Redrawing the map to novel DILI biomarkers in circulation: Where are we, where should we go, and how can we get there?

Circulating biomarkers of drug-induced liver injury (DILI) have been a focus of research in hepatology over the last decade, and several novel DILI biomarkers that hold promise for certain applications have been identified. For example, glutamate dehydrogenase holds promise as a specific biomarker of liver injury in patients with concomitant muscle damage. It may also be a specific indicator of mitochondrial damage. In addition, microRNA-122 is sensitive for early detection of liver injury in acetaminophen overdose patients. However, recent events in the field of DILI biomarker research have provided us with an opportunity to step back, consider how biomarker discovery has been done thus far, and determine how to move forward in a way that will optimize the discovery process. This is important because major challenges remain in the DILI field and related areas that could be overcome in part by new biomarkers. In this short review, we briefly describe recent progress in DILI biomarker discovery and development, identify current needs, and suggest a general approach to move forward.

Increased MMAB level in mitochondria as a novel biomarker of hepatotoxicity induced by Efavirenz

Background

Efavirenz (EFV), a non-nucleoside reverse transcriptase inhibitor (NNRTI), has been widely used in the therapy of human immunodeficiency virus (HIV) infection. Some of its toxic effects on hepatic cells have been reported to display features of mitochondrial dysfunction through bioenergetic stress and autophagy, etc. However, alteration of protein levels, especially mitochondrial protein levels, in hepatic cells during treatment of EFV has not been fully investigated.

Methods

We built a cell model of EFV-induced liver toxicity through treating Huh-7 cells with different concentrations of EFV for different time followed by the analysis of cell viability using cell counting kit -8 (CCK8) and reactive oxygen species (ROS) using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA) and MitoSox dye. Proteomic profiles in the mitochondria of Huh-7 cells stimulated by EFV were analyzed. Four differentially expressed proteins were quantified by real time RT-PCR. We also detected the expression of mitochondrial precursor Cob(I)yrinic acid a,c-diamide adenosyltransferase (MMAB) by immunohistochemistry analysis in clinical samples. The expression levels of MMAB and ROS were detected in EFV-treated Huh-7 cells with and without shRNA used to knock down MMAB, and in primary hepatocytes (PHC). The effects of other anti-HIV drugs (nevirapine (NVP) and tenofovirdisoproxil (TDF)), and hydrogen peroxide (H₂O₂) were also tested. Amino acid analysis and fatty aldehyde dehydrogenase (ALDH3A2) expression after MMAB expression knock-down with shRNA was used to investigate the metabolic effect of MMAB in Huh-7 cells.

Results

EFV treatment inhibited cell viability and increased ROS production with time- and concentration-dependence. Proteomic study was performed at 2 hours after EFV treatment. After treated Huh-7 cells with EFV (2.5mg/L or 10 mg/L) for 2 h, fifteen differentially expressed protein spots from purified mitochondrion that included four mitochondria proteins were detected in EFV-treated Huh-7 cells compared to controls. Consistent with protein expression levels, mRNA expression levels of mitochondrial protein MMAB were also increased by EFV treatment. In addition, the liver of EFV-treated HIV infected patients showed substantially higher levels of MMAB expression compared to the livers of untreated or protease inhibitor (PI)-treated HIV-infected patients. Furthermore, ROS were found to be decreased in Huh-7 cells treated with shMMAB compared with empty plasmid treated with EFV at the concentration of 2.5 or 10 mg/L. MMAB was increased in EFV-treated Huh-7 cells and primary hepatocytes. However, no change in MMAB expression was detected after treatment of Huh-7 cells and primary hepatocytes with anti-HIV drugs nevirapine (NVP) and tenofovirdisoproxil (TDF), or hydrogen peroxide (H₂O₂), although ROS was increased in these cells. Finally, knockdown of MMAB by shRNA induced increases in the β-Alanine (β-Ala) production levels and decrease in ALDH3A2 expression.

Conclusions

A mitochondrial proteomic study was performed to study the proteins related to EFV-inducted liver toxicity. MMAB might be a target and potential biomarker of hepatotoxicity in EFV-induced liver toxicity.

