Targeted and untargeted detection of fentanyl analogues and their metabolites in hair by means of UHPLC-QTOF-HRMS.

Detection of new psychoactive substances and synthetic opioids is generally performed by means of targeted methods in mass spectrometry, as they generally provide adequate sensitivity and specificity. Unfortunately, new and unexpected compounds are continuously introduced in the illegal market of abused drugs, preventing timely updating of the analytical procedures. Moreover, the investigation of biological matrices is influenced by metabolism and excretion, in turn affecting the chance of past intake detectability. In this scenario, new opportunities are offered by both the non-targeted approaches allowed by modern UHPLC-HRMS instrumentation and the investigation of hair as the matrix of choice to detect long-term exposure to toxicologically relevant substances. In this study, we present a comprehensive and validated workflow that combines the use of UHPLC-QTOF-HRMS instrumentation with a simple hair sample extraction procedure for the detection of a variety of fentanyl analogues and metabolites. A simultaneous targeted and untargeted analysis was applied to 100 real samples taken from opiates users. MS and MS/MS data were collected for each sample. Data acquisition included a TOF-MS high-resolution scan combined with TOF-MS/MS acquisition demonstrating considerable capability to detect expected and unexpected substances even at low concentration levels. The predominant diffusion of fentanyl was confirmed by its detection in 68 hair samples. Other prevalent analogues were furanylfentanyl (28 positive samples) and acetylfentanyl (14 positive samples). Carfentanil, methylfentanyl, and ocfentanil were not found in any of the analyzed samples. Furthermore, the retrospective data analysis based on untargeted acquisition allowed the identification of two fentanyl analogues, namely ß-hydroxyfentanyl and methoxyacetylfentanyl, which were not originally included in the panel of targeted analytes.

Simultaneous determination of 137 drugs of abuse, new psychoactive substances, and novel synthetic opioids in meconium by UHPLC-QTOF.

New psychoactive substances (NPS) have been introduced into the market in recent years, with new analytes reported every year. The use of these substances in women can occur at any stage of life, even in the childbearing age. Drug use during pregnancy presents significant risks for the mother and the fetus, so it is important to have tools that allow to detect prenatal exposure to these substances of abuse. Therefore, an analytical method for the determination of 137 NPS and other drugs of abuse in meconium by UHPLC-QTOF was developed and validated for semi-quantitative purpose. Linearity range, limit of detection (LOD), precision, matrix effect, selectivity, and specificity were evaluated. For all analytes, the calibration curves were studied in the ranges between 2, 10, or 50 ng/g and 750 or 1000 ng/g, (depending on the analyte) and the LOD ranged between 0.04 and 2.4 ng/g. The method was applied to 30 meconium specimens from cases in which fentanyl had been administered as epidural anesthesia at the time of delivery or cases in which the maternal hair was positive to other drug of abuse. Four meconium samples tested positive for fentanyl (range concentration = 440-750 ng/g) and two samples tested positive to acetylfentanyl (range concentration = 190-1400 ng/g).

Targeted and untargeted quantification of quorum sensing signalling molecules in bacterial cultures and biological samples via HPLC-TQ MS techniques.

Quorum sensing (QS) is the ability of some bacteria to detect and to respond to population density through signalling molecules. QS molecules are involved in motility and cell aggregation mechanisms in diseases such as sepsis. Few biomarkers are currently available to diagnose sepsis, especially in high-risk conditions. The aim of this study was the development of new analytical methods based on liquid chromatography-mass spectrometry for the detection and quantification of QS signalling molecules, including N-acyl homoserine lactones (AHL) and hydroxyquinolones (HQ), in biofluids. Biological samples used in the study were Pseudomonas aeruginosa bacterial cultures and plasma from patients with sepsis. We developed two MS analytical methods, based on neutral loss (NL) and product ion (PI) experiments, to identify and characterize unknown AHL and HQ molecules. We then established a multiple-reaction-monitoring (MRM) method to quantify specific QS compounds. We validated the HPLC-MS-based approaches (MRM-NL-PI), and data were in accord with the validation guidelines. With the NL and PI MS-based methods, we identified and characterized 3 and 13 unknown AHL and HQ compounds, respectively, in biological samples. One of the newly found AHL molecules was C12-AHL, first quantified in Pseudomonas aeruginosa bacterial cultures. The MRM quantitation of analytes in plasma from patients with sepsis confirmed the analytical ability of MRM for the quantification of virulence factors during sepsis. Graphical abstract.

