Genomic and genetic biomarkers of toxicity

Biomarkers in Veterinary Medicine

This article summarizes the relevant definitions related to biomarkers; reviews the general processes related to biomarker discovery and ultimate acceptance and use; and finally summarizes and reviews, to the extent possible, examples of the types of biomarkers used in animal species within veterinary clinical practice and human and veterinary drug development. We highlight opportunities for collaboration and coordination of research within the veterinary community and leveraging of resources from human medicine to support biomarker discovery and validation efforts for veterinary medicine.

Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow

Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world. The traditional experimental methods couldn't meet the current demand any more. With the development of the information technology and the growth of experimental data, in silico modeling has become a practical and rapid alternative for the assessment of chemical properties, especially for the toxicity prediction of organic chemicals. In this study, a quantitative regression workflow was built by KNIME to predict chemical properties. With this regression workflow, quantitative values of chemical properties can be obtained, which is different from the binary-classification model or multi-classification models that can only give qualitative results. To illustrate the usage of the workflow, two predictive models were constructed based on datasets of Tetrahymena pyriformis toxicity and Aqueous solubility. The qcv (2) and qtest (2) of 5-fold cross validation and external validation for both types of models were greater than 0.7, which implies that our models are robust and reliable, and the workflow is very convenient and efficient in prediction of various chemical properties.

The current status of biomarkers for predicting toxicity

INTRODUCTION

There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity.

AREAS COVERED

This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled 'Translational Biomarkers in Toxicology.' The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process.

EXPERT OPINION

There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process.

Glycocapture-assisted global quantitative proteomics (gagQP) reveals multiorgan responses in serum toxicoproteome

Blood is an ideal window for viewing our health and disease status. Because blood circulates throughout the entire body and carries secreted, shed, and excreted signature proteins from every organ and tissue type, it is thus possible to use the blood proteome to achieve a comprehensive assessment of multiple-organ physiology and pathology. To date, the blood proteome has been frequently examined for diseases of individual organs; studies on compound insults impacting multiple organs are, however, elusive. We believe that a characterization of peripheral blood for organ-specific proteins affords a powerful strategy to allow early detection, staging, and monitoring of diseases and their treatments at a whole-body level. In this paper we test this hypothesis by examining a mouse model of acetaminophen (APAP)-induced hepatic and extra-hepatic toxicity. We used a glycocapture-assisted global quantitative proteomics (gagQP) approach to study serum proteins and validated our results using Western blot. We discovered in mouse sera both hepatic and extra-hepatic organ-specific proteins. From our validation, it was determined that selected organ-specific proteins had changed their blood concentration during the course of toxicity development and recovery. Interestingly, the peak responding time of proteins specific to different organs varied in a time-course study. The collected molecular information shed light on a complex, dynamic, yet interweaving, multiorgan-enrolled APAP toxicity. The developed technique as well as the identified protein markers is translational to human studies. We hope our work can broaden the utility of blood proteomics in diagnosis and research of the whole-body response to pathogenic cues.

Blood transcriptomics: applications in toxicology

The number of new chemicals that are being synthesized each year has been steadily increasing. While chemicals are of immense benefit to mankind, many of them have a significant negative impact, primarily owing to their inherent chemistry and toxicity, on the environment as well as human health. In addition to chemical exposures, human exposures to numerous non-chemical toxic agents take place in the environment and workplace. Given that human exposure to toxic agents is often unavoidable and many of these agents are found to have detrimental human health effects, it is important to develop strategies to prevent the adverse health effects associated with toxic exposures. Early detection of adverse health effects as well as a clear understanding of the mechanisms, especially at the molecular level, underlying these effects are key elements in preventing the adverse health effects associated with human exposure to toxic agents. Recent developments in genomics, especially transcriptomics, have prompted investigations into this important area of toxicology. Previous studies conducted in our laboratory and elsewhere have demonstrated the potential application of blood gene expression profiling as a sensitive, mechanistically relevant and practical surrogate approach for the early detection of adverse health effects associated with exposure to toxic agents. The advantages of blood gene expression profiling as a surrogate approach to detect early target organ toxicity and the molecular mechanisms underlying the toxicity are illustrated and discussed using recent studies on hepatotoxicity and pulmonary toxicity. Furthermore, the important challenges this emerging field in toxicology faces are presented in this review article.

