Steering Away from Current Amoxicillin Dose Reductions in Hospitalized Patients with Impaired Kidney Function to Avoid Subtherapeutic Drug Exposure.

Current dose reductions recommended for amoxicillin in patients with impaired kidney function could lead to suboptimal treatments. In a prospective, observational study in hospitalized adults with varying kidney function treated with an IV or oral dose of amoxicillin, amoxicillin concentrations were measured in 1−2 samples on the second day of treatment. Pharmacometric modelling and simulations were performed to evaluate the probability of target attainment (PTA) for 40% of the time above MIC following standard (1000 mg q6h), reduced or increased IV dosing strategies. A total of 210 amoxicillin samples was collected from 155 patients with kidney function based on a CKD-EPI of between 12 and 165 mL/min/1.73 m2. Amoxicillin clearance could be well predicted with body weight and CKD-EPI. Recommended dose adjustments resulted in a clinically relevant reduction in the PTA for the nonspecies-related PK/PD breakpoint MIC of 8 mg/L (92%, 62% and 38% with a CKD-EPI of 10, 20 and 30 mL/min/1.73 m2, respectively, versus 100% for the standard dose). For MICs ≤ 2 mg/L, PTA > 90% was reached in these patients following both reduced and standard dose regimens. Our study showed that for amoxicillin, recommended dose reductions with impaired kidney function could lead to subtherapeutic amoxicillin concentrations in hospitalized patients, especially when targeting less susceptible pathogens.

Therapeutic Drug Monitoring of Orally Administered Letermovir Prophylaxis in Allogeneic Hematopoietic Stem Cell Transplant Recipients.

With balanced safety-efficacy profile, letermovir anti-cytomegalovirus (CMV) prophylaxis is used in hematopoietic stem cell transplant recipients (HSCTR). We assessed feasibility and usefulness of letermovir therapeutic drug monitoring (TDM) in HSCTR. We performed a prospective observational study on letermovir-TDM including 40 consecutive adult CMV-seropositive allogeneic-HSCTR who received orally (PO) administered letermovir. Minimal blood concentrations of letermovir (Ctrough) were measured on days 3 and 7 postletermovir initiation and weekly thereafter. Letermovir-Ctrough remained stable during the first 70 days post-HSCT at a median of 286 µg/L (interquartile range, 131 to 591 µg/L), with large interpatient/intrapatient variability. No associations between breakthrough clinically significant CMV infection or detectable CMV DNAemia and letermovir-Ctrough were observed. Patients with letermovir-associated adverse events had higher letermovir-Ctrough than patients without (400 versus 266 µg/L, P = 0.02). Letermovir-Ctrough was similar in patients with or without gastrointestinal symptoms (280 versus 300 µg/L, P = 0.49). Acute grade ≥2 GvHD was associated with higher letermovir-Ctrough (479 versus 248 µg/L, P = 0.001), including gastrointestinal GvHD (499 versus 263 µg/L, P = 0.004). Concomitantly administered posaconazole and cyclosporine were associated with higher letermovir-Ctrough (707 versus 259 µg/L, P < 0.001 and 437 versus 248 µg/L, P = 0.01, respectively). In multivariable analysis, both posaconazole (odds ratio [OR], 4.9; 95% confidence interval [CI], 2.4 to 9.7; P < 0.0001) and cyclosporine-adjusted letermovir dose at 240 mg daily (OR, 3.5; 95% CI, 1.4 to 9.0; P = 0.01) were independently associated with higher letermovir-Ctrough. In conclusion, administration of PO letermovir led to measurable and relatively stable letermovir-Ctrough, without noticeable associations with clinical efficacy. Letermovir exposure was not affected by gastrointestinal symptoms, but with posaconazole and cyclosporine administration. Associations between letermovir and concomitantly administered agents and adverse events warrant additional clinical studies.

Therapeutic drug monitoring and clinical outcomes in severely ill patients receiving amoxicillin: a single-centre prospective cohort study.

