Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes

Approaches to Test the Neurotoxicity of Environmental Contaminants in the Zebrafish Model: From Behavior to Molecular Mechanisms

The occurrence of neuroactive chemicals in the aquatic environment is on the rise and poses a potential threat to aquatic biota of currently unpredictable outcome. In particular, subtle changes caused by these chemicals to an organism's sensation or behavior are difficult to tackle with current test systems that focus on rodents or with in vitro test systems that omit whole-animal responses. In recent years, the zebrafish (Danio rerio) has become a popular model organism for toxicological studies and testing strategies, such as the standardized use of zebrafish early life stages in the Organisation for Economic Co-operation and Development's guideline 236. In terms of neurotoxicity, the zebrafish provides a powerful model to investigate changes to the nervous system from several different angles, offering the ability to tackle the mechanisms of action of chemicals in detail. The mechanistic understanding gained through the analysis of this model species provides a good basic knowledge of how neuroactive chemicals might interact with a teleost nervous system. Such information can help infer potential effects occurring to other species exposed to neuroactive chemicals in their aquatic environment and predicting potential risks of a chemical for the aquatic ecosystem. In the present article, we highlight approaches ranging from behavioral to structural, functional, and molecular analysis of the larval zebrafish nervous system, providing a holistic view of potential neurotoxic outcomes. Environ Toxicol Chem 2021;40:989-1006. © 2020 SETAC.

The application of 3D cell models to support drug safety assessment: opportunities & challenges

The selection of drug candidates early in development has become increasingly important to minimize the use of animals and to avoid costly failures of drugs later in development. In vitro systems to predict and assess organ toxicity have so far been of limited value due to difficulties in demonstrating in vivo-relevant toxicity at a cell culture level. To overcome the limitations of single-cell type monolayer cultures and short-lived primary cell preparations, researchers have created novel 3-dimensional culture systems which appear to more closely resemble in vivo biology. These could become a key for the pharmaceutical industry in the evaluation of drug candidates. However, the value and acceptance of those new models in standard drug safety applications have yet to be demonstrated. This review aims to provide an overview of the different approaches undertaken in the field of pre-clinical safety assessment, organ toxicity, in particular, with an emphasis on examples and technical challenges.

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

Background

High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results

In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators.

Conclusions

Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Biomarker classifiers for identifying susceptible subpopulations for treatment decisions

AIM

A main goal of pharmacogenomics is to develop genomic signatures to predict patients' responses to a drug or therapy for treatment decisions. Identification of patients who would have no beneficial effect or have the risk of developing adverse effects from an unnecessary treatment could save enormous cost in the healthcare system and clinical trials. This article presents an approach for developing a biomarker classifier for identifying a fraction of susceptible patients, who should be spared unnecessary treatment prior to treatment.

MATERIALS & METHODS

The identification of susceptible patients involves two steps. The first step is to identify biomarkers of susceptibility from a mixture of biomarkers of susceptibility and biomarkers of response; the second step is to develop a classifier using an ensemble classification algorithm, as the number of susceptible patients is generally much smaller than the number of nonsusceptible patients.

RESULTS

Selection of the biomarkers of susceptibility is essential to achieve good prediction accuracy. The ensemble algorithm significantly improves the prediction accuracy compared with the standard classifiers.

CONCLUSION

The study shows that classifiers developed based on the biomarkers obtained by comparing the genomic profiles of responders to those of nonresponders may lead to a high misclassification error rate. Classifiers to identify a small fraction of the subpopulation should take imbalanced class sizes into consideration. A large sample size may be needed in order to ensure detection of a sufficient number of biomarkers and a sufficient number of susceptible subjects for classifier development and validation.

An approach to identifying preclinical biomarkers of susceptibility to drug-induced toxicity

