Effect of two 5-HT6 receptor antagonists on the rat liver: a molecular approach

Pharmacodynamic and safety considerations for influenza vaccine and adjuvant design

INTRODUCTION

A novel adjuvant evaluation system for safety and immunogenicity is needed. Vaccination is important for infection prevention, for example, from influenza viruses. Adjuvants are considered critical for improving the effectiveness of influenza vaccines. Adjuvant development is an important issue in influenza vaccine design.

AREAS COVERED

A conventional in vivo evaluation method for vaccine safety has been limited in analyzing phenotypic and pathological changes. Therefore, it is difficult to obtain information on the changes at the molecular level. This review aims to explain the recently developed genomics analysis-based vaccine adjuvant safety evaluation tools verified by AddaVax^TM and polyinosinic-polycytidylic acid (poly I:C) using 18 biomarker genes and whole-virion inactivated influenza vaccine as a toxicity control. Genomics analyzes would help provide safety and efficacy information regarding influenza vaccine design by facilitating appropriate adjuvant selection.

EXPERT OPINION

The efficacy and safety profiles of influenza vaccines and adjuvants using genomics technologies provide useful information regarding immunogenicity, which is related to safety and efficacy. This approach provides important information to select appropriate inoculation routes, combinations of vaccine antigens and adjuvants, and dosing amounts. The efficacy of vaccine adjuvant evaluation by genomics analysis should be verified by various studies using various vaccines in the future.

Establishment of a novel safety assessment method for vaccine adjuvant development

Vaccines effectively prevent infectious diseases. Many types of vaccines against various pathogens that threaten humans are currently in widespread use. Recently, adjuvant adaptation has been attempted to activate innate immunity to enhance the effectiveness of vaccines. The effectiveness of adjuvants for vaccinations has been demonstrated in many animal models and clinical trials. Although a highly potent adjuvant tends to have high effectiveness, it also has the potential to increase the risk of side effects such as pain, edema, and fever. Indeed, highly effective adjuvants, such as poly(I:C), have not been clinically applied due to their high risks of toxicity in humans. Therefore, the task in the field of adjuvant development is to clinically apply highly effective and non- or low-toxic adjuvant-containing vaccines. To resolve this issue, it is essential to ensure a low risk of side effects and the high efficacy of an adjuvant in the early developmental phases. This review summarizes the theory and history of the current safety assessment methods for adjuvants, using the inactivated influenza vaccine as a model. Our novel method was developed as a system to judge the safety of a candidate compound using biomarkers identified by genomic technology and statistical tools. A systematic safety assessment tool for adjuvants would be of great use for predicting toxicity during novel adjuvant development, screening, and quality control.

An omics strategy for discovering pulmonary biomarkers potentially relevant to the evaluation of tobacco products

Smoking is known to cause serious lung diseases including chronic bronchitis, chronic obstructive lung disease, obstruction of small airways, emphysema and cancer. Tobacco smoke is a complex chemical aerosol containing at least 8000 chemical constituents, either tobacco derived or added by tobacco product manufacturers. Identification of all of the toxic agents in tobacco smoke is challenging, and efforts to understand the mechanisms by which tobacco use causes disease will be informed by new biomarkers of exposure and harm. In 2009, President Obama signed into law the Family Smoking Prevention and Tobacco Control Act granting the US FDA the authority to regulate tobacco products to protect public health. This perspective article presents the background, rationale and strategy for using omics technologies to develop new biomarkers, which may be of interest to the FDA when implementing the Family Smoking Prevention and Tobacco Control Act.

Impact of selenite and selenate on differentially expressed genes in rat liver examined by microarray analysis

Sodium selenite and sodium selenate are approved inorganic Se (selenium) compounds in human and animal nutrition serving as precursors for selenoprotein synthesis. In recent years, numerous additional biological effects over and above their functions in selenoproteins have been reported. For greater insight into these effects, our present study examined the influence of selenite and selenate on the differential expression of genes encoding non-selenoproteins in the rat liver using microarray technology. Five groups of nine growing male rats were fed with an Se-deficient diet or diets supplemented with 0.20 or 1.0 mg of Se/kg as sodium selenite or sodium selenate for 8 weeks. Genes that were more than 2.5-fold up- or down-regulated by selenite or selenate compared with Se deficiency were selected. GPx1 (glutathione peroxidase 1) was up-regulated 5.5-fold by both Se compounds, whereas GPx4 was up-regulated by only 1.4-fold. Selenite and selenate down-regulated three phase II enzymes. Despite the regulation of many other genes in an analogous manner, frequently only selenate changed the expression of these genes significantly. In particular, genes involved in the regulation of the cell cycle, apoptosis, intermediary metabolism and those involved in Se-deficiency disorders were more strongly influenced by selenate. The comparison of selenite- and selenate-regulated genes revealed that selenate may have additional functions in the protection of the liver, and that it may be more active in metabolic regulation. In our opinion the more pronounced influence of selenate compared with selenite on differential gene expression results from fundamental differences in the metabolism of these two Se compounds.

An in vitro study on 5-HT6 receptor antagonist induced hepatotoxicity based on biochemical assays and toxicogenomics

We investigated the effects of two 5-HT(6) receptor antagonists on rat primary hepatocytes using a combined biochemical and toxicogenomics approach. Both compounds share the same pharmacological target, but displayed strikingly different toxicity profiles in pre-clinical animal studies: While R7199 caused hepatic steatosis in rats, no hepatotoxicity was observed with R0074. Here, we partially reproduced the steatosis findings seen in vivo using primary rat hepatocytes. Biochemical analyses and gene expression results generally supported the findings observed in the animal model and also allowed the differentiation of both compounds with regards to hepatotoxic potential. In particular, the induction of Cyp 2B and Cyp 3A1 directly correlates to the findings in the livers of treated animals. The effects on genes of the steroideogenic pathway relate to the deregulation of cholesterol homeostasis. We also observed the inhibition of beta-oxidation, indicating impaired lipid metabolism. Hence, gene expression analysis in combination with biochemical parameters can provide additional insight into the possible mechanisms underlying adverse events.

Gene expression profiling for pharmaceutical safety assessment

Toxicogenomics is the application of gene expression profiling technology to toxicology. This results in the generation of very large and complex gene expression data sets associated with the development of toxicities. It is widely assumed that this data can be deconvoluted to reveal novel insights into toxicological processes that are of value to the task of risk assessment. More specifically, it is hoped that toxicogenomics will aid in the prediction of the toxic potential and mechanisms of toxicity of novel chemical entities. On the basis of such promise, the pharmaceutical industry has invested heavily in this area, as the perceived rewards in terms of improved pipeline efficiency and safer drugs are immense. Consequently, a great deal of groundwork has been done over the past several years to establish working methods in toxicogenomics, both within industry and academia, demonstrating utility in proof-of-concept studies, generating the databases on which some approaches depend, and developing new data analysis tools. Despite such activity, there is little reported evidence to suggest that toxicogenomics is making a significant impact on the discovery and development of drugs. This may partly reflect the understandable reluctance of pharmaceutical industries to share information in a competitive environment. It may also partly reflect difficulties in bridging the gap between theory and practice, as is required to deliver real value to the industry. This review will assess the successes and shortcomings of toxicogenomics, and consider how it can be usefully applied to a drug discovery pipeline.

Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit?

Almost 10 years ago, microarray technology was established as a new powerful tool for large-scale analysis of gene expression. Soon thereafter the new technology was discovered by toxicologists for the purpose of deciphering the molecular events underlying toxicity, and the term "Toxicogenomics" appeared in scientific literature. Ever since, the toxicology community was fascinated by the multiplicity of sophisticated possibilities toxicogenomics seems to offer: genome-wide analysis of toxicant-induced expression profiles may provide a means for prediction of toxicity prior to classical toxicological endpoints such as histopathology or clinical chemistry. Some researchers even speculated of the classical methods being superfluous before long. It was assumed that by using toxicogenomics it would be possible to classify compounds early in drug development and consequently save animals, time, and money in pre-clinical toxicity studies. Moreover, it seemed within reach to unravel the molecular mechanisms underlying toxicity. The feasibility of bridging data derived from in vitro and in vivo systems, identifying new biomarkers, and comparing toxicological responses "across-species" was also excessively praised. After several years of intensive application of microarray technology in the field of toxicology, not only by the pharmaceutical industry, it is now time to survey its achievements and to question how many of these wishes and promises have really come true.

Comparison of the expression profiles induced by genotoxic and nongenotoxic carcinogens in rat liver

Application of recently developed gene expression techniques using microarrays in toxicological studies (toxicogenomics) facilitate the interpretation of a toxic compound's mode of action and may also allow the prediction of selected toxic effects based on gene expression changes. In order to test this hypothesis, we investigated whether carcinogens at doses known to induce liver tumors in the 2-year rat bioassay deregulate characteristic sets of genes in a short term in vivo study and whether these deregulated genes represent defined biological pathways. Male Wistar rats were dosed with the four nongenotoxic hepatocarcinogens methapyrilene (MPy, 60 mg/kg/day), diethylstilbestrol (DES, 10 mg/kg/day), Wy-14643 (Wy, 60 mg/kg/day), and piperonylbutoxide (PBO, 1200 mg/kg/day). After 1, 3, 7, and 14 days, the livers were taken for histopathological evaluation and for analysis of the gene expression profiles on Affymetrix RG_U34A arrays. The expression profile of the four nongenotoxic carcinogens were compared to the profiles of the four genotoxic carcinogens 2-nitrofluorene (2-NF), dimethylnitrosamine (DMN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), and aflatoxin B1 (AB1) from a similar study reported previously. By using statistical and clustering tools characteristically deregulated genes were extracted and functionally classified. Distinct cellular pathways were affected by the nongenotoxic carcinogens compared to the genotoxic carcinogens which at least partly correlated with the two-stage model of carcinogenesis. Characteristic to genotoxic carcinogens were a DNA damage response and the activation of proliferative and survival signaling. Nongenotoxic carcinogens showed responses to oxidative DNA or protein damage, as well as cell cycle progression and signs of regeneration. Many of the gene alterations found with the nongenotoxic carcinogens imply compound-specific mechanisms. Although neither a single gene nor a single pathway will be sufficient to discriminate the two classes of carcinogens, it became evident that combinations of pathway-associated gene expression profiles may be used to predict a genotoxic or nongenotoxic carcinogenic potential of a compound in short-term studies.

Toxicogenomics in predictive toxicology in drug development

The goal of toxicology is the assessment of possible risk to man. An emerging technology with the potential to have a major impact on risk assessment is toxicogenomics. In this review, we provide an overview of the many possibilities for toxicogenomics including technology platforms, data interpretation, and regulatory perspective and we give examples of toxicogenomics investigations. Toxicogenomics is a powerful tool for compound classification, for mechanistic studies, and for the detection of toxicity markers. Thus, toxicogenomics helps in the extrapolation of findings across species and increases predictability. Biomarkers are valuable in the evaluation of compounds at earlier development phases, improving clinical candidate selection. Caution regarding the interpretation of the results is still necessary. Nevertheless, toxicogenomics will accelerate preclinical safety assessments and improve the prediction of toxic liabilities, as well as of potential risk accumulation for drug-drug or drug-disease interactions.

Discriminating different classes of toxicants by transcript profiling

Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.