Therapeutic drug monitoring (TDM) of ß-lactam antibiotics is increasingly used to overcome rising antimicrobial resistance and improve antibiotic exposure. However, there is little guidance on target amoxicillin plasma concentrations. We aimed to define these by evaluating associations between amoxicillin concentrations and clinical outcomes. This single-centre prospective cohort study enrolled severely ill and/or immunosuppressed adult patients receiving amoxicillin for suspected or confirmed bacterial infection. TDM with ≥1 intermediate and ≥1 trough level was performed 24 h after therapy initiation. Primary and secondary outcomes were incidence of adverse events (AEs) and clinical failure through Day 30, respectively. A total of 156 patients were included. Important variations were observed both for intermediate (mean 13 mg/L, S.D. 13) and trough (mean 7 mg/L, S.D. 9) amoxicillin levels. Of 111 patients, 33 (30%) had trough levels below the non-species-related breakpoint (2 mg/L). AEs occurred in 27/156 patients (17%); no intermediate- or trough-level threshold predicting toxicity could be established. Patients with the highest-quartile trough levels (9.07-51.5 mg/L) did not experience significantly increased AEs [6/28 (21%) vs. 13/83 (16%); P = 0.6]. Nearly one-third (48/156; 31%) experienced clinical failure; low trough levels did not correlate with failure. There were few amoxicillin AEs yet a relatively high incidence of clinical failure. While no toxicity threshold could be established, the absence of increased AEs among patients with the highest trough concentrations suggests that trough levels up to 40 mg/L may be safe, at least for limited durations. Larger trials must further define optimal amoxicillin concentrations. [ClinicalTrials.gov ID: NCT03790631].

Internal calibration as an emerging approach for endogenous analyte quantification: Application to steroids.

The use of mass spectrometry methods with triple quadrupole instruments is well established for quantification. However, the preparation of calibration curves can be time-consuming and prone to analytical errors. In this study, an innovative internal calibration (IC) approach using a one-standard calibration with a stable isotope-labeled (SIL) standard version of the endogenous compound was developed. To ensure optimal quantitative performance, the following parameters were evaluated: the stability of the analyte-to-SIL response factor (RF), the chemical and isotopic purities of the SIL, and the instrumental reproducibility. Using six clinically important endogenous steroids and their respective SIL standards, we demonstrated that RFs obtained on different LC-MS platforms were consistent. The quantitative performance of the proposed approach was determined using quality control samples prepared in depleted serum, and showed both satisfactory precision (1.3%-12.4%) and trueness (77.5%-107.0%, with only 3 values outside ±30%). The developed method was then applied to human serum samples, and the results were similar to those obtained with the conventional quantification approach based on external calibration: the Passing-Bablok regression showed a proportional bias of 6.8% and a mean difference of -5.9% between the two methodologies. Finally, we showed that the naturally occurring isotopes of the SIL can be used to provide additional calibration points and increase the accuracy for analytes with low concentrations.

Performance enhancement and sample throughput increase of a multiresidue pesticides method in fruits and vegetables using Data-Dependent MS acquisition.

Due to the growing number of analysed pesticide residues, analytical strategies have evolved for the data processing of 100s of pesticides in a single analysis. We present herein a LC-MS/MS method based on triple quadrupole technology capable of detecting concentrations at 5 ng/g and confirming 381 pesticides in a single injection. Confirmatory analysis is performed using data-dependent acquisition that compares full MS/MS spectra of candidates to a fast library interrogation within the same injection. A comparison on more than 200 samples of fruits and vegetables (representing principal types: normal, pigmented, and fatty) with pre-existing workflow based on single MRM analysis per compound was performed to validate this approach. A fast turnaround time was demonstrated due to more-unambiguous identification suppressing the need for reinjection to confirm candidates. The automated library searching and confirmation only of putative hits also allowed focusing on the manual verification and validation steps just for putative candidates which hence also increased overall throughput and results quality. Superior robustness of the method due partially to a reduced volume injected was also one of the key points achieved using this methodology. An interesting feature is also the capability to enrich the library and the number of pesticides screened with ease.

