Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease.

The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary-gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men.

Multitargeted Internal Calibration for the Quantification of Chronic Kidney Disease-Related Endogenous Metabolites Using Liquid Chromatography-Mass Spectrometry.

Accurate quantitative analysis in liquid chromatography-mass spectrometry (LC-MS) benefits from calibration curves generated in the same matrix as the study sample. In the case of endogenous compound quantification, as no blank matrix exists, the multitargeted internal calibration (MTIC) is an attractive and straightforward approach to avoid the need for extensive matrix similarity evaluation. Its principle is to take advantage of stable isotope labeled (SIL) standards as internal calibrants to simultaneously quantify authentic analytes using a within sample calibration. An MTIC workflow was developed for the simultaneous quantification of metabolites related to chronic kidney disease (CKD) using a volumetric microsampling device to collect 20 µL of serum or plasma, followed by a single-step extraction with acetonitrile/water and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Since a single concentration of internal calibrant is necessary to calculate the study sample concentration, the instrument response function was investigated to determine the best SIL concentration. After validation, the trueness of 16 endogenous analytes in authentic human serum ranged from 72.2 to 116.0%, the repeatability from 1.9 to 11.3%, and the intermediate precision ranged overall from 2.1 to 15.4%. The proposed approach was applied to plasma samples collected from healthy control participants and two patient groups diagnosed with CKD. Results confirmed substantial concentration differences between groups for several analytes, including indoxyl sulfate and cortisone, as well as metabolite enrichment in the kynurenine and indole pathways. Multitargeted methodologies represent a major step toward rapid and straightforward LC-MS/MS absolute quantification of endogenous biomarkers, which could change the paradigm of MS use in clinical laboratories.

Straightforward quantification of endogenous steroids with liquid chromatography-tandem mass spectrometry: Comparing calibration approaches.

Different calibration strategies are used in liquid chromatography hyphenated to mass spectrometry (LC-MS) bioanalysis. Currently, the surrogate matrix and surrogate analyte represent the most widely used approaches to compensate for the lack of analyte-free matrices in endogenous compounds quantification. In this context, there is a growing interest in rationalizing and simplifying quantitative analysis using a one-point concentration level of stable isotope-labeled (SIL) standards as surrogate calibrants. Accordingly, an internal calibration (IC) can be applied when the instrument response is translated into analyte concentration via the analyte-to-SIL ratio performed directly in the study sample. Since SILs are generally used as internal standards to normalize variability between authentic study sample matrix and surrogate matrix used for the calibration, IC can be calculated even if the calibration protocol was achieved for an external calibration (EC). In this study, a complete dataset of a published and fully validated method to quantify an extended steroid profile in serum was recomputed by adapting the role of SIL internal standards as surrogate calibrants. Using the validation samples, the quantitative performances for IC were comparable with the original method, showing acceptable trueness (79%-115%) and precision (0.8%-11.8%) for the 21 detected steroids. The IC methodology was then applied to human serum samples (n = 51) from healthy women and women diagnosed with mild hyperandrogenism, showing high agreement (R2 > 0.98) with the concentrations obtained using the conventional quantification based on EC. For IC, Passing-Bablok regression showed proportional biases between -15.0% and 11.3% for all quantified steroids, with an average difference of -5.8% compared to EC. These results highlight the reliability and the advantages of implementing IC in clinical laboratories routine to simplify quantification in LC-MS bioanalysis, especially when a large panel of analytes is monitored.

Low-polarity untargeted metabolomic profiling as a tool to gain insight into seminal fluid.