Aim: Drug-induced toxicity that leads to termination of candidate drugs or postmarketing removal of approved drugs can potentially be explained by the existence of susceptible subpopulations. If the susceptible subpopulations are identified in advance, the drug’s benefits could be maximized by optimal treatment decisions. This article presents a statistical model and an approach for identifying pharmacogenomic biomarkers of susceptibility to drug-induced toxicity for detecting the susceptible subpopulations. Materials & methods: Biomarkers are categorized into three disjoint sets: biomarkers of both susceptibility and exposure (A); biomarkers of susceptibility only (B); and biomarkers of exposure only (C). Set B contains the most useful biomarkers to identify susceptible subpopulations prior to drug exposure; these markers demonstrate no change in response before and after drug exposure. We present a sample size analysis to illustrate the issues and challenges facing identifying biomarker set B. Results: The required sample size increases as the proportion of the susceptible subpopulation decreases. The examples demonstrated that at least 75 subjects per group are needed for a population with a 5% susceptible subpopulation and more than 1000 are often necessary. Conclusion: This study demonstrates that the biomarkers identified by common methods are a mixture of biomarkers of exposure and susceptibility (A and C), it further demonstrates that the proposed approach could be used to identify biomarkers of susceptibility (B), where a large sample size may be required for adequate power and low false-positive rate.

Original submitted 14 October 2010; Revision submitted 8 December 2010.

Reverse engineering adverse outcome pathways

The toxicological effects of many stressors are mediated through unknown, or incompletely characterized, mechanisms of action. The application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) can be used to overcome these limitations. This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows (FHM, Pimephales promelas). Gene expression changes in FHM ovaries in response to seven different chemicals, over different times, doses, and in vivo versus in vitro conditions, were captured in a large data set of 868 arrays. Potential AOPs of the antiandrogen flutamide were examined using two mutual information-based methods to infer gene regulatory networks and potential AOPs. Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment, thus leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biological processes, biomarkers, or alternative endpoints that can be used to monitor an AOP. Finally, the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented.

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

Background

There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.

Results

We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.

Conclusions

The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Genomic and proteomic approaches for predicting toxicity and adverse drug reactions

BACKGROUND

In the toxicology field, it remains a major challenge to predict and understand drug toxicity and adverse drug reactions (ADRs) in man.

OBJECTIVE

Recent progress in genomics and proteomics technologies and their application in predicting drug toxicity and ADRs.

METHODS

The key genomic and proteomic approaches are outlined, their applications in predicting toxicity and ADRs are described and their future developments in this field are discussed.

CONCLUSION

These technologies, used to measure expression at the transcript and protein levels, each convey different information and have different technical capabilities that can complement each other. The fields of genomics and proteomics continue to develop rapidly and it is already evident that genomic and proteomic approaches have much to contribute to the early prediction of drug toxicity and ADRs.

Serum-free collagen sandwich cultures of adult rat hepatocytes maintain liver-like properties long term: a valuable model for in vitro toxicity and drug-drug interaction studies

Cultures of primary hepatocytes from various species, including human, are used in several applications during pre-clinical drug development. Their use is however limited by cell survival and conservation of liver-specific functions in vitro. The differentiation status of hepatocytes in culture strongly depends on medium formulation and the extracellular matrix environment. We incubated primary rat hepatocytes for 10 days on collagen monolayer and in collagen sandwich cultures with or without serum. Restoration of polygonal cell shape and formation of functional bile canaliculi-like structures was stable only in serum-free sandwich cultures. Variations in general cell viability, as judged by the cellular ATP content, LDH release or apoptosis, were less pronounced between alternative cultures. The intracellular glutathione content was preserved close to in vivo levels especially in serum-free sandwich cultures. Basal activities of cytochrome P450 enzymes (P450) varied strongly between cultures. There was a minor effect on CYP1A but CYP2B activity was only detectable in the serum-free sandwich culture after 3 days and beyond. CYP2C activity was slightly elevated in both sandwich cultures, whereas CYP3A showed increased levels in both serum-free cultures. Inducibility of these P450s was fully maintained over time in serum-free collagen sandwich only. Gene expression was largely constant over time in serum-free sandwich cultures that was closest to liver. This liver-like property was supported by protein profiling results. Taken together, the serum-free collagen sandwich culture of primary rat hepatocytes maintained liver-like features over 10 days and is therefore a suitable model for long-term toxicity and drug-drug interaction studies.

Toxicogenomics and classic toxicology: how to improve prediction and mechanistic understanding of human toxicity

The field of toxicogenomics has been advancing during the past decade or so since its origin. Most pharmaceutical companies are using it in one or more ways to improve their productivity and supplement their classic toxicology studies. Acceptance of toxicogenomics will continue to grow as regulatory concerns are addressed, proof of concept studies are disseminated more fully, and internal case studies show value for the use of this new technology in concert with classic testing.

Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach

Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.