Protein pathway analysis to study development-dependent effects of acute and repeated trimethyltin (TMT) treatments in 3D rat brain cell cultures.

Trimethyltin is an organometallic compound, described to be neurotoxic and to trigger neuroinflammation and oxidative stress. Previous studies associated TMT with the perturbation of mitochondrial function, or neurotransmission. However, the mechanisms of toxicity may differ depending on the duration of exposure and on the stage of maturation of brain cells. This study aim at elucidating whether the toxicity pathways triggered by a known neurotoxicant (TMT) differs depending on cell maturation stage or duration of exposure. To this end omics profiling of immature and differentiated 3D rat brain cell cultures exposed for 24 h or 10 days (10-d) to 0.5 and 1 µM of TMT was performed to better understand the underlying mechanisms of TMT associated toxicity. Proteomics identified 55 and 17 proteins affected by acute TMT treatment in immature and differentiated cultures respectively, while 10-day treatment altered 96 proteins in immature cultures versus 353 in differentiated. The results suggest different sensitivity to TMT depending on treatment duration and cell maturation. In accordance with known TMT mechanisms oxidative stress and neuroinflammation was observed after 10-d treatment at both maturation stages, whereas the neuroinflammatory process was more prominent in differentiated cultures than in the immature, no development-dependent difference could be detected for oxidative stress or synaptic neurodegeneration. Pathway analysis revealed that both vesicular trafficking and the synaptic machinery were strongly affected by 10-d TMT treatment in both maturation stages, as was GABAergic and glutamatergic neurotransmission. This study shows that omics approaches combined with pathway analysis constitutes an improved tool-set in elucidating toxicity mechanisms.

An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments.

Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.

Removal of batch effects using stratified subsampling of metabolomic data for in vitro endocrine disruptors screening.

The human adrenal cell line H295R constitutes a well-established model to evaluate potential alterations of steroidogenic pathways as a result of chemical exposure. However, to date most assays are based on the targeted investigation of a limited number of steroid hormones, thus preventing in-depth mechanistic interpretation with respect to steroidogenesis. In that context, analytical strategies coupling liquid chromatography and high-resolution mass spectrometry (LC-HRMS) have been reported as promising methods for an extended monitoring of steroid metabolites. However, unwanted sources of variability occurring during the acquisition process, including batch effects, may prevent relevant biochemical information to be properly highlighted. Dedicated data mining strategies are therefore needed to overcome these limitations, and extract relevant extended steroidomic profiles. The present study combines an untargeted LC-HRMS acquisition strategy with automated steroid metabolite annotation based on accurate mass and isotopic patterns, and a chemometric tool allowing the different sources of variability to be decomposed based on experimental design. This workflow was applied to the extended monitoring of steroidogenic dysregulations due to endocrine disrupting chemicals (EDCs) exposure in H295R cell cultures. A series of six chemicals, including acetyl tributylcitrate, octyl methoxycinnamate, torcetrapib, forskolin, linuron, and octocrylene, and dimethylsulfoxide as solvent control, were investigated through the simultaneous monitoring of 130 potential steroid metabolites, repeating the whole experiment independently three times. A stratified subsampling strategy was carried out to remove efficiently systematic batch variations and highlight subgroups of chemicals with similar steroid patterns. The proposed approach was reported as a potent screening strategy, as it allowed specific alterations of the steroid hormone biosynthesis and metabolism related to distinct mechanisms of action to be distinguished.

Steroid profiles in both blood serum and seminal plasma are not correlated and do not reflect sperm quality: Study on the male reproductive health of fifty young Swiss men.

