict hepatotoxicity in humans.

Network-based analysis of transcriptional profiles from chemical perturbations experiments

BACKGROUND

Methods for inference and comparison of biological networks are emerging as powerful tools for the identification of groups of tightly connected genes whose activity may be altered during disease progression or due to chemical perturbations. Connectivity-based comparisons help identify aggregate changes that would be difficult to detect with differential analysis methods comparing individual genes.

METHODS

In this study, we describe a pipeline for network comparison and its application to the analysis of gene expression datasets from chemical perturbation experiments, with the goal of elucidating the modes of actions of the profiled perturbations. We apply our pipeline to the analysis of the DrugMatrix and the TG-GATEs, two of the largest toxicogenomics resources available, containing gene expression measurements for model organisms exposed to hundreds of chemical compounds with varying carcinogenicity and genotoxicity.

RESULTS

Starting from chemical-specific transcriptional networks inferred from these data, we show that the proposed comparative analysis of their associated networks identifies groups of chemicals with similar functions and similar carcinogenicity/genotoxicity profiles. We also show that the in-silico annotation by pathway enrichment analysis of the gene modules with a significant gain or loss of connectivity for specific groups of compounds can reveal molecular pathways significantly associated with the chemical perturbations and their likely modes of action.

CONCLUSIONS

The proposed pipeline for transcriptional network inference and comparison is highly reproducible and allows grouping chemicals with similar functions and carcinogenicity/genotoxicity profiles. In the context of drug discovery or drug repositioning, the methods presented here could help assign new functions to novel or existing drugs, based on the similarity of their associated network with those built for other known compounds. Additionally, the method has broad applicability beyond the uses here described and could be used as an alternative or as a complement to standard approaches of differential gene expression analysis.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.