The Microphysiology Systems Database for Analyzing and Modeling Compound Interactions with Human and Animal Organ Models

Microfluidic human organ models, microphysiology systems (MPS), are currently being developed as predictive models of drug safety and efficacy in humans. To design and validate MPS as predictive of human safety liabilities requires safety data for a reference set of compounds, combined with in vitro data from the human organ models. To address this need, we have developed an internet database, the MPS database (MPS-Db), as a powerful platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. The present study demonstrates the capability of the MPS-Db in early safety testing using a human liver MPS to relate the effects of tolcapone and entacapone in the in vitro model to human in vivo effects. These two compounds were chosen to be evaluated as a representative pair of marketed drugs because they are structurally similar, have the same target, and were found safe or had an acceptable risk in preclinical and clinical trials, yet tolcapone induced unacceptable levels of hepatotoxicity while entacapone was found to be safe. Results demonstrate the utility of the MPS-Db as an essential resource for relating in vitro organ model data to the multiple biochemical, preclinical, and clinical data sources on in vivo drug effects.

Identification of drug-induced toxicity biomarkers for treatment determination

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

Pharmacogenomic biomarkers for personalized medicine

Pharmacogenomics examines how the benefits and adverse effects of a drug vary among patients in a target population by analyzing genomic profiles of individual patients. Personalized medicine prescribes specific therapeutics that best suit an individual patient. Much current research focuses on developing genomic biomarkers to identify patients, to identify which patients would benefit from a treatment, have an adverse response, or no response at all, prior to treatment according to relevant differences in risk factors, disease types and/or responses to therapy. This review describes the use of the two personalized medicine biomarkers, prognostic and predictive, to classify patients into subgroups for treatment recommendation.

The metabolomic window into hepatobiliary disease

The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation, and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develops. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver.

High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury

Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals from the market. Although there is evidence that dosages of ≥100 mg/day are associated with increased risk for hepatotoxicity, many drugs are safe at such dosages. There is an unmet need to predict risk for DILI more reliably, and lipophilicity might be a contributing factor. We analyzed the combined factors of daily dose and lipophilicity for 164 US Food and Drug Administration-approved oral medications and observed high risk for hepatotoxicity (odds ratio [OR], 14.05; P < 0.001) for drugs given at dosages ≥100 mg/day and octanol-water partition coefficient (logP) ≥3. This defined the "rule-of-two." Similar results were obtained for an independent set of 179 oral medications with 85% of the rule-of-two positives being associated with hepatotoxicity (OR, 3.89; P < 0.01). Using the World Health Organization's Anatomical Therapeutic Chemical classification system, the rule-of-two performed best in predicting DILI in seven therapeutic categories. Among 15 rule-of-two positives, 14 were withdrawn from hepatotoxic drugs, and one was over-the-counter medication labeled for liver injury. We additionally examined drug pairs that have similar chemical structures and act on the same molecular target but differ in their potential for DILI. Again, the rule-of-two predicted hepatotoxicity reliably. Finally, the rule-of-two was applied to clinical case studies to identify hepatotoxic drugs in complex comedication regimes to further demonstrate its use.

CONCLUSION

Apart from dose, lipophilicity contributes significantly to risk for hepatotoxicity. Applying the rule-of-two is an appropriate means of estimating risk for DILI compared with dose alone.

Integrated analysis of transcriptomics and metabonomics profiles in aflatoxin B1-induced hepatotoxicity in rat

The aim of this work was to identify mechanisms and potential biomarkers for predicting the development and progression of aflatoxin B1 (AFB1)-induced acute hepatotoxicity. In this study, microarray analysis and metabolites profiles were used to identify shifts in gene expression and metabolite levels associated with the affected physiological processes of rats treated with AFB1. Histopathological examinations and serum biochemical analysis were simultaneously performed; the results indicated that hepatotoxicity occurred in higher dosage groups. However, gene expression analysis and metabolite profiles are more sensitive than general toxicity studies for detecting AFB1-induced acute hepatotoxicity as the patterns of low-dose AFB1-treated rats in these two technique platforms were more similar to the rats in higher dosage groups than to the control rats. Integrated analysis of the results from general toxicity studies, transcriptomics and metabonomics profiles suggested that p53 signaling pathway induced by oxidative damage was the crucial step in AFB1-induced acute hepatotoxicity, whereas gluconeogenesis and lipid metabolism disorder were found to be the major metabolic effects after acute AFB1 exposure. The genes and metabolites significantly affected in common in rat liver or serum of three doses AFB1 treatments served as potential biomarkers for detecting AFB1-induced acute hepatotoxicity.

