MALDI imaging mass spectrometry: an emerging tool in neurology.

Neurological disease and disorders remain a large public health threat. Thus, research to improve early detection and/or develop more effective treatment approaches are necessary. Although there are many common techniques and imaging modalities utilized to study these diseases, existing approaches often require a label which can be costly and time consuming. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a label-free, innovative and emerging technique that produces 2D ion density maps representing the distribution of an analyte(s) across a tissue section in relation to tissue histopathology. One main advantage of MALDI IMS over other imaging modalities is its ability to determine the spatial distribution of hundreds of analytes within a single imaging run, without the need for a label or any a priori knowledge. Within the field of neurology and disease there have been several impactful studies in which MALDI IMS has been utilized to better understand the cellular pathology of the disease and or severity. Furthermore, MALDI IMS has made it possible to map specific classes of analytes to regions of the brain that otherwise may have been lost using more traditional methods. This review will highlight key studies that demonstrate the potential of this technology to elucidate previously unknown phenomenon in neurological disease.

An Integrated Analysis of Metabolites, Peptides, and Inflammation Biomarkers for Assessment of Preanalytical Variability of Human Plasma.

Discrepancies in blood sample collection and processing could have a significant impact on levels of metabolites, peptides, and protein biomarkers of inflammation in the blood; thus, sample quality control is critical for successful biomarker identification and validation. In this study, we analyzed the effects of several preanalytical processing conditions, including different storage times and temperatures for blood or plasma samples and different centrifugation forces on the levels of metabolites, peptides, and inflammation biomarkers in human plasma samples using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. Temperature was found to be the major factor for metabolite variation, and both time and temperature were identified as major factors for peptide variation. For inflammation biomarkers, temperature played different roles depending on the sample type (blood or plasma). Low temperature affected inflammation biomarkers in blood, while room temperature impacted inflammation biomarkers in plasma.

Stability of the Human Plasma Proteome to Pre-analytical Variability as Assessed by an Aptamer-Based Approach.

Variable processing and storage of whole blood and/or plasma are potential confounders in biomarker development and clinical assays. The goal of the study was to investigate how pre-analytical variables impact the human plasma proteome. Whole blood obtained from 16 apparently healthy individuals was collected in six EDTA tubes and processed randomly under six pre-analytical variable conditions including blood storage at 0 °C or RT for 6 h (B6h0C or B6hRT) before processing to plasma, plasma storage at 4 °C or RT for 24 h (P24h4C or P24hRT), low centrifugal force at 1300 × g, (Low×g), and immediate processing to plasma under 2500 × g (control) followed by plasma storage at -80 °C. An aptamer-based proteomic assay was performed to identify significantly changed proteins (fold change ≥1.2, P < 0.05, and false discovery rate < 0.05) relative to the control from a total of 1305 proteins assayed. Pre-analytical conditions Low×g and B6h0C resulted in the most plasma proteome changes with 200 and 148 proteins significantly changed, respectively. Only 36 proteins were changed under B6hRT. Conditions P24h4C and P24hRT yielded changes of 28 and 75 proteins, respectively. The complement system was activated in vitro under the conditions B6hRT, P24h4C, and P24hRT. The results suggest that particular pre-analytical variables should be controlled for clinical measurement of specific biomarkers.

Early metabolomics changes in heart and plasma during chronic doxorubicin treatment in B6C3F1 mice.

The present study aimed to identify molecular markers of early stages of cardiotoxicity induced by a potent chemotherapeutic agent, doxorubicin (DOX). Male B6C3F1 mice were dosed with 3 mg kg(-1) DOX or saline via tail vein weekly for 2, 3, 4, 6 or 8 weeks (cumulative DOX doses of 6, 9, 12, 18 or 24 mg kg(-1) , respectively) and euthanized a week after the last dose. Mass spectrometry-based and nuclear magnetic resonance spectrometry-based metabolic profiling were employed to identify initial biomarkers of cardiotoxicity before myocardial injury and cardiac pathology, which were not noted until after the 18 and 24 mg kg(-1) cumulative doses, respectively. After a cumulative dose of 6 mg kg(-1) , 18 amino acids and four biogenic amines (acetylornithine, kynurenine, putrescine and serotonin) were significantly increased in cardiac tissue; 16 amino acids and two biogenic amines (acetylornithine and hydroxyproline) were significantly altered in plasma. In addition, 16 acylcarnitines were significantly increased in plasma and five were significantly decreased in cardiac tissue compared to saline-treated controls. Plasma lactate and succinate, involved in the Krebs cycle, were significantly altered after a cumulative dose of 6 mg kg(-1) . A few metabolites remained altered at higher cumulative DOX doses, which could partly indicate a transition from injury processes at 2 weeks to repair processes with additional injury happening concurrently before myocardial injury at 8 weeks. These altered metabolic profiles in mouse heart and plasma during the initial stages of injury progression due to DOX treatment may suggest these metabolites as candidate early biomarkers of cardiotoxicity. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Translational biomarkers of acetaminophen-induced acute liver injury.