Steroids play an important role in sperm production and quality. These hormones have been extensively studied in blood, but poorly investigated in semen. The purpose of our study was to evaluate the relationship between sperm quality and steroid profiles in blood and semen in a small cohort of young Swiss men. Another objective was to determine whether the presence of xenobiotics or drugs could influence these profiles. Semen analysis was performed according to WHO guidelines, and steroid profiles in blood serum and seminal plasma were determined by two complementary approaches: a targeted investigation involving the quantification of a limited number of relevant steroids for testing putative correlations with sperm parameters and a global "steroidomic" analysis highlighting their complex metabolic relationship. Results showed that steroid profiles are distinct within blood and seminal fluid. No significant correlation was found between individual steroids measured in blood and in semen, demonstrating the relevance of assessing hormone levels in both fluids. Moreover, testosterone and androstenedione levels were significantly correlated in semen but not in blood. None of the evaluated spermiogram parameters was linked to steroid levels measured in any medium. The steroidomic analyses confirmed that the steroids present in both fluids are different and that there is no correlation with spermiogram parameters. Finally, upon toxicological screening, we observed that all the three samples positive for tetrahydrocannabinol, which is known to act as an endocrine disruptor, displayed low seminal testosterone concentrations. In conclusion, we did not find any evidence suggesting using steroid profiles, neither in blood nor in semen, as surrogates for sperm analyses. However, steroid profiles could be useful biomarkers of individual exposure to endocrine disruptors.

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study.

The development of metabolomics based on ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) now allows hundreds to thousands of metabolites to be simultaneously monitored in biological matrices. In that context, bioinformatics and multivariate data analysis (MVA) play a crucial role in the detection of relevant alteration patterns. However, sound biological interpretations must necessarily be supported by metabolite identifications to be definitive or at least have a high degree of confidence. Each compound, should be characterised by unique molecular properties. Among them, the exact mass and the chromatographic retention time are recognised as major and complementary criteria for compound identification. While the former is easily derived from the molecular structure, building generic and accurate retention time open databases still constitutes a critical issue because of the vast diversity of instruments, stationary phases and operating conditions in UHPLC-HRMS. Because several hits matching a molecular formula obtained from an exact mass and an isotopic pattern are often generated for each analyte, this methodology rarely allows a unique and unambiguous molecular identity to be gained. This work aims to provide a flexible solution to facilitate reliable compound annotation based on retention time in reversed-phase liquid chromatography (RPLC). It proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted. Starting from a subset of the Human Metabolite Database (HMDB), a new dynamic database involving LSS parameters was developed. A real case study involving steroidogenesis alterations due to forskolin exposure was conducted using the adrenal H295R OECD reference cell model for endocrine disruptor screening. The prediction of retention times was successfully achieved, facilitating steroid identification. An automated procedure which implements the compound annotation levels encouraged by the Metabolite Standard Initiative (MSI) and the Coordination of Standards in Metabolomics (COSMOS) was also developed to speed up the process and enhance the data reusability.

Steroid profiling in H295R cells to identify chemicals potentially disrupting the production of adrenal steroids.

The validated OECD test guideline 456 based on human adrenal H295R cells promotes measurement of testosterone and estradiol production as read-out to identify potential endocrine disrupting chemicals. This study aimed to establish optimal conditions for using H295R cells to detect chemicals interfering with the production of key adrenal steroids. H295R cells' supernatants were characterized by liquid chromatography-mass spectrometry (LC-MS)-based steroid profiling, and the influence of experimental conditions including time and serum content was assessed. Steroid profiles were determined before and after incubation with reference compounds and chemicals to be tested for potential disruption of adrenal steroidogenesis. The H295R cells cultivated according to the OECD test guideline produced progestins, glucocorticoids, mineralocorticoids and adrenal androgens but only very low amounts of testosterone. However, testosterone contained in Nu-serum was metabolized during the 48h incubation. Thus, inclusion of positive and negative controls and a steroid profile of the complete medium prior to the experiment (t=0h) was necessary to characterize H295R cells' steroid production and indicate alterations caused by exposure to chemicals. Among the tested chemicals, octyl methoxycinnamate and acetyl tributylcitrate resembled the corticosteroid induction pattern of the positive control torcetrapib. Gene expression analysis revealed that octyl methoxycinnamate and acetyl tributylcitrate enhanced CYP11B2 expression, although less pronounced than torcetrapib. Further experiments need to assess the toxicological relevance of octyl methoxycinnamate- and acetyl tributylcitrate-induced corticosteroid production. In conclusion, the extended profiling and appropriate controls allow detecting chemicals that act on steroidogenesis and provide initial mechanistic evidence for prioritizing chemicals for further investigations.

Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies.

Among the various biological matrices used in metabolomics, urine is a biofluid of major interest because of its non-invasive collection and its availability in large quantities. However, significant sources of variability in urine metabolomics based on UHPLC-MS are related to the analytical drift and variation of the sample concentration, thus requiring normalization. A sequential normalization strategy was developed to remove these detrimental effects, including: (i) pre-acquisition sample normalization by individual dilution factors to narrow the concentration range and to standardize the analytical conditions, (ii) post-acquisition data normalization by quality control-based robust LOESS signal correction (QC-RLSC) to correct for potential analytical drift, and (iii) post-acquisition data normalization by MS total useful signal (MSTUS) or probabilistic quotient normalization (PQN) to prevent the impact of concentration variability. This generic strategy was performed with urine samples from healthy individuals and was further implemented in the context of a clinical study to detect alterations in urine metabolomic profiles due to kidney failure. In the case of kidney failure, the relation between creatinine/osmolality and the sample concentration is modified, and relying only on these measurements for normalization could be highly detrimental. The sequential normalization strategy was demonstrated to significantly improve patient stratification by decreasing the unwanted variability and thus enhancing data quality.

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification.

The untargeted profiling of steroids constitutes a growing research field because of their importance as biomarkers of endocrine disruption. New technologies in analytical chemistry, such as ultra high-pressure liquid chromatography coupled with mass spectrometry (MS), offer the possibility of a fast and sensitive analysis. Nevertheless, difficulties regarding steroid identification are encountered when considering isotopomeric steroids. Thus, the use of retention times is of great help for the unambiguous identification of steroids. In this context, starting from the linear solvent strength (LSS) theory, quantitative structure retention relationship (QSRR) models, based on a dataset composed of 91 endogenous steroids and VolSurf + descriptors combined with a new dedicated molecular fingerprint, were developed to predict retention times of steroid structures in any gradient mode conditions. Satisfactory performance was obtained during nested cross-validation with a predictive ability (Q(2)) of 0.92. The generalisation ability of the model was further confirmed by an average error of 4.4% in external prediction. This allowed the list of candidates associated with identical monoisotopic masses to be strongly reduced, facilitating definitive steroid identification.

Evaluation of steroidomics by liquid chromatography hyphenated to mass spectrometry as a powerful analytical strategy for measuring human steroid perturbations.

This review presents the evolution of steroid analytical techniques, including gas chromatography coupled to mass spectrometry (GC-MS), immunoassay (IA) and targeted liquid chromatography coupled to mass spectrometry (LC-MS), and it evaluates the potential of extended steroid profiles by a metabolomics-based approach, namely steroidomics. Steroids regulate essential biological functions including growth and reproduction, and perturbations of the steroid homeostasis can generate serious physiological issues; therefore, specific and sensitive methods have been developed to measure steroid concentrations. GC-MS measuring several steroids simultaneously was considered the first historical standard method for analysis. Steroids were then quantified by immunoassay, allowing a higher throughput; however, major drawbacks included the measurement of a single compound instead of a panel and cross-reactivity reactions. Targeted LC-MS methods with selected reaction monitoring (SRM) were then introduced for quantifying a small steroid subset without the problems of cross-reactivity. The next step was the integration of metabolomic approaches in the context of steroid analyses. As metabolomics tends to identify and quantify all the metabolites (i.e., the metabolome) in a specific system, appropriate strategies were proposed for discovering new biomarkers. Steroidomics, defined as the untargeted analysis of the steroid content in a sample, was implemented in several fields, including doping analysis, clinical studies, in vivo or in vitro toxicology assays, and more. This review discusses the current analytical methods for assessing steroid changes and compares them to steroidomics. Steroids, their pathways, their implications in diseases and the biological matrices in which they are analysed will first be described. Then, the different analytical strategies will be presented with a focus on their ability to obtain relevant information on the steroid pattern. The future technical requirements for improving steroid analysis will also be presented.