The role of gene expression profiling in drug discovery

Monitoring gene expression through the dual approaches of transcriptomics (RNA profiling) and proteomics (protein profiling) has become a key component in our efforts to understand complex biological processes. From the molecular stratification of disease states and the selection of potential drug targets, to patient selection and the confirmation of engagement of pharmacology in clinical studies, we are seeing the impact of gene expression profiling across all phases of the drug discovery process. Ongoing technological advances have driven an expansion in the use of these techniques, demonstrated utility in preclinical and clinical settings and increased regulatory and clinical acceptance. As technologies continue to advance apace, gene expression profiling is likely to play an increasingly important role in the future development of drug discovery.

Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

Identification and categorization of liver toxicity markers induced by a related pair of drugs

Drug-induced liver injury (DILI) is the primary adverse event that results in the withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in the pre-clinical or clinical phases of drug development. This paper presents an approach for identifying potential liver toxicity genomic biomarkers from a liver toxicity biomarker study involving the paired compounds entacapone (“non-liver toxic drug”) and tolcapone (“hepatotoxic drug”). Molecular analysis of the rat liver and plasma samples, combined with statistical analysis, revealed many similarities and differences between the in vivo biochemical effects of the two drugs. Six hundred and ninety-five genes and 61 pathways were selected based on the classification scheme. Of the 61 pathways, 5 were specific to treatment with tolcapone. Two of the 12 animals in the tolcapone group were found to have high ALT, AST, or TBIL levels. The gene Vars2 (valyl-tRNA synthetase 2) was identified in both animals and the pathway to which it belongs, the aminoacyl-tRNA biosynthesis pathway, was one of the three most significant tolcapone-specific pathways identified.

Phenotypic and genotypic assessment of concomitant drug-induced toxic effects in liver, kidney and blood

Several studies have characterized drug-induced toxicity in liver and kidney. However, the majority of these studies have been performed with 'individual' organs in isolation. Separately, little is known about the role of whole blood as a surrogate tissue in drug-induced toxicity. Accordingly, we investigated the 'concurrent' response of liver, kidney and whole blood during a toxic assault. Rats were acutely treated with therapeutics (acetaminophen, rosiglitazone, fluconazole, isoniazid, cyclophosphamide, amphotericin B, gentamicin and cisplatin) reported for their liver and/or kidney toxicity. Changes in clinical chemistry parameters (e.g. AST, urea) and/or observed microscopic tissue damage confirmed induced hepatotoxicity and/or nephrotoxicity by all drugs. Drug-induced toxicity was not confined to an 'individual' organ. Not all drugs elicited significant alterations in phenotypic parameters of toxicity (e.g. ALT, creatinine). Accordingly, the transcriptional profile of the organs was studied using a toxicity panel of 30 genes derived from literature. Each of the test drugs generated specific gene expression patterns which were unique for all three organs. Hierarchical cluster analyses of purported hepatotoxicants and nephrotoxicants each led to characteristic 'fingerprints' (e.g. decrease in Cyp3a1 indicative of hepatotoxicity; increase in Spp1 and decrease in Gstp1 indicative of nephrotoxicity). In whole blood cells, a set of genes was derived which closely correlated with individual drug-induced concomitant changes in liver or kidney. Collectively, these data demonstrate drug-induced multi-organ toxicity. Furthermore, our findings underscore the importance of transcriptional profiling during inadequate phenotypic anchorage and suggest that whole blood may be judiciously used as a surrogate for drug-induced extra-hematological organ toxicity.