INTRODUCTION: A decrease in sperm cell count has been observed along the last several decades, especially in the most developed regions of the world. The use of metabolomics to study the composition of the seminal fluid is a promising approach to gain access to the molecular mechanisms underlying this fact. OBJECTIVES: In the present work, we aimed at relating metabolomic profiles of young healthy men to their semen quality parameters obtained from conventional microscopic analysis. METHODS: An untargeted metabolomics approach focusing on low- to mid-polarity compounds was used to analyze a subset of seminal fluid samples from a cohort of over 2700 young healthy men. RESULTS: Our results show that a broad metabolic profiling comprising several families of compounds (including acyl-carnitines, steroids, and other lipids) can contribute to effectively distinguish samples provided by individuals exhibiting low or high absolute sperm counts. CONCLUSION: A number of metabolites involved in sexual development and function, signaling, and energy metabolism were highlighted as being distinctive of samples coming from either group, proving untargeted metabolomics as a promising tool to better understand the pathophysiological processes responsible for male fertility impairment.

A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood.

BACKGROUND: Most current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. RESULTS: Integrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. SIGNIFICANCE AND NOVELTY: The detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares.

BACKGROUND: Adequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS. RESULTS: A versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors. SIGNIFICANCE AND NOVELTY: The rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.

Extended steroid profiling in H295R cells provides deeper insight into chemical-induced disturbances of steroidogenesis: Exemplified by prochloraz and anabolic steroids.

Human adrenocortical H295R cells have been validated by the OECD Test Guideline 456 to detect chemicals disrupting testosterone and 17ß-estradiol (estradiol) biosynthesis. This study evaluated a novel approach to detect disturbances of steroidogenesis in H295R cells, exemplified by prochloraz and five anabolic steroids. Steroid profiles were assessed by an untargeted LC-MS-based method, providing a relative quantification of 57 steroids annotated according to their accurate masses and retention times. Such a panel of steroids included several mineralocorticoids, glucocorticoids, progestins and adrenal androgens. The coverage of a high number of metabolites in this extended steroid profiling facilitated grouping of chemicals with similar effects and detecting subtler differences between chemicals. It allowed, for example, distinguishing between the effects of turinabol and oxymetholone, supposed to act similarly in a previous characterization including only nine adrenal steroids. Furthermore, the results revealed that product/substrate ratios can provide superior information on altered enzyme activities compared to individual metabolite levels. For example, the 17α-hydroxypregnenolone/pregnenolone ratio was found to be a more sensitive marker for detecting 17α-hydroxylase inhibition by prochloraz than the corresponding individual steroids. These results illustrate that chemical grouping and calculation of product/substrate ratios can provide valuable information on mode-of-action and help prioritizing further experimental work.

From fundamentals in calibration to modern methodologies: A tutorial for small molecules quantification in liquid chromatography-mass spectrometry bioanalysis.

Over the last two decades, liquid chromatography coupled to mass-spectrometry (LC‒MS) has become the gold standard to perform qualitative and quantitative analyses of small molecules. When quantitative analysis is developed, an analyst usually refers to international guidelines for analytical method validation. In this context, the design of calibration curves plays a key role in providing accurate results. During recent years and along with instrumental advances, strategies to build calibration curves have dramatically evolved, introducing innovative approaches to improve quantitative precision and throughput. For example, when a labeled standard is available to be spiked directly into the study sample, the concentration of the unlabeled analog can be easily determined using the isotopic pattern deconvolution or the internal calibration approach, eliminating the need for multipoint calibration curves. This tutorial aims to synthetize the advances in LC‒MS quantitative analysis for small molecules in complex matrices, going from fundamental aspects in calibration to modern methodologies and applications. Different work schemes for calibration depending on the sample characteristics (analyte and matrix nature) are distinguished and discussed. Finally, this tutorial outlines the importance of having international guidelines for analytical method validation that agree with the advances in calibration strategies and analytical instrumentation.

Digoxin Induces Human Astrocyte Reaction In Vitro.