Acetaminophen (APAP) is a commonly used analgesic drug that can cause liver injury, liver necrosis and liver failure. APAP-induced liver injury is associated with glutathione depletion, the formation of APAP protein adducts, the generation of reactive oxygen and nitrogen species and mitochondrial injury. The systems biology omics technologies (transcriptomics, proteomics and metabolomics) have been used to discover potential translational biomarkers of liver injury. The following review provides a summary of the systems biology discovery process, analytical validation of biomarkers and translation of omics biomarkers from the nonclinical to clinical setting in APAP-induced liver injury.

¹³C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.

An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

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.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Co-Culture of Human Primary Hepatocytes and Nonparenchymal Liver Cells in the Emulate® Liver-Chip for the Study of Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a significant public health issue, but standard animal tests and clinical trials sometimes fail to predict DILI due to species differences and the relatively low number of human subjects involved in preapproval studies of a new drug, respectively. In vitro models have long been used to aid DILI prediction, with primary human hepatocytes (PHHs) being generally considered the gold standard. However, despite many efforts and decades of work, traditional culture methods have been unsuccessful in either fully preserving essential liver functions after isolation of PHHs or in emulating interactions between PHHs and hepatic nonparenchymal cells (NPCs), both of which are essential for the development of DILI under in vivo conditions. Recently, various liver-on-a-chip (Liver-Chip) systems have been developed to co-culture hepatocytes and NPCs in a three-dimensional environment on microfluidic channels, enabling better maintenance of primary liver cells and thus improved DILI prediction. The Emulate® Liver-Chip is a commercially available system that can recapitulate some in vivo DILI responses associated with certain compounds whose liver safety profile cannot be accurately evaluated using conventional approaches involving PHHs or animal models due to a lack of innate immune responses or species-dependent toxicity, respectively. Here, we describe detailed procedures for the use of Emulate® Liver-Chips for co-culturing PHHs and NPCs for the purpose of DILI evaluation. First, we describe the procedures for preparing the Liver-Chip. We then outline the steps needed for sequential seeding of PHHs and NPCs in the prepared Liver-Chips. Lastly, we provide a protocol for utilizing cells maintained in perfusion culture in the Liver-Chips to evaluate DILI, using acetaminophen as an example. In all, use of this system and the procedures described here allow better preservation of the functions of human primary liver cells, resulting in an improved in vitro model for DILI assessment. © 2022 Wiley Periodicals LLC. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Basic Protocol 1: Liver-Chip preparation Basic Protocol 2: Seeding primary human hepatocytes and nonparenchymal cells on Liver-Chips Basic Protocol 3: Perfusion culture for the study of acetaminophen-induced liver injury.

Modification of norfloxacin by a Microbacterium sp. strain isolated from a wastewater treatment plant.

Antimicrobial residues found in municipal wastewater may increase selective pressure on microorganisms for development of resistance, but studies with mixed microbial cultures derived from wastewater have suggested that some bacteria are able to inactivate fluoroquinolones. Medium containing N-phenylpiperazine and inoculated with wastewater was used to enrich fluoroquinolone-modifying bacteria. One bacterial strain isolated from an enrichment culture was identified by 16S rRNA gene sequence analysis as a Microbacterium sp. similar to a plant growth-promoting bacterium, Microbacterium azadirachtae (99.70%), and a nematode pathogen, "M. nematophilum" (99.02%). During growth in medium with norfloxacin, this strain produced four metabolites, which were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) analyses as 8-hydroxynorfloxacin, 6-defluoro-6-hydroxynorfloxacin, desethylene norfloxacin, and N-acetylnorfloxacin. The production of the first three metabolites was enhanced by ascorbic acid and nitrate, but it was inhibited by phosphate, amino acids, mannitol, formate, and thiourea. In contrast, N-acetylnorfloxacin was most abundant in cultures supplemented with amino acids. This is the first report of defluorination and hydroxylation of a fluoroquinolone by an isolated bacterial strain. The results suggest that some bacteria may degrade fluoroquinolones in wastewater to metabolites with less antibacterial activity that could be subject to further degradation by other microorganisms.

Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity.

Hepatotoxicity and nephrotoxicity are two major reasons that drugs are withdrawn post-market, and hence it is of major concern to both the FDA and pharmaceutical companies. The number of cases of serious adverse effects (SAEs) in marketed drugs has climbed faster than the number of total drug prescriptions issued. In some cases, preclinical animal studies fail to identify the potential toxicity of a new chemical entity (NCE) under development. The current clinical chemistry biomarkers of liver and kidney injury are inadequate in terms of sensitivity and/or specificity, prompting the need to discover new translational specific biomarkers of organ injury. Metabolomics along with genomics and proteomics technologies have the capability of providing translational diagnostic and prognostic biomarkers specific for early stages of liver and kidney injury. Metabolomics has several advantages over the other omics platforms such as ease of sample preparation, data acquisition and use of biofluids collected through minimally invasive procedures in preclinical and clinical studies. The metabolomics platform is reviewed with particular emphasis on applications involving drug-induced hepatotoxicity and nephrotoxicity. Analytical platforms for metabolomics, chemometrics for mining metabolomics data and the applications of the metabolomics technologies are covered in detail with emphasis on recent work in the field.

Evaluations of the trans-sulfuration pathway in multiple liver toxicity studies.

Drug-induced liver injury has been associated with the generation of reactive metabolites, which are primarily detoxified via glutathione conjugation. In this study, it was hypothesized that molecules involved in the synthesis of glutathione would be diminished to replenish the glutathione depleted through conjugation reactions. Since S-adenosylmethionine (SAMe) is the primary source of the sulfur atom in glutathione, UPLC/MS and NMR were used to evaluate metabolites involved with the transulfuration pathway in urine samples collected during studies of eight liver toxic compounds in Sprague-Dawley rats. Urinary levels of creatine were increased on day 1 or day 2 in 8 high dose liver toxicity studies. Taurine concentration in urine was increased in only 3 of 8 liver toxicity studies while SAMe was found to be reduced in 4 of 5 liver toxicity studies. To further validate the results from the metabonomic studies, microarray data from rat liver samples following treatment with acetaminophen was obtained from the Gene Expression Omnibus (GEO) database. Some genes involved in the trans-sulfuration pathway, including guanidinoacetate N-methyltransferase, glycine N-methyltransferase, betaine-homocysteine methyltransferase and cysteine dioxygenase were found to be significantly decreased while methionine adenosyl transferase II, alpha increased at 24 h post-dosing, which is consistent with the SAMe and creatine findings. The metabolic and transcriptomic results show that the trans-sulfuration pathway from SAMe to glutathione was disturbed due to the administration of heptatotoxicants.

Delineating liver events in trichloroethylene-induced autoimmune hepatitis.

Exposure to the environmental pollutant trichloroethylene (TCE) has been linked to autoimmune disease development in humans. Chronic (32-week) low-level exposure to TCE has been shown to promote autoimmune hepatitis in association with CD4(+) T cell activation in autoimmune-prone MRL+/+ mice. MRL+/+ mice are usually thought of as a model of systemic lupus rather than an organ-specific disease such as autoimmune hepatitis. Consequently, the present study examined gene expression and metabolites to delineate the liver events that skewed the autoimmune response toward that organ in TCE-treated mice. Female MRL+/+ mice were treated with 0.5 mg/mL TCE in their drinking water. The results showed that TCE-induced autoimmune hepatitis could be detected in as little as 26 weeks. TCE exposure also generated a time-dependent increase in the number of antibodies specific for liver proteins. The gene expression correlated with the metabolite analysis to show that TCE upregulated the methionine/homocysteine pathway in the liver after 26 weeks of exposure. The results also showed that TCE exposure altered the expression of selective hepatic genes associated with immunity and inflammation. On the basis of these results, future mechanistic studies will focus on how alterations in genes associated with immunity and inflammation, in conjunction with protein alterations in the liver, promote liver immunogenicity in TCE-treated MRL+/+ mice.

The liver toxicity biomarker study: phase I design and preliminary results.

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.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite.

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material®s (SRM®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Modeling and assaying dioxin-like biological effects for both dioxin-like and certain non-dioxin-like compounds.

13C NMR data have been correlated to Toxic Equivalency Factors (TEFs) of the 29 PCDDs, PCDFs, or PCBs for which non-zero TEFs have been defined. Such correlations are called quantitative spectrometric data-activity relationship (QSDAR) models. An improved QSDAR model predicted TEFs of 0.037 and 0.004, respectively, for 1,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and 1,2,3,4,7-pentachlorodibenzo-p-dioxin (PeCDD), both among the 390 congeners for which zero value TEFs are assumed. A QSDAR model of Relative Potency (REP) values estimated the corresponding values as 0.115 and 0.020. Results from both models indicated that these two congeners may exhibit significant dioxin-like toxicity. If other such congeners have non-zero toxicity, TEF-based risk assessments of some dioxin-, furan-, or PCB-contaminated sites or foods may underestimate toxicity. Both models were extensively cross-validated and the TEF model was externally validated. We confirmed the predictions by an independent in vitro method, a luciferase gene expression assay based on mouse liver cells that found REPs of 0.027 and 0.013, respectively, for 1,3,7,8-TCDD and 1,2,3,4,7-PeCDD. The QSDAR-estimated and gene-expression assayed values agreed. The models were used to predict activity for an applicability domain including 108 non-2,3,7,8 dioxin, furan, or PCB congeners and 2,3,7,8-tetrachlorophenothiazine, a dioxin analog proposed as a drug candidate. This study showed that QSDAR prediction followed by a relatively inexpensive in vitro assay could be used to nominate a few candidates among hundreds for further investigation. It suggested that in silico and in vitro nomination protocols may facilitate practical risk assessment when chemical family members exhibit different degrees of toxicity operating via a common mechanism.