Optimization and validation of a GC-MS quantitative method for the determination of an extended estrogenic profile in human urine: Variability intervals in a population of healthy women.

An analytical method based on GC-MS was developed for the determination of a wide panel of urinary estrogens, together with their principal metabolites. Because of the low concentration of estrogens in urine, an efficient sample pre-treatment was optimized by a design of experiment (DoE) procedure to achieve satisfactory sensitivity. A second DoE was built for the optimization of the chromatographic run, with the purpose of reaching the most efficient separation of analytes with potentially interfering ions and similar chromatographic properties. The method was fully validated using a rigorous calibration strategy: from several replicate analyses of blank urine samples spiked with the analytes, calibration models were built with particular attention to the study of heteroscedasticity and quadraticity. Other validation parameters, including the limit of detection, intra-assay precision and accuracy, repeatability, selectivity, specificity, and carry-over, were obtained using the same set of data. Further experiments were performed to evaluate matrix effect and extraction recovery. Then the urinary estrogen profiles of 138 post-menopausal healthy women were determined. These profiles provide a representation of physiological concentration ranges, which, in forthcoming studies, will be matched on the base of multivariate statistics with the urinary estrogenic profile of women with breast or ovarian cancer.

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples.

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.

Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models.

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares-discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.

Limited Role of Hair Cortisol and Cortisone Measurement for Detecting Cortisol Autonomy in Patients With Adrenal Incidentalomas.

Several studies demonstrated the diagnostic accuracy of hair glucocorticoid measurement in patients with overt Cushing syndrome, but few data are available for patients with adrenal incidentaloma (AI) and cortisol autonomy. The aim of our study was to assess whether measurement of 5 corticosteroid hormones with the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method in the keratin matrix is useful to stratify patients with AI by the presence of autonomous cortisol secretion [ACS] (defined as serum cortisol after 1 mg dexamethasone suppression test (DST) > 138 nmol/l) or possible ACS [PACS] (defined as serum cortisol after 1 mg DST > 50 nmol/l but ≤138 nmol/l). We analysed data of 67 AI patients (32 with cortisol autonomy) and 81 healthy subjects. We did not find any significant statistical difference comparing hair cortisol, cortisone, and 20ß-dihydrocortisol concentrations between healthy controls and AI patients, while 6ß-hydroxycortisol and 11-deoxycortisol were undetectable. Moreover, no significant difference was found in hair cortisol, cortisone, and 20ß-dihydrocortisol levels of AI patients with or without cortisol autonomy. Finally, we did not find any correlation in patients with AI between hormonal concentrations in the keratin matrix and serum, salivary, and urinary cortisol levels, or with body mass index. In conclusion, our findings suggest that hair glucocorticoid measurement is not suitable as a diagnostic test for cortisol autonomy (ACS and PACS).

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia.

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Prospective evaluation of urinary steroids and prostate carcinoma-induced deviation: preliminary results.

BACKGROUND: The serum prostate-specific antigen is the most widespread biomarker for prostate disease. Its low specificity for prostatic malignancies is a matter of concern and the reason why new biomarkers for screening purposes are needed. The correlation between altered production of the main steroids and prostate carcinoma (PCa) occurrence is historically known. The purpose of this study is to evaluate the modifications of a comprehensive urinary endogenous steroidal profile (USP) induced by PCa, by multivariate statistical methods. METHODS: A total of 283 Italian subjects were included in the study, 139 controls and 144 PCa-affected patients. The USP, including 17 steroids and five urinary steroidal ratios, was quantitatively evaluated using gas chromatography coupled with single quadrupole mass spectrometry (GC-MS). The data were interpreted using a chemometric, multivariate approach (intrinsically more sensible to alterations with respect to traditional statistics) and a model for the discrimination of cancer-affected profiles was built. RESULTS: Two multivariate classification models were calculated, the former including three steroids with the highest statistical significance (e.g. testosterone, etiocholanolone and 7ß-OH-DHEA) and PSA values, the latter considering the three steroids' levels only. Both models yielded high sensitivity and specificity scores near to 70%, resulting significantly higher than PSA alone. CONCLUSIONS: Three USP steroids resulted significantly altered in our PCa population. These preliminary results, combined with the simplicity and low-cost of the analysis, open to further investigation of the potential role of this restricted USP in PCa diagnosis.

Experimental and statistical protocol for the effective validation of chromatographic analytical methods.

The validation of analytical methods is of crucial importance in several fields of application. A new protocol for the validation of chromatographic methods has been proposed. The overall protocol is described in a parallel paper, where the case of a multi-targeted gas chromatography - mass spectrometry (GC-MS) method for the determination of androgens in human urine is in-depth discussed. The purpose of this paper is to report the details about the GC-MS separation and detection of the target analytes, and to provide the mathematical formulas needed to perform the validation of the principal parameters. Briefly, the validation protocol foresees the repetition of three calibration curves in three different days, providing a total amount of nine replicates. Such a structured design allows to use the same experiments to•perform a rigorous calibration study, by the evaluation of heteroscedasticity, comparison of several weights and linear/quadratic calibration curves.•determine several parameters which are traditionally computed from dedicated experiments, namely intra- and inter-day accuracy and precision, limit of detection, specificity, selectivity, ion abundance repeatability, and carry over.•Finally, few further experiments are necessary to evaluate the retention time repeatability, matrix effect and extraction recovery.

Multivariate interpretation of the urinary steroid profile and training-induced modifications. The case study of a Marathon runner.

The steroidal module of the athlete biological passport (ABP) introduced by the World Anti-Doping Agency (WADA) in 2014 includes six endogenous androgenic steroids and five of their concentration ratios, monitored in urine samples collected repeatedly from the same athlete, whose values are interpreted by a Bayesian model on the basis of intra-individual variability. The same steroid profile, plus dihydrotestosterone (DHT) and DHEA, was determined in 198 urine samples collected from an amateur marathon runner monitored over three months preceding an international competition. Two to three samples were collected each day and subsequently analyzed by a fully validated gas chromatography-mass spectrometry protocol. The objective of the study was to identify the potential effects of physical activity at different intensity levels on the physiological steroid profile of the athlete. The results were interpreted using principal component analysis and Hotelling's T2 vs Q residuals plots, and were compared with a profile model based on the samples collected after rest. The urine samples collected after activity of moderate or high intensity, in terms of cardiac frequency and/or distance run, proved to modify the basal steroid profile, with particular enhancement of testosterone, epitestosterone, and 5α-androstane-3α,17ß-diol. In contrast, all steroid concentration ratios were apparently not modified by intense exercise. The alteration of steroid profiles seemingly lasted for few hours, as most of the samples collected 6 or more hours after training showed profiles compatible with the "after rest" model. These observations issue a warning about the ABP results obtained immediately post-competition.

Individual and cyclic estrogenic profile in women: Structure and variability of the data.

