| Sign In to gain access to subscriptions and/or personal tools. |
The Liver Toxicity Biomarker Study: Phase I Design and Preliminary Results
1 BG Medicine, Inc., Waltham, MA, USA Correspondence: Robert Nicholas McBurney, 610 N. Lincoln St., Waltham, MA 02451; e-mail:rmcburney{at}bg-medicine.com.
Drug-induced liver injury (DILI) is the primary adverse event that results in withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in development. The Liver Toxicity Biomarker Study (LTBS) is an innovative approach to investigate DILI because it compares molecular events produced in vivo by compound pairs that (a) are similar in structure and mechanism of action, (b) are associated with few or no signs of liver toxicity in preclinical studies, and (c) show marked differences in hepatotoxic potential. The LTBS is a collaborative preclinical research effort in molecular systems toxicology between the National Center for Toxicological Research and BG Medicine, Inc., and is supported by seven pharmaceutical companies and three technology providers. In phase I of the LTBS, entacapone and tolcapone were studied in rats to provide results and information that will form the foundation for the design and implementation of phase II. Molecular analysis of the rat liver and plasma samples combined with statistical analyses of the resulting datasets yielded marker analytes, illustrating the value of the broad-spectrum, molecular systems analysis approach to studying pharmacological or toxicological effects.
Key Words: liver toxicity • biomarker • entacapone • tolcapone Abbreviations: ALT, alanine aminotransferase • ANOVA, analysis of variance • AST, aspartate aminotransferase • CRADA, cooperative research and development agreement • CV, coefficient of variation • DTT, dithiothreitol • DFTMP, 1,1-difluoro-1-trimethylsilanyl methyl phosphanic acid • DILI, drug-induced liver injury • DNA, deoxyribonucleic acid • EH, high-dose, entacapone-treated cohort • EL, low-dose, entacapone-treated cohort • EM, medium-dose, entacapone-treated cohort • FDA, U.S. Food and Drug Administration • FT-MS, fourier transform mass spectrometer or spectrometry • FWHM, full width at half maximum • HPLC, high-performance liquid chromatography • LC/MS, liquid chromatography coupled to mass spectrometry • LTBS, The Liver Toxicity Biomarker Study • MALDI, matrix-assisted laser desorption ionization • MIAME, Minimal Information About a Microarray Experiment • MS, mass spectrometer or mass spectrometry • MS/MS, tandem mass spectrometry • NMR, nuclear magnetic resonance • PBS, phosphate-buffered saline • PCA, principal components analysis • QC, quality control • RIN, RNA integrity number • RNA, ribonucleic acid • RNAse, ribonuclease • RT, retention time • SCX, strong cation exchange • TEAB, tetraethylammonium bicarbonate • TFA, trifluoroacetic acid • TCEP, tris(2-carboxyethyl)phosphine • TH, high-dose, tolcapone-treated cohort • TL, low-dose, tolcapone-treated cohort • TM, medium-dose, tolcapone-treated cohort • ToF, time of flight • V, vehicle-treated cohort
Drug-Induced Liver Injury and the Liver Toxicity Biomarker Study Drug-induced toxicity is the primary reason for the withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in the clinical phases of drug development (Fung et al. 2001; Lasser et al. 2002; Shuster et al. 2005; Temple and Himmel 2002; Zimmerman 1999). From the analyses of available information on drug withdrawals and drug development terminations, it is apparent that the liver is the most frequent site of drug-induced toxicity (Fung et al. 2001; Kaplowitz 2001; Schuster et al. 2005). The consequences of drug-induced liver injury (DILI) can be catastrophic. In a prospective study, transplant-free survival for patients with DILI, excluding that caused by acetaminophen overdose, occurred in only 25% of cases (Ostapowicz et al. 2002). Beyond the direct cost in human suffering and health care expenditure, the cost of DILI to pharmaceutical companies is substantial, with one company alone estimating that DILI cost it $2 billion over a ten-year period (Rotman 2004). Clearly, there is a need to prevent DILI, either by preventing drugs that have a liability for causing liver injury from entering the marketplace or by close monitoring of patients who take drugs that carry such a liability, particularly if the drug is unique in its potential for therapeutic benefit. DILI that arises in clinical trials, but has not been seen in preclinical studies, can have an incidence in trial subjects or patients of one in ten to one in several thousand. DILI that is first recognized only when a drug is on the market generally has an incidence in the target patient population that is too low to be detected in common phase III clinical trials involving hundreds to single-digit thousands of patients. This type of liver toxicity, which has an incidence of one in ten thousand to one in one hundred thousand, is referred to as "idiosyncratic," indicating the key role of individual susceptibility in the generation of the liver injury. It is not clear whether these two different DILI situations are distinct in nature or simply represent a continuum of susceptibilities involving the same general biochemical mechanisms. Irrespective of the frequency of DILI in patients taking a certain drug, the fact that it occurs at all at therapeutic doses, even though no indications of liver toxicity were detected in high-dose toxicity studies performed in at least two animal species, is sufficient reason to reconsider the current approach