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS.

Urinary metabolic perturbations associated with acute and chronic acetaminophen-induced hepatotoxicity were investigated using nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography/mass spectrometry (UPLC/MS) metabonomics approaches to determine biomarkers of hepatotoxicity. Acute and chronic doses of acetaminophen (APAP) were administered to male Sprague-Dawley rats. NMR and UPLC/MS were able to detect both drug metabolites and endogenous metabolites simultaneously. The principal component analysis (PCA) of NMR or UPLC/MS spectra showed that metabolic changes observed in both acute and chronic dosing of acetaminophen were similar. Histopathology and clinical chemistry studies were performed and correlated well with the PCA analysis and magnitude of metabolite changes. Depletion of antioxidants (e.g. ferulic acid), trigonelline, S-adenosyl-L-methionine, and energy-related metabolites indicated that oxidative stress was caused by acute and chronic acetaminophen administration. Similar patterns of metabolic changes in response to acute or chronic dosing suggest similar detoxification and recovery mechanisms following APAP administration.

The role of metabolic biomarkers in drug toxicity studies.

ABSTRACT Metabolic profiling is a technique that can potentially provide more sensitive and specific biomarkers of toxicity than the current clinical measures benefiting preclinical and clinical drug studies. Both nuclear magnetic resonance (NMR) and mass spectrometry (MS) platforms have been used for metabolic profiling studies of drug toxicity. Not only can both techniques provide novel biomarker(s) of toxicity but the combination of both techniques gives a broader range of metabolites evaluated. Changes in metabolic patterns can provide insight into mechanism(s) of toxicity and help to eliminate a potentially toxic new chemical entity earlier in the developmental process. Metabolic profiling offers numerous advantages in toxicological research and screening as sample collection and preparation are relatively simple. Further, sample throughput, reproducibility, and accuracy are high. The area of drug toxicity of therapeutic compounds has already been impacted by metabolic profiling studies and will continue to be impacted as new, more specific biomarker(s) are found. In order for a biomarker or pattern of biomarkers to be accepted, it must be shown that they originate from the target tissue of interest. Metabolic profiling studies are amenable to any biofluid or tissue sample making it possible to link the changes noted in urine for instance as originating from renal injury. Additionally, the ease of sample collection makes it possible to follow a single animal or subject over time in order to determine whether and when the toxicity resolves itself. This review focuses on the advantages of metabolic profiling for drug toxicity studies.

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis.

BACKGROUND: Urine from male Sprague-Dawley rats 25, 40, and 80 days old was analyzed by NMR and UPLC/MS. The effects of data normalization procedures on principal component analysis (PCA) and quantitative analysis of NMR-based metabonomics data were investigated. Additionally, the effects of age on the metabolic profiles were examined by both NMR and UPLC/MS analyses. RESULTS: The data normalization factor was shown to have a great impact on the statistical and quantitative results indicating the need to carefully consider how to best normalize the data within a particular study and when comparing different studies. PCA applied to the data obtained from both NMR and UPLC/MS platforms reveals similar age-related differences. NMR indicated many metabolites associated with the Krebs cycle decrease while citrate and 2-oxoglutarate, also associated with the Krebs cycle, increase in older rats. CONCLUSION: This study compared four different normalization methods for the NMR-based metabonomics spectra from an age-related study. It was shown that each method of normalization has a great effect on both the statistical and quantitative analyses. Each normalization method resulted in altered relative positions of significant PCA loadings for each sample spectra but it did not alter which chemical shifts had the highest loadings. The greater the normalization factor was related to age, the greater the separation between age groups was observed in subsequent PCA analyses. The normalization factor that showed the least age dependence was total NMR intensity, which was consistent with UPLC/MS data. Normalization by total intensity attempts to make corrections due to dietary and water intake of the individual animal, which is especially useful in metabonomics evaluations of urine. Additionally, metabonomics evaluations of age-related effects showed decreased concentrations of many Krebs cycle intermediates along with increased levels of oxidized antioxidants in urine of older rats, which is consistent with current theories on aging and its association with diminishing mitochondrial function and increasing levels of reactive oxygen species. Analysis of urine by both NMR and UPLC/MS provides a comprehensive and complementary means of examining metabolic events in aging rats.