Astrocyte reaction is a complex cellular process involving astrocytes in response to various types of CNS injury and a marker of neurotoxicity. It has been abundantly studied in rodents but relatively poorly in human cells due to limited access to the brain. Astrocytes play important roles in cerebral energy metabolism and are also key players in neuroinflammation. Astroglial metabolic and inflammatory changes have been reported with age, leading to the hypothesis that mitochondrial metabolism and inflammatory responses are interconnected. However, the relationship between energy metabolism and astrocyte reactivity in the context of neurotoxicity is not known. We hypothesized that changes in energy metabolism of astrocytes will be coupled to their activation by xenobiotics. Astrocyte reaction and associated energy metabolic changes were assessed by immunostaining, gene expression, proteomics, metabolomics, and extracellular flux analyses after 24 h of exposure of human ReN-derived astrocytes to digoxin (1-10 µM) or TNFα (30 ng/ml) used as a positive control. Strong astrocytic reaction was observed, accompanied by increased glycolysis at low concentrations of digoxin (0.1 and 0.5 µM) and after TNFα exposure, suggesting that increased glycolysis may be a common feature of reactive astrocytes, independent of the triggering molecule. In conclusion, whether astrocyte activation is triggered by cytokines or a xenobiotic, it is strongly tied to energy metabolism in human ReN-derived astrocytes. Increased glycolysis might be considered as an endpoint to detect astrocyte activation by potentially neurotoxic compounds in vitro. Finally, ReN-derived astrocytes may help to decipher mechanisms of neurotoxicity in ascertaining the ability of chemicals to directly target astrocytes.

Evaluation of Prototype CE-MS Interfaces.

Capillary electrophoresis-mass spectrometry (CE-MS) coupling is a powerful analytical solution bringing together the separation power of CE and the wealth of chemical information afforded by MS. Nevertheless, interfaces making the hyphenation of both techniques possible have always been the subject of a quest for improvement by their users in search for more sensitive and robust setups. This fact has led to numerous technical developments and new interface designs claiming to outrival existing approaches in different aspects. Nevertheless, the task of evaluating and comparing a new interface to previous solutions is not always straightforward. Issued from our own experience in the field, we herein propose a protocol to optimize the operation parameters of a new CE-MS interface design, assess its analytical performance, and compare it to a reference interface if desired. Electrospray stability, sensitivity, reproducibility, and robustness are practically evaluated as key elements of the process.

Challenges in ESI-MS-based Untargeted Metabolomics.

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. In this article, we aim to provide a short overview of the liquid-phase separation methods hyphenated to MS to perform untargeted metabolomics of biological samples. Each approach is complemented by up-to-date literature to guide readers, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls. This article covers mainly data acquisition, but a short discussion is provided regarding signal processing and data treatment, as well as data analysis and its biological interpretation in the context of metabolomic studies.

Longitudinal evaluation of multiple biomarkers for the detection of testosterone gel administration in women with normal menstrual cycle.

In women, hormonal fluctuations related to the menstrual cycle may impose a great source of variability for some biomarkers of testosterone (T) administration, which can ultimately disrupt the sensitivity of their longitudinal monitoring. In this study, the sensitivity of the current urinary and haematological markers of the Athlete Biological Passport (ABP), as well as serum steroid biomarkers, was investigated for the monitoring of a 28-day T gel treatment combined with endogenous fluctuation of the menstrual cycle in 14 healthy female subjects. Additionally, the analysis of urinary target compounds was performed on a subset of samples for endogenous/exogenous origin via isotope ratio mass spectrometry (IRMS). In serum, concentrations of T and dihydrotestosterone (DHT) increased significantly during the treatment, whereas in urine matrix the most affected biomarkers were found to be the ratios of testosterone/epitestosterone (T/E) and 5α-androstane-3α,17ß-diol/epitestosterone (5αAdiol/E). The detection capability of both urinary biomarkers was heavily influenced by [E], which fluctuated depending on the menstrual cycle, and resulted in low sensitivity of the urinary steroidal ABP module. On the contrary, an alternative approach by the longitudinal monitoring of serum T and DHT concentrations with the newly proposed T/androstenedione ratio showed higher sensitivity. The confirmatory IRMS results demonstrated that less than one third of the tested urine samples fulfilled the criteria for positivity. Results from this study demonstrated that the 'blood steroid profile' represents a powerful complementary approach to the 'urinary module' and underlines the importance of gathering bundle of evidence to support the scenario of an endogenous prohibited substance administration.