to preclinical drug safety testing in the area of liver toxicity. In rethinking the current practice of determining a drug candidate’s potential for causing liver toxicity, the following hypothesis arises. Despite the absence of conventional indicators of liver toxicity in preclinical studies, there exist biochemical signals (molecular biomarkers) in liver or body fluids that can be used to distinguish between a drug candidate that has the potential to cause DILI in susceptible patients and drugs that do not have this potential. The Liver Toxicity Biomarker Study (LTBS) is an innovative approach to addressing the dilemma of unanticipated clinical drug-induced liver injury and its negative impact on individuals, the health care system, and pharmaceutical companies. The overall goal of the LTBS is to test the above hypothesis and, if the hypothesis is not refuted, to discover candidate biomarkers that could, following validation, be incorporated into the drug development process. The LTBS was conceived in late 2005 as a collaborative research effort in molecular systems toxicology between the US Food and Drug Administration’s (FDA’s) National Center for Toxicological Research (NCTR) and BG Medicine, Inc., and is being carried out under a Cooperative Research and Development Agreement (CRADA). The research program is supported financially and with scientific expertise by an international group of seven pharmaceutical companies (Mitsubishi Chemical Holdings Corporation, Eisai Co. Ltd., Daiichi Sankyo Co. Ltd., UCB Pharma, Orion Pharma, Johnson and Johnson, Inc. and Pfizer, Inc.) and is supported with access to certain technologies by Applied Biosystems, Inc., Affymetrix, Inc. and TIBCO Software, Inc. Oversight of the LTBS is provided by a scientific advisory committee, chaired by Dr. Paul Watkins of the University of North Carolina, which includes Dr. Neil Kaplowitz, University of Southern California; and Dr. John Senior, FDA, as well as representatives from the supporting companies, NCTR, and BG Medicine. The LTBS does not address the nature of an individual patient’s susceptibility for DILI (Kaplowitz 2005) but focuses on the biochemical effects of drugs that can interact with such susceptibility to cause DILI. Individual patient susceptibility is being addressed in clinical studies, such as those currently being conducted by the Drug-Induced Liver Injury Network (DILIN, http://dilin.dcri.duke.edu/index.html). The LTBS is based on the fundamental assumption that a specific type of DILI, such as hepatocellular necrosis, is likely to result from one of a small number of biochemical mechanisms. Therefore, drugs of different chemical structures and different primary mechanisms of therapeutic benefit, but which cause that specific type of liver injury in certain susceptible patients, are likely to share some common biochemical effects, most probably not related to their primary mechanism of action. By comparing, in preclinical studies, the "off-target" biochemical effects of a number of compounds that are known to cause clinical DILI, it should be possible to discover pre-clinical predictive biomarkers for DILI. The key feature of the research strategy for the LTBS is the comprehensive molecular systems analysis of the in vivo effects in rats of related compound pairs, one a "Clean Compound" and the other a "Toxic Compound" (see below for definitions), to discover the "off-target" biochemical response differences between the two compounds in each pair. The "on-target" biochemical response should be similar between the two compounds and, therefore, should be revealed by the comparison of the drug-effect biomarker sets for each compound in a compound pair. Clean Compound: An approved drug that exhibited no signs of liver toxicity in preclinical studies, in clinical trials, or on the market. The LTBS is addressing its objectives through a molecular systems analysis of liver tissue, blood plasma, and urine samples derived from an experimental paradigm that is based on a standard, three-dosage-level, twenty-eight-day rat toxicity study with animal sacrifice on day 29. Additional features of the LTBS are: an early sacrifice group for each compound at one dosage level (day 4 sacrifice after three days of dosing) and twenty-four-hour urine collections (days 0–4, and day 27 in the twenty-eight-day dosing study). Five compound pairs will eventually be studied in this experimental paradigm. A differential marker set, representing the comprehensive molecular differences between the tissue and/or body fluid biochemical response of rats to each member of a compound pair, will be determined. Five such differential marker sets will be generated in the LTBS. If no molecular components or predicted biochemical pathways are found to be common among at least some of the differential marker sets, the hypothesis will be considered refuted. If common molecular components or common predicted biochemical pathways are found among the differential marker sets, such molecular components or biochemical pathways will be designated putative liver toxicity predictive biomarkers. Any putative liver toxicity predictive biomarker will represent a new hypothesis that will require testing and validation in subsequent experiments that are currently outside the scope of the LTBS. To be useful practically, the sensitivity and specificity of a liver toxicity predictive biomarker must be determined through additional studies involving sufficient numbers of compounds to generate robust statistical results.
Phases I and II of the LTBS
Phase I Compound Pair
Contents of this Article
The overall design of phase I of the LTBS is shown in Figure 1. The overall workflow for the analysis of liver, plasma, and urine samples is shown in Figure 2.
Drugs Entacapone was provided by Orion (Espoo, Finland) as a single lot. Tolcapone was purchased from US Pharmacopeia (Rockville, MD, USA) as a single lot.
Animal Study Design, Conduct, and Sample Collection Although both male and female rats were included in the dosing and sample collection component of the study, the preliminary results reported here are for the male rats only. The gross and microscopic evaluation of the tissue samples and the clinical chemistry measurement on the plasma samples were undertaken on both sexes. Only the samples from the male rats have been subjected to the bioanalytical profiling at this time.
Dose Levels
Twenty-eight-day Dosing Study Two days before the initiation of dosing, the rats were transferred to metabolism cages (day –2) and allowed to acclimatize for forty-eight hours. A twenty-four-hour control urine sample was collected (beginning on day 0) on ice in 50-mL polypropylene tubes containing 1 mL of 1% sodium azide. The urine samples were immediately stored at –60°C for subsequent metabolomic analyses. Dosing was initiated on day 1 and continued for twenty-eight consecutive days. Body weights were obtained daily to determine the appropriate dosing volume. Urine samples were collected for twenty-four hours after each of the first four doses. On dose day 25 the animals were placed once again in metabolism cages and allowed to acclimatize before collecting a twenty-four-hour urine sample on day 27. The rats were euthanized by exposure to carbon dioxide one day after the last dose. Blood was collected by cardiac puncture, placed in tubes containing lithium heparin, and plasma was prepared, aliquoted into 500-µL portions, and frozen at –60°C. The livers were weighed as soon as possible after dissection. The median lobe of the livers from all animals was processed for histopathological examination. The remaining two lobes of the livers were immediately frozen in liquid nitrogen and stored at –60°C.
Three-day Dosing Study
Pathology
Plasma Clinical Chemistry Measurements
Analysis of Liver Samples For both liver and plasma sample analyses on all analytical platform, sample randomization schemes were created to distribute the primary samples and the QC pool samples randomly throughout the analytical platform run order and among analytical batches (see, for example, van der Greef et al. 2007).
Gene Transcript Analysis Qiagen RNeasy mini kits (Qiagen, Chatsworth, CA, USA) were used for RNA extraction following the standard Qiagen protocol. The concentration of the final RNA solution was determined by absorption at 260 nm. The RNA solution was stored at –80ºC until use. The quality of extracted RNA was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). All RNA samples had an RNA Integrity Number (RIN) greater than 8.0. Affymetrix GeneChip Rat Genome 230 v2.0 arrays (Affymetrix, Inc., Santa Clara, CA USA) were used for DNA microarray analysis. The built-in quality control was used to access the labeling and hybridization performance of each DNA microarray, and all were within recommended ranges. Protocols from Affymetrix were followed to process these microarray chips. Twelve GeneChips were processed in a batch. Each batch included eleven primary samples and one aliquot from the QC sample pool. The Affymetrix one-cycle amplification protocol was followed for cRNA amplification and biotin labeling. The Affymetrix GeneChip Instrument System, which includes work station, fluidics station, hybridization oven, and scanner, was used for the microarray process (hybridization, washing, staining, and scanning). The resulting images were analyzed by GCOS software (Affymetrix) to generate .cel files, which contain the raw intensity values of each probe. The data and experimental annotation were stored in the NCTR ArrayTrack database (Tong et al. 2003) that is Minimum Information About a Microarray Experiment (MIAME) compliant and a component of the NCTR Toxicoinformatics Integrated System (TIS) (Tong et al. 2004). PLIER, which is one of the multiple array–based normalization methods in the Affymetrix Expression Console Software, was then used to convert the probe-level intensity data into normalized probeset-level intensity data.
Proteomic Analysis
Peptide Chromatography
Mass Spectrometry and Data Processing The MS/MS spectra were searched against the International Protein Index rat protein database (version 3.36, European Bioinformatic Institute, www.ebi.ac.uk), using the Mascot Search Algorithm Version 2.0 (Matrix Biosciences, Boston, MA, USA). A mass tolerance of 50 ppm and 0.4 Da was used for precursor and fragment ions, respectively. Two missed cleavage sites were allowed. The following variable modifications were used: iTRAQ 8Plex (N-terminus and K), deamidation (N only), oxidation (M), Pyro CmC N-term CamC), and Pyro-Glu (N-term Q).
Lipid LC/MS Metabolomic Analysis
Amino Acid Analysis (AAA)
Polar LC/MS Metabolomics Analysis
Analysis of Plasma Samples
Proteomic Analysis iTRAQ labeling with the 8-plex reagent was carried out as described above for the liver sample proteomic analysis. Peptide chromatography, mass spectrometry, and data processing were carried out as described above for the liver sample proteomic analysis.
Multiplexed Protein Immunoassays Immunoassay results were analyzed in terms of coefficients of variations (CVs) as observed from twenty-four replicate QC pool samples and of number of valid measurements, that is, measurements above the limit of detection. Any antigen for which the CV exceeded 20% or for which the number of measurements below the limit of detection exceeded 20% of the samples (twenty-four or more samples) was excluded from the statistical analysis. This filtering procedure resulted in the use of nineteen analytes in the statistical analysis. Failing the QC or limit of detection criteria typically occurred together, indicating that poor CVs are associated with inadequate sensitivity of the assay for the given sample type. The main reason for the relatively few analytes satisfying the QC criteria is in the nature of "generic rodent" assays in RodentMAP Version2. These assays were not optimized specifically for rat antigens, but rather to deliver a balanced performance on both rat and mouse plasma samples.
Lipid LC/MS Metabolomic Analysis
Amino Acid Analysis
Polar LC/MS Metabolomic Analysis
Analysis of Urine Samples Urine and QC samples were prepared for analysis by the addition of 250 µL of sodium phosphate buffer (pH 7.4) and 75 µL of a mixture of 10 mM 1,1-difluoro-1-trimethylsilanyl methyl phosphanic acid (DFTMP) (Bridge Organics, Vicksburg, MI, USA) and 1 mM 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS-d6) (Sigma, St. Louis, MO, USA) in deuterium oxide (D2O, Cambridge Isotope Laboratories, Andover, MA, USA) to 500 µL of urine. The samples were centrifuged at 12,000 rpm for twelve minutes at 10ºC, and 550 µL were transferred to 5 mm o.d. NMR tubes. For quality assurance purposes, each day, a sensitivity standard was run first and the S/N recorded to evaluate performance before starting sample analysis. The QC sample was run every twenty samples throughout the NMR analysis. 2D NMR JRES and HSQC experiments were run overnight on the last urine sample evaluated each day. NMR spectra were acquired on a Bruker Avance 600 MHz spectrometer (Bruker, Billerica, MA, USA) operating at 600.133 MHz for proton. NMR spectra were acquired as previously described (Schnackenberg et al. 2007). The analysis time for each sample was approximately ten minutes using the parameters described above and including the autoshimming routine. Only ten urine samples were placed in the sample carousel at any one time to reduce the amount of time that the samples were exposed to room temperature prior to NMR analysis. Spectra for urine, QA, and QC samples were processed using ACD/Labs 1D NMR Manager Version 10 (ACD/Labs, Toronto, Ontario, Canada) as previously described (Schnackenberg et al. 2007). All spectra were autoreferenced to the DSS-d6 peak at 0.0 ppm. The table of integrals was exported as a text file for statistical analyses. Spectra were exported as JCAMP files for quantitative analysis. The full width at half maximum (FWHM) of the DSS-d6 peak at 0.00 ppm was assessed for each spectrum, and if the FWHM of DSS-d6 was greater than 3 Hz, the spectrum was rejected and the sample reanalyzed. The raw NMR data were processed using ACD/Labs 1D NMR Manager (ACD/Labs). JCAMP files for each spectrum were used for analysis of specific metabolites using the Chenomx Eclipse software (Chenomx, Edmonton, Alberta, Canada). The Chenomx NMR spectral database was used to determine the concentrations of specific metabolites in the spectra. The concentrations of the metabolites in each spectrum were normalized by the absolute spectral intensity of that spectrum. For integration, the total binned intensity across each spectrum was normalized to a nominal value of 100 in the ACD/Labs integration module. Metabolites were identified and quantified using the Chenomx NMR Suite (Chenomx), which has a database of more than 250 metabolites. Identification will be verified by 2D HSQC and JRES data.
Statistical Methods for Biomarker Discovery Univariate statistical analysis was performed to address the primary objective of the study, namely to discover absence versus presence, fold-change, and change-in-CV markers that are indicative of the toxic potential of the drug tolcapone. For each univariate analysis, derived vectors of p values corresponding to the list of all analytes measured in each platform individually and combined were adjusted for multiple comparisons using the method by Benjamini and Hochberg (1995). For clarity, we use the term "marker" to refer to the initial results of a statistical analysis and the term "biomarker" to describe a marker, or marker set, that has received additional evaluation to assess its candidacy as an indicator of a biological process (Biomarkers Definition Working Group 2001).
Absence versus Presence Markers
Fold-Change Markers
Change-in-CV Markers
Platform Performance Metrics
The results presented here are intended to provide an overview of some preliminary findings from phase I of the LTBS. They include the evaluation of liver toxicity indicators, histopathology and clinical chemistry, the performance of the bioanalytical platforms (except the NMR urine analysis), and an initial overview discussion of marker findings. For a summary comparison of entacapone and tolcapone, please see the supplemental appendix to this article published electronically only at http://tpx.sagepub.com/supplemental.
Gross and Microscopic Pathology Consistent with the presence of glycogen within hepatocytes, minor hepatocellular cytoplasmic vacuolization was noted in the rat livers and was considered a result of the animals not being fasted prior to necropsy. The incidence and severity of these cytoplasmic vacuoles were similar in all treatment cohorts, indicating that they were unrelated to treatment of the rats with entacapone or tolcapone.
Plasma Clinical Chemistries Inspection of the individual box-plots for the ALT and AST measurements across all treatment cohorts in the twenty-eight-day dosing study revealed that the three highest measured values for ALT occurred in the high-dose, tolcapone-treated cohort (see Figure 3A, animal numbers 173, 83, and 75). Furthermore, in the high-dose, tolcapone-treated cohort, the same animals had the three highest AST values within that cohort (Figure 3B). Based on this intriguing observation, results for rats 173, 83, and 75 will receive attention in the analysis of the "-omics" datasets to identify further trends involving these rats that might signal low-grade hepatotoxicity associated with tolcapone exposure.
Overall Performance of Bioanalytical Profiling Platforms An understanding of the reproducibility of an analytical platform is an important basis upon which to interpret the results obtained with the platform. The primary metric used here for evaluating platform performance is the distribution of the coefficient of variation (CV) for each measured analyte derived from multiple analyses of the QC pool samples. Tables 1 and 2 present the distributions of CVs for all analytes measured in the QC samples created from the rat liver samples and rat plasma samples, respectively. A total of 32,895 analytes was measured in the liver QC samples by the five bio-analytical platforms applied to those samples (Table 1). The CVs were less than 20% for the majority of analytes measured in the liver QC samples (Table 1). A total of 678 analytes was measured in the plasma QC samples by the five bioanalytical platforms applied to those samples (Table 2). The CVs were less than 20% for the majority of analytes measured in the plasma QC samples (Table 2). For both liver and plasma analyses, the Polar LC/MS platform had the highest proportion of analytes, with CVs greater than 20%. The higher CVs obtained for analytes measured by this platform most probably reflect additional variability introduced by the chemical modification (butylation) step employed in the sample preparation prior to profiling on the LC/MS.
Preliminary Statistical Results for Comparisons of Tolcapone Effect and Entacapone Effect in Liver and Plasma from the Twenty-eight-day Dosing Study The comprehensive molecular systems analysis of liver and plasma samples combined with statistical analyses of the resulting datasets has revealed many similarities and differences between the in vivo biochemical effects of the two drugs. All bioanalytical platforms applied to either liver or plasma samples contributed marker analytes, illustrating the value of the broad-spectrum, molecular systems analysis approach to studying pharmacological or toxicological effects. For both the three-day dosing study and the twenty-eight-day dosing study, on a platform-by-platform basis, each dataset was subjected to ANOVA to determine the median fold changes and statistical significance of each pairwise comparison between the treatment cohorts. Analytes meeting a certain criterion of statistical significance in certain cohort comparisons, such as a p value cutoff, were declared to be "markers." For the twenty-eight-day dosing study with seven treatment cohorts, such an effort had the potential to yield a large number of different cohort comparisons, and markers of many different comparisons. Some of these markers would be of little or no interest in light of the overall objective of the study. We therefore focused on markers derived from just nine cohort comparisons of interest (TL-V, TM-V, TH-V, EL-V, EM-V, EH-V, TL-EL, TM-EM and TH-EH; see Abbreviations for definitions of cohort abbreviations). To manage the results of the ANOVA in a fashion consistent with the overall study objective, four major classes of marker behavior were defined as follows: tolcapone-specific; entacapone-specific; common to both drugs; and divergent between both drugs. The purpose of this marker classification approach was to consolidate separate lists of markers to elucidate similarities and differences between the biochemical pathways affected by the drug treatments. We found the process of selecting and classifying markers of the actions of tolcapone and entacapone to be considerably more challenging than anticipated during the study design stage. The following is an overview of the criteria used for marker classification and of the number of markers in each class on a platform-by-platform basis. In a strict sense, tolcapone-specific markers should be statistically significant in the tolcapone-treated to vehicle-treated (T-V) and tolcapone-treated to entacapone-treated (T-E) cohort comparisons, but not statistically significant in entacapone-treated to vehicle-treated (E-V) cohort comparisons. The number of such strict-sense, tolcapone-specific markers discovered by each bioanalytical platform for the twenty-eight-day dosing study is presented in Table 3 in the column labeled "T." In a liberal sense, a marker qualifying in the T-V but not in the E-V nor in the T-E cohort comparisons could be considered tolcapone specific, though this definition encompasses a wide range of marker behavior, from one extreme where the entacapone-treated cohorts are nearly indistinguishable from vehicle-treated cohorts (most desirable), to another extreme, where the entacapone-treated cohorts are nearly indistinguishable from tolcapone-treated cohorts (less desirable). Markers significant in T-V, E-V, and T-E cohort comparisons, where the magnitude of change in abundance of an analyte from its abundance level in the vehicle-treated cohort is greater in the tolcapone-treated cohort than in the entacapone-treated cohort, can be considered as liberal sense, tolcapone specific. Markers for the case where T-V and E-V are not significant but T-E was significant are also included in the liberal-sense, Tolcapone-specific class. The number of such liberal-sense, Tolcapone-specific markers discovered by each bioanalytical platform for the twenty-eight-day dosing study is presented in Table 3 in the column labeled "t."