Toxicogenomic analysis of the particle dose- and size-response relationship of silica particles-induced toxicity in mice

This study investigated the relationship between particle size and toxicity of silica particles (SP) with diameters of 30, 70, and 300 nm, which is essential to the safe design and application of SP. Data obtained from histopathological examinations suggested that SP of these sizes can all induce acute inflammation in the liver. In vivo imaging showed that intravenously administrated SP are mainly present in the liver, spleen and intestinal tract. Interestingly, in gene expression analysis, the cellular response pathways activated in the liver are predominantly conserved independently of particle dose when the same size SP are administered or are conserved independently of particle size, surface area and particle number when nano- or submicro-sized SP are administered at their toxic doses. Meanwhile, integrated analysis of transcriptomics, previous metabonomics and conventional toxicological results support the view that SP can result in inflammatory and oxidative stress, generate mitochondrial dysfunction, and eventually cause hepatocyte necrosis by neutrophil-mediated liver injury.

Latest advances in predicting DILI in human subjects: focus on biomarkers

INTRODUCTION

The quest for a biomarker that would reliably identify patients at risk of developing acute drug-induced liver injury (DILI) to a specific agent or class of agents before it occurs, has been underway for years. Historical host factors for DILI, such as older age and female gender, are not considered sufficient to truly predict an individual's inherent risk of DILI. In vitro and animal-based biomarker discoveries, in many instances, have not been considered accurate enough for drug development in human subjects nor for use in clinical practice.

AREAS COVERED

In order to assess the current state of biomarkers to predict idiosyncratic human DILI, the authors utilized the PubMed literature search tool to identify research reports dealing with clinical DILI biomarkers covering the period of 2010 through to June 2012. Studies involving pharmacogenetic, proteomic and toxicogenomic analyses are preferentially reviewed.

EXPERT OPINION

Although acute DILI has been linked to specific genetic associations (e.g., flucloxacillin and HLA-B*5701; and certain polymorphisms seen with anti-TB agent DILI), such predictors have been able to identify only some patients at risk for only a limited number of drugs. Proteomic-based biomarkers from stored sera in the US DILI Network, such as apolipoprotein E, have been identified as potential candidates, but require further study. As it currently stands, the quest for a widely applicable, validated DILI biomarker remains an ongoing clinical challenge.

Bisphenol A alters n-6 fatty acid composition and decreases antioxidant enzyme levels in rat testes: a LC-QTOF-based metabolomics study

Background

Male reproductive toxicity induced by exposure to bisphenol A (BPA) has been widely reported. The testes have proven to be a major target organ of BPA toxicity, so studying testicular metabolite variation holds promise for the discovery of mechanisms linked to the toxic effects of BPA on reproduction.

Methodology/Principal Findings

Male Sprague-Dawley rats were orally administered doses of BPA at the levels of 0, 50 mg/kg/d for 8 weeks. We used an unbiased liquid chromatography-quadrupole time-of-flight (LC-QTOF)-based metabolomics approach to discover, identify, and analyze the variation of testicular metabolites. Two n-6 fatty acids, linoleic acid (LA) and arachidonic acid (AA) were identified as potential testicular biomarkers. Decreased levels of LA and increased levels of AA as well as AA/LA ratio were observed in the testes of the exposed group. According to these suggestions, testicular antioxidant enzyme levels were detected. Testicular superoxide dismutase (SOD) declined significantly in the exposed group compared with that in the non-exposed group, and the glutathione peroxidase (GSH-Px) as well as catalase (CAT) also showed a decreasing trend in BPA treated group.

Conclusions/Significance

BPA caused testicular n-6 fatty acid composition variation and decreased antioxidant enzyme levels. This study emphasizes that metabolomics brings the promise of biomarkers identification for the discovery of mechanisms underlying reproductive toxicity.