An integrated metabolomics and proteogenomics approach reveals molecular alterations following carbamazepine exposure in the male mussel Mytilus galloprovincialis.

Carbamazepine is one of the most abundant pharmaceutical active compounds detected in aquatic systems. Based on laboratory exposures, carbamazepine has been proven to adversely affect aquatic organisms. However, the underlying molecular events remain poorly understood. This study aims to investigate the molecular mechanisms potentially associated with toxicological effects of carbamazepine on the mussel Mytilus galloprovincialis exposed for 3 days at realistic concentrations encountered in coastal environments (80 ng/L and 8 µg/L). An integrated metabolomics and proteogenomics approach, including data fusion strategy, was applied to gain more insight in molecular events and cellular processes triggered by carbamazepine exposure. Consistent metabolic and protein signatures revealed a metabolic rewiring and cellular stress at both concentrations (e.g. intensification of protein synthesis, transport and catabolism processes, disruption of lipid and amino acid metabolisms). These highlighted molecular signatures point to the induction of autophagy, closely related with carbamazepine mechanism of action, as well as a destabilization of the lysosomal membranes and an enzymatic overactivity of the peroxisomes. Induction of programmed cell death was highlighted by the modulation of apoptotic cognate proteins. The proposed integrative omics data analysis was shown to be highly relevant to identify the modulations of the two molecular levels, i.e. metabolites and proteins. Multi-omics approach is able to explain the resulting complex biological system, and document stronger toxicological pieces of evidence on pharmaceutical active compounds at environmental concentrations in sentinel organisms.

Steroid profiling by UHPLC-MS/MS in dried blood spots collected from healthy women with and without testosterone gel administration.

The quantification of a large panel of endogenous steroids in serum by LC-MS/MS represents a powerful clinical tool for the screening or diagnosis of diverse endocrine disorders. This approach has also demonstrated excellent sensitivity for the detection of testosterone misuse in the anti-doping field, especially in female athlete population. In both situations, the use of dried blood spots (DBS) could provide a viable alternative to invasive venous blood collection. Here, the evaluation of DBS sampling for the quantification of a panel of endogenous steroids using UHPLC-MS/MS is described. The UHPLC-MS/MS method was validated for quantitative analysis of eleven free and eight conjugated steroids and was then used for the analysis of DBS samples collected in 14 healthy women during a normal menstrual cycle (control phase) followed by a 28-days testosterone gel treatment (treatment phase). Results were compared with those obtained from serum matrix. Satisfactory performance was obtained for all compounds in terms of selectivity, linearity, accuracy, precision, combined uncertainty, stability as well as extraction recovery and matrix effects. In control phase, high correlation was observed between DBS and serum concentrations for most compounds. In treatment phase, higher testosterone concentrations were observed in capillary than in venous DBS, suggesting a possible interference resulting from testosterone contamination on finger(s) used for gel application. Steroid profiling in capillary DBS represents a simple and efficient strategy for monitoring endogenous steroid concentrations and their fluctuation in clinical context of steroid-related disorders, or for the detection of testosterone abuse in anti-doping.

Identification of a predictive metabolic signature of response to immune checkpoint inhibitors in non-small cell lung cancer: METABO-ICI clinical study protocol.

BACKGROUND: Immune checkpoints inhibitors (ICI) are becoming new standards of care for the treatment of non-small cell lung cancer (NSCLC), both as first (alone or in association with chemotherapy) and second line. However, no powerful predictive biomarker of therapeutic response to ICI has been found to date. It has been recently shown that microbiota composition could influence the ability of patients to respond to ICI. Indeed, the microbiota produces circulating metabolites that will subsequently act on immune system, the investigators hypothesized that plasma metabolic signature, reflecting a global microbiota function, could represent a predictive biomarker of response to ICI. METHODS: Monocentric prospective study. Primary objective is to identify baseline metabolic signature (metabolomics analysis by mass spectrometry) associated to ICI response. Secondary objectives are to link metabolic signature with microbiota composition (metagenomics analysis RNA 16S) and immune profile, and altogether with clinic response to ICI. The study will include 60 NSCLC patients treated by ICI in 1st, 2nd or 3rd line of treatment at the Grenoble Alpes University hospital (CHUGA) in 18 months. Patients that have received antibiotic or steroid treatment, 2 or 4 weeks before ICI initiation, respectively, will be excluded. Blood and feces will be collected prior to, at 2 months after ICI treatment initiation, and at 6 months or at progression. EXPECTED RESULTS: We expect to highlight a metabolic profile predictive of response to ICI. By identifying factors associated with early progression, we could avoid to treat potential non-responding patients. Moreover, by restoring a favorable microbiota, patients' ability to respond to these treatments might be restored.

Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model.

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.

Neuroinflammatory Response to TNFα and IL1ß Cytokines Is Accompanied by an Increase in Glycolysis in Human Astrocytes In Vitro.

Astrogliosis has been abundantly studied in rodents but relatively poorly in human cells due to limited access to the brain. Astrocytes play important roles in cerebral energy metabolism, and are also key players in neuroinflammation. Astroglial metabolic and inflammatory changes as a function of age have been reported, leading to the hypothesis that mitochondrial metabolism and inflammatory responses are interconnected in supporting a functional switch of astrocytes from neurotrophic to neurotoxic. This study aimed to explore the metabolic changes occurring in astrocytes during their activation. Astrocytes were derived from human ReN cell neural progenitors and characterized. They were activated by exposure to tumor necrosis factor alpha (TNFα) or interleukin 1ß (IL1ß) for 24 h. Astrocyte reaction and associated energy metabolic changes were assessed by immunostaining, gene expression, proteomics, metabolomics and extracellular flux analyses. ReN-derived astrocytes reactivity was observed by the modifications of genes and proteins linked to inflammation (cytokines, nuclear factor-kappa B (NFκB), signal transducers and activators of transcription (STATs)) and immune pathways (major histocompatibility complex (MHC) class I). Increased NFκB1, NFκB2 and STAT1 expression, together with decreased STAT3 expression, suggest an activation towards the detrimental pathway. Strong modifications of astrocyte cytoskeleton were observed, including a glial fibrillary acidic protein (GFAP) decrease. Astrogliosis was accompanied by changes in energy metabolism characterized by increased glycolysis and lactate release. Increased glycolysis is reported for the first time during human astrocyte activation. Astrocyte activation is strongly tied to energy metabolism, and a possible association between NFκB signaling and/or MHC class I pathway and glycolysis is suggested.

Wipe-sampling procedure optimisation for the determination of 23 antineoplastic drugs used in the hospital pharmacy.

PURPOSE: Optimise a wipe sampling procedure to evaluate the surface contamination for 23 antineoplastic drugs used in the hospital pharmacy. METHODS: The influence of various parameters (ie, sampling device, sampling solution, desorption modes) was evaluated using a validated liquid chromatography-mass spectrometry (LC-MS/MS) method able to quantify 23 antineoplastic drugs widely used in the hospital pharmacy: 5-fluorouracil, busulfan, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxorubicin, epirubicin, etoposide, etoposide phosphate, fludarabine phosphate, ganciclovir, gemcitabine, idarubicin, ifosfamide, irinotecan, methotrexate, paclitaxel, pemetrexed, raltitrexed, topotecan and vincristine. Best conditions were tested with real samples from a hospital pharmacy chemotherapy compounding unit. RESULTS: Polyester swabs (TX714 and TX716) gave satisfactory results for the desorption step for all compounds with mean recoveries of 90% and 95%, respectively. For the wiping step, higher recoveries were obtained using TX716 and isopropanol 75% as wiping solution. As anticipated, most intense contaminations were found close to the chemotherapy production site, on surfaces the most frequently in contact with operators' hands. CONCLUSION: Wipe sampling method was successfully developed and applied to real samples to determine surface contamination with 23 antineoplastic agents in trace amounts.

Approaches in metabolomics for regulatory toxicology applications.

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections.

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a "metabotypes", expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections.