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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 (C

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.

Therapeutic drug monitoring (TDM) of β-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].

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.

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.

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 3β-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 11β-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 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.

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 48 h incubation. Thus, inclusion of positive and negative controls and a steroid profile of the complete medium prior to the experiment (t = 0 h) 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.

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 controlebased 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.

The quantification of acetaminophen (APAP) and two of its metabolites, i.e. acetaminophen-glucuronide (APAP-GLUC) and acetaminophen-cysteine (APAP-CYS), is described in human plasma using ultra-high performance liquid chromatography coupled to a triple quadrupole linear ion trap mass spectrometer operating in the selected reaction monitoring (SRM/MS) mode and to a high resolution quadrupole time-of -flight mass spectrometer operating in the MS/MS (HR-SRM/MS) mode. Starting with a 50 μL plasma aliquot, a generic sample preparation was performed using protein precipitation with methanol/ethanol. Both methods were found to be linear over 2.5 orders of magnitude. Similar performances to the SRM/MS assay were obtained for APAP, APAP-CYS and APAP-GLUC using high resolution-selected reaction monitoring mode with LLOQ of 20, 50 and 50 ng/mL, respectively. For all analytes, precision was found to be better than 12% and accuracy in the range 90.3–109%. The present study demonstrates the ability of QqTOF platforms for accurate and precise quantification in MS/MS mode using short duty cycle with similar sensitivity to LC–SRM/MS. Additionally, as full scan data MS ALL are available qualitative and quantitative information on metabolites can also be obtained in a single LC–MS run.

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.

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).

A liquid chromatography–high-resolution mass spectrometry platform was used for simultaneous qualitative and quantitative (QUAL/QUAN) acquisition, enabling drug metabolism and metabolomics investi- gations. Plasma study samples were monitored for three different groups of patients at a single time-point (1 h after drug administration): one group received acetaminophen (APAP), one group received both APAP and ketorolac and one group was a control group. The quantification of APAP and two of its metabolites (APAP-glucuronide and APAP-cysteine) was performed on a fast acquisition quadrupole-Time-Of-Flight (50–100 ms duty cycle, resolving power of 30,000) compatible with UHPLC time constraints. High-resolution Selected Reaction Monitoring was used for quantification of APAP and its metabolites from 50–10,000 ng/mL using a 50 ?L plasma aliquot. Average measured concentrations were for APAP 6,650 ng/mL vs 6,160 ng/mL, APAP-CYS concentrations were 154.2 ng/mL vs 140.6 ng/mL and APAP-GLU concentrations 8,750 ng/mL vs 8,430 ng/mL between the group that received only APAP (n = 11) and the group that received APAP in combination with ketorolac (n = 11). No major differences were observed between the two groups of patients, as it would be expected due to the differing metabolism pathway for both substances. For the qualitative aspect, a metabolomics data processing platform with biological QC samples was applied to the study samples to search for unanticipated metabolites and biomarkers related to APAP and ketorolac metabolism. Multivariate analysis (i.e. Principle Component Analysis), variables grouping tools (i.e. PCVG) and high-resolution MS(/MS) spectra from the MSALL acquisition strategy enabled the profiling and characterization of circulating metabolites of APAP in plasma such as APAP-sulfate, APAP-mercapturate as well as ketorolac.

Quantitative and qualitative high-resolution (HR) dependent and independent acquisition schemes on a QqTOF MS (with resolving power 20,000-40,000) were investigated for the analysis of pharmaceutical compounds in biological fluids. High-resolution selected reaction monitoring (HR-SRM) was found to be linear over three orders of magnitude for quantitative analysis of paracetamol in human plasma, offering a real alternative to triple quadrupole LC-SRM/MS. Metabolic stability of talinolol in microsomes was characterized by use of three different acquisition schemes: (i) information-dependent acquisition (IDA) with a TOF MS experiment as survey scan and product-ion scan as dependent scan; (ii) MS(ALL) by collecting TOF mass spectra with and without fragmentation by alternating the collision energy of the collision cell between a low (i.e., 10 eV) and high setting (i.e., 40 eV); and (iii) a novel independent acquisition mode referred to as "sequential window acquisition of all theoretical fragment-ion spectra" (SWATH) or "global precursor ions scan mode" (GPS) in which sequential precursor ions windows (typically 20 u) are used to collect the same spectrum precursor and fragment ions using a collision energy range. SWATH or GPS was found to be superior to IDA or MS(ALL) in combination with UHPLC for qualitative analysis but requires a rapidly acquiring mass spectrometer. Finally, the GPS concept was used for QUAL/QUAN analysis (i.e. integration of qualitative and quantitative analysis) of bosentan and its metabolites in urine over a concentration range from 5 to 2,500 ng mL(-1).