Transcriptional profiling to identify biomarkers of disease and drug response

The discovery, biological qualification and analytical validation of genomic biomarkers requires extensive collaborations between individuals with expertise in biology, statistics, bioinformatics, chemistry, clinical medicine, regulatory science and so on. For clinical utility, blood-borne biomarkers (e.g., mRNA and miRNA) of organ damage, drug toxicity and/or response would be preferred to those that are tissue based. Currently used biomarkers such as serum creatinine (indicating renal dysfunction) denote organ damage whether caused by disease, physical injury or drugs. Therefore, it is anticipated that studies of disease will discover biomarkers that can also be used to identify drug-induced injury and vice versa. This article describes transcriptomic blood-borne biomarkers that have been reported to be connected with disease and drug toxicity. Much more qualification and validation needs to be carried out before many of these biomarkers can prove useful. Discussed here are some of the lessons learned and roadblocks to success.

Translational medicine and the value of biomarker qualification

The gap between development of exploratory biomarkers and their acceptance in drug development and regulatory review is a hurdle in the development of better therapies. The U.S. Food and Drug Administration has developed a regulatory process for biomarker qualification to accelerate the process by which new biomarkers are integrated in the development of therapies.

Oxygenomics in environmental stress

Environmental stressors such as chemicals and physical agents induce various oxidative stresses and affect human health. To elucidate their underlying mechanisms, etiology and risk, analyses of gene expression signatures in environmental stress-induced human diseases, including neuronal disorders, cancer and diabetes, are crucially important. Recent studies have clarified oxidative stress-induced signaling pathways in human and experimental animals. These pathways are classifiable into several categories: reactive oxygen species (ROS) metabolism and antioxidant defenses, p53 pathway signaling, nitric oxide (NO) signaling pathway, hypoxia signaling, transforming growth factor (TGF)-beta bone morphogenetic protein (BMP) signaling, tumor necrosis factor (TNF) ligand-receptor signaling, and mitochondrial function. This review describes the gene expression signatures through which environmental stressors induce oxidative stress and regulate signal transduction pathways in rodent and human tissues.

Pharmacogenetics and forensic toxicology

Large inter-individual variability in drug response and toxicity, as well as in drug concentrations after application of the same dosage, can be of genetic, physiological, pathophysiological, or environmental origin. Absorption, distribution and metabolism of a drug and interactions with its target often are determined by genetic differences. Pharmacokinetic and pharmacodynamic variations can appear at the level of drug metabolizing enzymes (e.g., the cytochrome P450 system), drug transporters, drug targets or other biomarker genes. Pharmacogenetics or toxicogenetics can therefore be relevant in forensic toxicology. This review presents relevant aspects together with some examples from daily routines.

Biomarkers for drug-induced liver injury

Of the estimated 10,000 documented human drugs, more than 1000 have been associated with drug-induced liver injury (DILI), although causality has not always been established clearly. Numerous biomarkers for DILI have been explored, but less than ten are adopted or qualified as valid by the US FDA. The biomarkers for DILI are individual or a panel of proteins, nucleic acids or metabolites from various sources, such as the liver, blood and urine. While most DILI biomarkers are drug independent, some possibly 'drug-specific' DILIs have been explored, but specificity and sensitivity of both types need to be improved for the diagnosis of DILI during drug development and in clinical practice. Novel approaches for DILI biomarkers have been actively investigated recently, but produced mainly animal-based biomarkers, which are possibly useful for drug development, but are not suitable or have not been validated for clinical applications. This review summarizes the current practice and future perspectives for DILI biomarkers.

Multi-gene expression-based statistical approaches to predicting patients' clinical outcomes and responses

Gene expression profiling technique now enables scientists to obtain a genome-wide picture of cellular functions on various human disease mechanisms which has also proven to be extremely valuable in forecasting patients' prognosis and therapeutic responses. A wide range of multivariate techniques have been employed in biomedical applications on such expression profiling data in order to identify expression biomarkers that are highly associated with patients' clinical outcome and to train multi-gene prediction models that can forecast various human disease outcome and drug toxicities. We provide here a brief overview on some of these approaches, succinctly summarizing relevant basic concepts, statistical algorithms, and several practical applications. We also introduce our recent in vitro molecular expression-based algorithm, the so-called COXEN technique, which uses specialized gene profile signatures as a Rosetta Stone for translating the information between two different biological systems or populations.