Evaluation and identification of dioxin exposure biomarkers in human urine by high-resolution metabolomics, multivariate analysis and in vitro synthesis.

A previous high-resolution metabolomic study pointed out a dysregulation of urinary steroids and bile acids in human cases of acute dioxin exposure. A subset of 24 compounds was highlighted as putative biomarkers. The aim of the current study was (i) to evaluate the 24 biomarkers in an independent human cohort exposed to dioxins released from the incineration fumes of a municipal waste incinerator and; (ii) to identify them by comparison with authentic chemical standards and biosynthesised products obtained with in vitro metabolic reactions. An orthogonal projection to latent structures discriminant analysis built on biomarker profiles measured in the intoxicated cohort and the controls separated both groups with reported values of 93.8%; 100% and 87.5% for global accuracy; sensitivity and specificity; respectively. These results corroborated the 24 compounds as exposure biomarkers; but a definite identification was necessary for a better understanding of dioxin toxicity. Dehydroepiandrosterone 3ß-sulfate, androsterone 3α-glucuronide, androsterone 3α-sulfate, pregnanediol 3α-glucuronide and 11-ketoetiocholanolone 3α-glucuronide were identified by authentic standards. Metabolic reactions characterised four biomarkers: glucuronide conjugates of 11ß-hydroxyandrosterone; glycochenodeoxycholic acid and glycocholic acid produced in human liver microsomes and glycoursodeoxycholic acid sulfate generated in cytosol fraction. The combination of metabolomics by high-resolution mass spectrometry with in vitro metabolic syntheses confirmed a perturbed profile of steroids and bile acids in human cases of dioxin exposure.

The use of mass spectrometry to analyze dried blood spots.

Dried blood spots (DBS) typically consist in the deposition of small volumes of capillary blood onto dedicated paper cards. Comparatively to whole blood or plasma samples, their benefits rely in the fact that sample collection is easier and that logistic aspects related to sample storage and shipment can be relatively limited, respectively, without the need of a refrigerator or dry ice. Originally, this approach has been developed in the sixties to support the analysis of phenylalanine for the detection of phenylketonuria in newborns using bacterial inhibition test. In the nineties tandem mass spectrometry was established as the detection technique for phenylalanine and tyrosine. DBS became rapidly recognized for their clinical value: they were widely implemented in pediatric settings with mass spectrometric detection, and were closely associated to the debut of newborn screening (NBS) programs, as a part of public health policies. Since then, sample collection on paper cards has been explored with various analytical techniques in other areas more or less successfully regarding large-scale applications. Moreover, in the last 5 years a regain of interest for DBS was observed and originated from the bioanalytical community to support drug development (e.g., PK studies) or therapeutic drug monitoring mainly. Those recent applications were essentially driven by improved sensitivity of triple quadrupole mass spectrometers. This review presents an overall view of all instrumental and methodological developments for DBS analysis with mass spectrometric detection, with and without separation techniques. A general introduction to DBS will describe their advantages and historical aspects of their emergence. A second section will focus on blood collection, with a strong emphasis on specific parameters that can impact quantitative analysis, including chromatographic effects, hematocrit effects, blood effects, and analyte stability. A third part of the review is dedicated to sample preparation and will consider off-line and on-line extractions; in particular, instrumental designs that have been developed so far for DBS extraction will be detailed. Flow injection analysis and applications will be discussed in section IV. The application of surface analysis mass spectrometry (DESI, paper spray, DART, APTDCI, MALDI, LDTD-APCI, and ICP) to DBS is described in section V, while applications based on separation techniques (e.g., liquid or gas chromatography) are presented in section VI. To conclude this review, the current status of DBS analysis is summarized, and future perspectives are provided.