Markers were classified as strict sense or liberal sense entacapone specific following logical principles identical to those described above for classifying markers as tolcapone-specific. The number of strict-sense, entacapone-specific markers discovered by each bioanalytical platform for the twenty-eight-day dosing study is presented in Table 3 in the column labeled "E," and the number of liberal-sense, entacapone-specific markers discovered by each bioanalytical platform for the twenty-eight-day dosing study is presented in Table 3 in the column labeled "e." Strict-sense Common markers encompass cases where the T-V and E-V cohort comparisons are statistically significant but the T-E comparison is not statistically significant (see Table 3 column labeled "C," for the numbers of such markers from the twenty-eight-day dosing study), and liberal-sense Common markers are those where T-V and E-V cohort comparisons are statistically significant and the T-E cohort comparison is statistically significant but the direction of change of both T and E cohorts from the V cohort is identical (see Table 3 column labeled "c" for the number of such markers from the twenty-eight-day dosing study). Divergent markers are the case where T-V and E-V cohort comparisons are statistically significant but the T-V marker changes its abundance in a different direction relative to vehicle from the E-V marker. The number of Divergent markers discovered by each bioanalytical platform for the twenty-eight-day dosing study is presented in Table 3 in the column labeled "D." For rats dosed for twenty-eight days with high (H), medium (M), and low (L) doses, emphasis in this preliminary analysis was placed on the high and medium doses. Table 3 presents a compendium of markers revealed by the ANOVA applied to the datasets generated from the application of the bioanalytical platforms to the terminal liver and plasma samples from the twenty-eight-day dosing study. The table arranges these markers into the classifications described above. Markers were revealed in all platform datasets. There were only two Divergent markers found in the twenty-eight-day dosing study, and both of these were found with the Liver Transcript platform. The number of markers in each classification depends on the p value cutoff criterion used to declare an analyte a marker for a particular comparison of cohorts. For the results shown in Table 3, different p values have been used for different platforms based on the number of analytes measured in that platform and a consideration of false discovery rates (Benjamini and Hochberg 1995), particularly for a dataset such as the gene transcript dataset that contains over 30,000 analytes. In Table 3, it is apparent that seven out of eleven platforms generated higher numbers of entacapone-specific markers than tolcapone-specific markers. One possible explanation for this result is that for each dose cohort, the dosing for entacapone on a molar basis was approximately twice the dosing for tolcapone. This molar difference between entacapone and tolcapone at the different dosing levels is a consequence of the design aspect of the study, which attempted to set the doses at equi-efficacious levels so that the pharmacological effects of each drug on its primary target, catechol-O-methyltransferase, would be equivalent. In light of this possible explanation, the greater number of tolcapone-specific markers than entacapone-specific markers in four out of eleven platforms (highlighted with a yellow background) might be considered particularly worthy of attention during the ongoing data analysis and interpretation aspects of phase I and when the results of phase I are compared to the results of phase II. The liver lipid LC/MS results are especially intriguing, given the substantial role of certain lipid species in inflammation and membrane degeneration/regeneration. The preliminary results for phase I of the LTBS contain differential marker sets composed of both liver and plasma molecules that were derived from comparisons of the effects on rats of tolcapone and entacapone in a three-day dosing study or a twenty-eight-day dosing study. The existence of such differential marker sets, under conditions where no overt toxicity is observed in tolcapone-treated rats, is a prerequisite condition for the future discovery of a liver toxicity predictive biomarker from the LTBS. Specific statistical results and candidate biomarker identities will be reported in subsequent publications.