Application of microarray-based analysis of gene expression in the field of toxicogenomics

The field of toxicogenomics, which is becoming an important sub-discipline of toxicology, resulted from the natural convergence of the field of conventional toxicological research and the emergent field of functional genomics. One technology that has played a significant role in the field of toxicogenomics (in addition to many others) is the gene expression microarray. In this chapter, the authors provide an example of the application of gene expression microarrays to the field of toxicogenomics by detailing the strategy that was used for obtaining, analyzing, and interpreting gene expression data generated from RNA isolated from the liver of toxicant-exposed rats.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

Evaluation of aging influence on renal toxicity caused by segment-specific nephrotoxicants of the proximal tubule in rat

Little is known concerning the sensitivity of aged rats to xenobiotics inducing kidney damage. To increase this knowledge, the age-dependent response of the kidney to hexachloro-1 : 3-butadiene (HCBD) or potassium dichromate (chromate) was investigated. Rats were treated at different ages with a single dose of segment-specific nephrotoxicants of the proximal tubule, chosen on the basis of their specificity for S(3) and for S(1)-S(2) segments, respectively. The toxicological impact of these xenobiotics has been evaluated through biochemical and genomic markers, and histopathological investigation of kidney samples. HCBD treatment induced tubular necrosis of the S(3) segment of the proximal tubule associated with changes of toxicological markers unrelated to the age. In contrast, chromate treatment induced an increased kidney damage related to the rat age. In fact, histopathological investigation revealed that at 1 month of age tubular vacuolar degeneration was seen affecting S(1)-S(2) segments of the proximal tubule, whereas at 3 months of age tubular necrosis occurred in the same segments associated with tubular dilation of the distal portions. Consistently, biochemical analysis confirmed a direct correlation among genomic and biochemical marker variability and animal age. Altogether, the results show that during aging there is an increased sensitivity of kidney to chromate but not to HCBD-induced damage and evidence differential age-related selectivity of rats for nephrotoxic compounds. Significance for human risk assessment is discussed.

Computational toxicology: an overview of the sources of data and of modelling methods

BACKGROUND

Toxicology has the goal of ensuring the safety of humans, animals and the environment. Computational toxicology is an area of active development and great potential. There are tangible reasons for the emerging interest in this discipline from academia, industry, regulatory bodies and governments.

RESULTS

Pharmaceuticals, personal health care products, nutritional ingredients and products of the chemical industries are all potential hazards and need to be assessed. Toxicological tests for these products are costly, frequently use laboratory animals and are time-consuming. This delays end-user access to improved products or, conversely, the timely withdrawal of dangerous substances from the market. The aim of computational toxicology is to accelerate the assessment of potentially dangerous substances through in silico models.

CONCLUSIONS

In this review, we provide an overview of the development of models for computational toxicology. Addressing the significant divide between the experimental and computational worlds-believed to be a prime hindrance to computational toxicology-we briefly consider the fundamental issue of toxicological data and the assays they stem from. Different kinds of models that can be built using such data are presented: computational filters, models for specific toxicological endpoints and tools for the generation of testable hypotheses.

Applications of high-throughput genomics to antiviral research: evasion of antiviral responses and activation of inflammation during fulminant RNA virus infection

Host responses can contribute to the severity of viral infection, through the failure of innate antiviral mechanisms to recognize and restrict the pathogen, the development of intense systemic inflammation leading to circulatory failure or through tissue injury resulting from overly exuberant cell-mediated immune responses. High-throughput genomics methods are now being used to identify the biochemical pathways underlying ineffective or damaging host responses in a number of acute and chronic viral infections. This article reviews recent gene expression studies of 1918 H1N1 influenza and Ebola hemorrhagic fever in cell culture and animal models, focusing on how genomics experiments can be used to increase our understanding of the mechanisms that permit those viruses to cause rapidly overwhelming infection. Particular attention is paid to how evasion of type I IFN responses in infected cells might contribute to over-activation of inflammatory responses. Reviewing recent research and describing how future studies might be tailored to understand the relationship between the infected cell and its environment, this article discusses how the rapidly growing field of high-throughput genomics can contribute to a more complete understanding of severe, acute viral infections and identify novel targets for therapeutic intervention.

Toxicogenomics: transcription profiling for toxicology assessment

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.