Steroidomic Footprinting Based on Ultra-High Performance Liquid Chromatography Coupled with Qualitative and Quantitative High-Resolution Mass Spectrometry for the Evaluation of Endocrine Disrupting Chemicals in H295R Cells.

The screening of endocrine disrupting chemicals (EDCs) that may alter steroidogenesis represents a highly important field mainly due to the numerous pathologies, such as cancer, diabetes, obesity, osteoporosis, and infertility that have been related to impaired steroid-mediated regulation. The adrenal H295R cell model has been validated to study steroidogenesis by the Organization for Economic Co-operation and Development (OECD) guideline. However, this guideline focuses solely on testosterone and estradiol monitoring, hormones not typically produced by the adrenals, hence limiting possible in-depth mechanistic investigations. The present work proposes an untargeted steroidomic footprinting workflow based on ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution MS for the screening and mechanistic investigations of EDCs in H295R cell supernatants. A suspected EDC, triclocarban (TCC), used in detergents, cosmetics, and personal care products, was selected to demonstrate the efficiency of the reported methodology, allowing the simultaneous assessment of a steroidomic footprint and quantification of a selected subset of steroids in a single analysis. The effects of exposure to increasing TCC concentrations were assessed, and the selection of features with database matching followed by multivariate analysis has led to the selection of the most salient affected steroids. Using correlation analysis, 11 steroids were associated with a high, 18 with a medium, and 8 with a relatively low sensitivity behavior to TCC. Among the candidates, 13 identified steroids were simultaneously quantified, leading to the evaluation and localization of the disruption of steroidogenesis caused by TCC upstream of the formation of pregnenolone. The remaining candidates could be associated with a specific steroid class (progestogens and corticosteroids, or androgens) and represent a specific footprint of steroidogenesis disruption by TCC. This strategy was devised to be compatible with medium/high-throughput screening and could be useful for the mechanistic elucidation of EDCs.

Human urinary biomarkers of dioxin exposure: analysis by metabolomics and biologically driven data dimensionality reduction.

Untargeted metabolomic approaches offer new opportunities for a deeper understanding of the molecular events related to toxic exposure. This study proposes a metabolomic investigation of biochemical alterations occurring in urine as a result of dioxin toxicity. Urine samples were collected from Czech chemical workers submitted to severe dioxin occupational exposure in a herbicide production plant in the late 1960s. Experiments were carried out with ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution quadrupole time-of-flight (QTOF) mass spectrometry. A chemistry-driven feature selection was applied to focus on steroid-related metabolites. Supervised multivariate data analysis allowed biomarkers, mainly related to bile acids, to be highlighted. These results supported the hypothesis of liver damage and oxidative stress for long-term dioxin toxicity. As a second step of data analysis, the information gained from the urine analysis of Victor Yushchenko after his poisoning was examined. A subset of relevant urinary markers of acute dioxin toxicity from this extreme phenotype, including glucuro- and sulfo-conjugated endogenous steroid metabolites and bile acids, was assessed for its ability to detect long-term effects of exposure. The metabolomic strategy presented in this work allowed the determination of metabolic patterns related to dioxin effects in human and the discovery of highly predictive subsets of biologically meaningful and clinically relevant compounds. These results are expected to provide valuable information for a deeper understanding of the molecular events related to dioxin toxicity. Furthermore, it presents an original methodology of data dimensionality reduction by using extreme phenotype as a guide to select relevant features prior to data modeling (biologically driven data reduction).