Implementation of Phase I of the LTBS
Completion of Phase I of the LTBS and Initiation of Phase II With regard to the overall biological interpretation of the results of phase I, a variety of data integration and data mining approaches are being used at the NCTR and at BG Medicine, including ArrayTrack (Tong et al. 2003; Tong et al. 2004), Ingenuity Pathways Analysis (Ingenuity Systems, Redwood City, CA, USA), and Correlation Network Analysis (Adourian et al. 2008). One focus of this analysis is to determine whether there is evidence of mitochondrial uncoupling in the integrated molecular profiling dataset for the effects of tolcapone on rat liver, as has been suggested by the previous comparisons of the actions of entacapone and tolcapone (Haasio et al. 2001; Haasio et al. 2002). Phase I of the LTBS is just the first step on the path to testing the overall hypothesis of the study and, if possible, to discovering liver toxicity predictive biomarkers that can be employed in pre-clinical drug development studies in rats to evaluate the a drug candidate’s potential to cause liver toxicity in patients despite the absence of conventional signs of liver toxicity in those preclinical studies. Four additional compound pairs will be studied in phase II of the LTBS to provide a total of 5 differential marker sets. When comparisons are made between the analytes of these 5 differential marker sets, it will be possible to determine whether there are analytes in common that would represent hypothetical liver toxicity predictive biomarkers.
We thank Ralph Patton, Toxicologic Pathology Associates, NCTR, for conducting the clinical chemistry measurements and William M. Witt, Toxicologic Pathology Associates, NCTR, for performing the histopathological analyses. We also thank Dr. Paul Watkins for chairing the scientific advisory committee and for his comments on the manuscript and Drs. Neil Kaplowitz and John Senior for their advice and counsel. We are grateful to the following companies for financial support: Mitsubishi Chemical Holdings Corporation, Eisai Co. Ltd., Daiichi Sankyo Co. Ltd., UCB Pharma, Orion Pharma, Johnson and Johnson, Inc., and Pfizer, Inc. In addition, we thank the following companies for technology support: Applied Biosystems, Inc., Affymetrix, Inc., and TIBCO Software, Inc. The views expressed in this paper do not necessarily represent those of the U.S. Food and Drug Administration.
Adourian, A, Jennings, E, Balsubramanian, R, Hines, WM, Damian, D, Plasterer, TN, Clish, CB, Stroobant, P, McBurney, R, Verheij, ER, Bobeldijk, I, van der Greef, J, Lindberg, J, Kenne, K, Andersson, U, Hellmond, H, Nilsson, K, Salter, H, & Schuppe-Koistinen, I. (2008). Correlation network analysis for data integration and biomarker selection. Mol BioSyst, 4, 249-59[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Assal, F, Spahr, L, Hadengue, A, Rubbia-Brandt, L, & Burkhard, PR. (1998). Tolcapone and fulminant hepatitis. Lancet, 352, 958[Web of Science][Medline] [Order article via Infotrieve] Benjamini, Y, & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B, 57, 289-300 Biomarkers Definitions Working Group. (2001). Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther, 69, 89-95[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Bonifati, V, & Meco, G. (1999). New, selective catechol-O-methyltransferase inhibitors as therapeutic agents in Parkinson’s disease. Phamacol Ther, 81, 1-36 Comtan (Entacapone) Tablets NDA 20-796 (December 30, 1999). Available at: www.fda.gov/cder/foi/nda/99/20796_Comtan.htm. Davis, TL. (1998). Catechol-O-methyltransferase inhibitors in Parkinson’s disease: guidelines for effective use. CNS Drugs, 10, 239-46[CrossRef][Web of Science] Fisher, A, Croft-Baker, J, Davis, D, Purcell, P, & McLean, AJ. (2002). Entacapone-induced hepatotoxicity and hepatic dysfunction. Mov Disord, 17, 1362-65[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Fung, M, Thornton, A, Mybeck, K, Wu, JHH, Hornbuckle, K, & Muniz, E. (2001). Evaluation of the characteristics of safety withdrawal of prescription drugs from worldwide pharmaceutical markets – 1960 to 1999. Drug Inf J, 35, 293-317[Web of Science] Haasio, K, Sopanen, L, Vaalavirta, L, Linden, I-B, & Heinonen, EH. (2001). Comparative toxicological study on the hepatic safety of entacapone and tolcapone in the rat. J Neural Transm, 108, 79-91[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Haasio, K, Koponen, A, Penttila, KE, & Nissinen, E. (2002). Effects of entacapone and tolcapone on mitochondrial membrane potential. Eur J Pharmacol, 453, 21-26[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Lasser, KE, Allen, PD, Woolhandler, SJ, Himmelstein, DU, Wolfe, SM, & Bor, DH. (2002). Timing of new black box warnings and withdrawals for prescription medications. JAMA, 287, 2215-20 Kaplowitz, N. (2001). Drug-induced liver disorders: implications for drug development and regulation. Drug Saf, 24, 483-90[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Kaplowitz, N. (2005). Idiosyncratic drug hepatotoxicity. Natl Rev Drug Disc, 4, 489-99[CrossRef] Napolitano, A, Bellini, G, Borroni, E, Zurcher, G, & Bonucelli, U. (2003). Effects of peripheral and central catechol-O-methyltransferase inhibition on striatal extracellular levels of dopamine: a microdialysis study in freely moving rats. Parkinsonism Relat Disord, 9, 145-50[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Liang, K-Y, & Zeger, ST. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22 Lynch, M. (2005). An automated method for highly abundant protein partitioning of human plasma samples prior to proteomic nalysis. Am Biotechnol Lab, 23, 24-26 Olanow, CW. (2000). Tolcapone and hepatotoxic effects. Arch Neurol, 57, 263-67 Olanow, CW, & Watkins, PB. (2007). Tolcapone: an efficacy and safety review (2007). Clin Neuropharmacol, 30, 287-94[Web of Science][Medline] [Order article via Infotrieve] Ostapowicz, G, Fontana, RJ, Schiodt, FV, Larson, A, Davern, TJ, Han, SHB, McCashland, TM, Shakil, AO, Hay, JE, Hynan, L, Crippin, JS, Blei, AT, Samuel, G, Reisch, J, & Lee, WM. the U.S. Acute Liver Failure Study Group. (2002). Results of a prospective study of acute liver failure at 17 tertiary care centers in the United States. Ann Intern Med, 137, 947-54 Pitman, EJG. (1937). The closest estimates of statistical parameters. Proc Camb Philol Soc, 33, 212-22[CrossRef] Rotman, D. (2004). Can Pfizer deliver? Technol Rev. February 2004. February 2004. February 2004. Schnackenberg, LK, Dragan, YP, Reily, MD, Robertson, DG, & Beger, RD. (2007). Evaluation of NMR spectral data of urine in conjunction with measured clinical chemistry and histopathology parameters to assess the effects of liver and kidney toxicants. Metabolomics, 3, 87-100[CrossRef][Web of Science] Shuster, D, Laggner, C, & Langer, T. (2005). Why drugs fail—a study on side effects in new chemical entities. Curr Pharma Des, 11, 3545-59[CrossRef] Tasmar (Tolcapone) Tablets NDA 20-697 (January 29, 1998). Available at http://www.fda.gov/cder/foi/nda/98/20697_Tasmar.htm. Temple, RJ, & Himmel, MH. (2002). Safety of newly approved drugs. JAMA, 287, 2273-75 Tong, W, Cao, X, Harris, S, Sun, H, Fuscoe, J, Harris, A, Hong, H, Xie, Q, Perkins, R, Shi, L, & Casciano, D. (2003). ArrayTrack: Supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research. Environ Health Perspect, 111, 1819-26[Web of Science][Medline] [Order article via Infotrieve] Tong, W, Harris, S, Cao, X, Fang, H, Shi, L, Sun, H, Fuscoe, J, Hong, H, Xie, Q, Perkins, R, & Casciano, D. (2004). Development of a Public Toxicogenomics Software for Microarray Data Management and Analysis. Mutat Res, 549, 241-53[Web of Science][Medline] [Order article via Infotrieve] Tornwall, M, & Mannisto, PT. (1993). Effects of three types of catechol O-methylation inhibitors on L-3,4,-dihydroxyphenylalanine-induced circling behaviour in rats. Eur J Phamacol, 250, 77-84[CrossRef][Web of Science][Medline] [Order article via Infotrieve] van der Greef, J, Martin, S, Juhasz, P, Adourian, A, Plasterer, T, Verheij, ER, & McBurney, RN. (2007). The Art and Practice of Systems Biology in Medicine: Mapping Patterns of Relationships. J Proteome Res, 4, 1540-59 Watkins, P. (2000). COMT inhibitors and liver toxicity. Neurology, 55(11Suppl 4), S51-52[CrossRef][Web of Science][Medline] [Order article via Infotrieve] Zimmerman, H. (1999). Hepatotoxicity: The Adverse Effects of Drugs and Other Chemicals on the Liver. Philadelphia, PA: Lippincott Williams and Wilkins
This version was published on January
1, 2009 Toxicologic Pathology, Vol. 37, No. 1,
52-64 (2009)
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 |
|
|
 |
|
Sign In to gain access to subscriptions and/or personal tools.
Top
Abstract
Introduction
-glutamyl transferase. 