A panel of serum microRNAs as specific biomarkers for diagnosis of compound- and herb-induced liver injury in rats

Background

Drug-induced liver injury (DILI) has been a public, economic and pharmaceutical issue for many years. Enormous effort has been made for discovering and developing novel biomarkers for diagnosing and monitoring both clinical and preclinical DILI at an early stage, though progress has been relatively slow. Additionally, herb-induced liver injury is an emerging cause of liver disease because herbal medicines are increasingly being used worldwide. Recently, circulating microRNAs (miRNAs) have shown potential to serve as novel, minimally invasive biomarkers to diagnose and monitor human cancers and other diseases at early stages.

Methodology/Principal Findings

In order to identify candidate miRNAs as diagnostic biomarkers for DILI, miRNA expression profiles of serum and liver tissue from two parallel liver injury Sprague-Dawley rat models induced by a compound (acetaminophen, APAP) or an herb (Dioscorea bulbifera, DB) were screened in this study. The initial screens were performed on serum using a MicroRNA TaqMan low-density qPCR array and on liver tissue using a miRCURY LNA hybridization array and were followed by a TaqMan probe-based quantitative reverse transcription-PCR (qRT-PCR) assay to validate comparison with serum biochemical parameters and histopathological examination. Two sets of dysregulated miRNA candidates in serum and liver tissue were selected in the screening phase. After qRT-PCR validation, a panel of compound- and herb- related serum miRNAs was identified.

Conclusions/Significance

We have demonstrated that this panel of serum miRNAs provides potential biomarkers for diagnosis of DILI with high sensitivity and specificity.

The liver toxicity biomarker study phase I: markers for the effects of tolcapone or entacapone

The Liver Toxicity Biomarker Study is a systems toxicology approach to discover biomarkers that are indicative of a drug's potential to cause human idiosyncratic drug-induced liver injury. In phase I, the molecular effects in rat liver and blood plasma induced by tolcapone (a "toxic" drug) were compared with the molecular effects in the same tissues by dosing with entacapone (a "clean" drug, similar to tolcapone in chemical structure and primary pharmacological mechanism). Two durations of drug exposure, 3 and 28 days, were employed. Comprehensive molecular analysis of rat liver and plasma samples yielded marker analytes for various drug-vehicle or drug-drug comparisons. An important finding was that the marker analytes associated with tolcapone only partially overlapped with marker analytes associated with entacapone, despite the fact that both drugs have similar chemical structures and the same primary pharmacological mechanism of action. This result indicates that the molecular analyses employed in the study are detecting substantial "off-target" markers for the two drugs. An additional interesting finding was the modest overlap of the marker data sets for 3-day exposure and 28-day exposure, indicating that the molecular changes in liver and plasma caused by short- and long-term drug treatments do not share common characteristics.

Effects of pooling samples on the performance of classification algorithms: a comparative study

A pooling design can be used as a powerful strategy to compensate for limited amounts of samples or high biological variation. In this paper, we perform a comparative study to model and quantify the effects of virtual pooling on the performance of the widely applied classifiers, support vector machines (SVMs), random forest (RF), k-nearest neighbors (k-NN), penalized logistic regression (PLR), and prediction analysis for microarrays (PAMs). We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. All investigated classifiers yield lower misclassification rates with smaller pool sizes. RF mainly outperforms other investigated algorithms, while accuracy levels are comparable among all the remaining ones. Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints.

Candidate proteins, metabolites and transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA) clinical study

Background

Spinal Muscular Atrophy (SMA) is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1) gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets.

Objective:

To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches.

Materials and Methods:

A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2–12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS) and to a number of secondary clinical measures.

Results

A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites) and 44 urine metabolites. No transcripts correlated with MHFMS.

Discussion

In this cross-sectional study, “BforSMA” (Biomarkers for SMA), candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm these findings, demonstrate sensitivity to change with disease progression, and assess potential impact on clinical trial design.

Trial Registry

Clinicaltrials.gov NCT00756821.

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.

A metabonomic characterization of (+)-usnic acid-induced liver injury by gas chromatography-mass spectrometry-based metabolic profiling of the plasma and liver in rat

Three doses of (+)-usnic acid (100, 200, and 240 mg/kg per d) were administered orally to Wistar rats for 8 days, and metabonomic characterization of (+)-usnic acid-induced liver injury based on gas chromatography-mass spectrometry metabolic profiles was evaluated. Serum biochemical analysis and histopathological examinations were simultaneously performed. The liver/body weight ratio was significantly increased in (+)-usnic acid-treated groups, whereas serum alanine aminotransferase and total bilirubin were significantly elevated. In liver sections of 200 and 240 mg/kg dosage groups, widespread hydropic degeneration of hepatocytes was observed. Clusters in partial least squares discriminant analysis score plots showed control and (+)-usnic acid-treated groups had an obvious separation. (+)-Usnic acid exposure can lead to disturbances in energy metabolism, amino acid metabolism, lipid metabolism, and nucleotide metabolism, which may be attributable to (+)-usnic acid toxicological effects on the liver through oxidative stress. The significant changes in 22 metabolites in liver might be adopted as potential biomarkers.

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.

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.

Functional toxicogenomics: mechanism-centered toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21^st century.

Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms

BACKGROUND

data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques.

RESULTS

the analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper.

CONCLUSION

no single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-dimensionality data.

The Japanese toxicogenomics project: application of toxicogenomics

Biotechnology advances have provided novel methods for the risk assessment of chemicals. The application of microarray technologies to toxicology, known as toxicogenomics, is becoming an accepted approach for identifying chemicals with potential safety problems. Gene expression profiling is expected to identify the mechanisms that underlie the potential toxicity of chemicals. This technology has also been applied to identify biomarkers of toxicity to predict potential hazardous chemicals. Ultimately, toxicogenomics is expected to aid in risk assessment. The following discussion explores potential applications and features of the Japanese Toxicogenomics Project.

Science letters: Proteomic analysis of differentially expressed proteins in mice with concanavalin A-induced hepatitis

OBJECTIVE

To find new protein biomarkers for the detection and evaluation of liver injury and to analyze the relationship between such proteins and disease progression in concanavalin A (Con A)-induced hepatitis.

METHODS

Twenty-five mice were randomly divided into five groups: an untreated group, a control group injected with phosphate buffered saline (PBS), and groups with Con A-induced hepatitis evaluated at 1, 3 and 6 h. Two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) were used to identify differences in protein expression among groups. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the results.

RESULTS

In mice with Con A-induced hepatitis, expression levels of four proteins were increased: RIKEN, fructose bisphosphatase 1 (fbp1), ketohexokinase (khk), and Chain A of class pi glutathione S-transferase. Changes in fbp1 and khk were confirmed by qRT-PCR.

CONCLUSION

Levels of two proteins, fbp1 and khk, are clearly up-regulated in mice with Con A-induced hepatitis.

Toxicology and safety of COMT inhibitors

The development of catechol-O-methyltransferase (COMT) inhibitors for the adjunct treatment to levodopa and aromatic L-amino acid decarboxylase (AADC) inhibitors in Parkinson's disease started in the late 1950s. The first-generation inhibitors were associated with toxic properties: they induced convulsions, or they were toxic to the liver. None of them was taken into clinical use. The second-generation inhibitors entacapone and tolcapone have now been in clinical use for over a decade, and some new inhibitors are under development. The main adverse events in the use of entacapone and tolcapone are dopaminergic and dependent of the concomitant use of levodopa, but the symptoms are generally moderate or mild. Among the non-dopaminergic adverse events, diarrhea is the most prominent one induced by both entacapone and tolcapone. In clinical use, entacapone has been safe, but tolcapone is under strict regulations on liver enzyme monitoring, since in the early years, a few hepatotoxicity cases appeared, three of them with fatal outcome. The mechanism behind tolcapone-induced liver toxicity has been evaluated both in vitro and in vivo, but no clear answer exists at the moment. In the regulatory animal studies, both inhibitors have been safe with no reported toxicity. Also nebicapone, the latest of the second-generation inhibitors in clinical trials has shown some liver enzyme elevations in human subjects. New inhibitors with a structure differing from nitrocatechols are under development. No safety concerns have been reported connected to COMT inhibiton as such. COMT knockout mice are fertile without any pathologies due to the total COMT inhibition.