The concentration of estrogens in the body fluids of women is highly variable, due to the menstrual cycle, circadian oscillations, and other physiological and pathological causes. To date, only the cyclic fluctuations of the principal estrogens (estradiol and estrone) have been studied, with limited outcome of general significance. Aim of the present study was to examine in detail the cyclic variability of a wide estrogens' panel and to interpret it by multivariate statistics. Four estrogens (17α-estradiol, 17ß-estradiol, estrone, estriol) and eleven of their metabolites (4-methoxyestrone, 2-methoxyestrone, 16α-hydroxyestrone, 4-hydroxyestrone, 2-hydroxyestrone, 4-methoxyestradiol, 2-methoxyestradiol, 4-hydroxyestradiol, 2-hydroxyestradiol, estriol, 16-epiestriol, and 17-epiestriol) were determined in urine by a gas chromatography - mass spectrometry method, which was developed by design of experiments and fully validated according to ISO 17025 requirements. Then, urine samples collected every morning for a complete menstrual cycle from 9 female volunteers aged 24-35 years (1 parous) were analysed. The resulting three-dimensional data (subjects × days × estrogens) were interpreted using several statistical tools. Parallel Factor Analysis compared the estrogen profiles in order to explore the cyclic and inter-individual variability of each analyte. Principal Component Analysis (PCA) provided clear separation of the sampling days along the cycle, allowing discrimination among the luteal, ovulation, and follicular phases. The scores obtained from PCA were used to build a Linear Discriminant Analysis classification model which enhanced the recognition of the three cycle's phases, yielding an overall classification non-error rate equal to 90%. These statistical models may find prospective application in fertility studies and the investigation of endocrinology disorders and other hormone-dependent diseases.

Correlation between chronological and physiological age of males from their multivariate urinary endogenous steroid profile and prostatic carcinoma-induced deviation.

The biosynthesis of endogenous androgenic anabolic steroids (EAAS) in males varies with age. Knowledge of the general urinary EAAS profile's dependence from aging - not reported up to now - may represents a prerequisite for its exploitation in the screening and diagnostic support for several pathologies. Extended urinary EAAS profiles were obtained from healthy and pathological individuals, using a GC-MS method which was fully validated by a stepwise, analyst-independent scheme. Seventeen EAAS and five of their concentration ratios were determined and investigated using multivariate statistical methods. A regression model based on Kernel partial least squares algorithm was built to correlate the chronological age of healthy male individuals with their "physiological age" as determined from their urinary EAAS profile. Strong correlation (R2 = 0.75; slope = 0.747) and good prediction ability of the real chronological age was inferred from EAAS data. In contrast, patients with recent diagnosis (not pharmacologically treated) of prostatic carcinoma (PCa) exhibited a comprehensive EAAS profile with strong negative deviation from the model, corresponding a younger predicted age. This result is possibly related to the activation of anomalous steroid biosynthesis induced from PCa. Over a restricted 60-80 years-old population, PLS-discriminant analysis (DA) was used to distinguish healthy subjects from patients with untreated PCa. PLS-DA yielded excellent discrimination (sensitivity and specificity >90%) between healthy and pathological individuals. This proof-of-concept study provides a preliminary evaluation of multivariate DA on wide EAAS profiles as a screening method to distinguish PCa from non-pathological conditions, overcoming the potentially interfering effect of ageing.

Application of multivariate statistics to the Steroidal Module of the Athlete Biological Passport: A proof of concept study.

The Technical Document TD2014EAAS was drafted by the World Anti-Doping Agency (WADA) in order to fight the spread of endogenous anabolic androgenic steroids (EAAS) misuse in several sport disciplines. In particular, adoption of the so-called Athlete Biological Passport (ABP) - Steroidal Module allowed control laboratories to identify anomalous EAAS concentrations within the athletes' physiological urinary steroidal profile. Gas chromatography (GC) combined with mass spectrometry (MS), indicated by WADA as an appropriate technique to detect urinary EAAS, was utilized in the present study to develop and fully-validate an analytical method for the determination of all EAAS markers specified in TD2014EAAS, plus two further markers hypothetically useful to reveal microbial degradation of the sample. In particular, testosterone, epitestosterone, androsterone, etiocholanolone, 5α-androstane-3α,17ß-diol, 5ß-androstane-3α,17ß-diol, dehydroepiandrosterone, 5α-dihydrotestosterone, were included in the analytical method. Afterwards, the multi-parametric feature of ABP profile was exploited to develop a robust approach for the detection of EAAS misuse, based on multivariate statistical analysis. In particular, Principal Component Analysis (PCA) was combined with Hotelling T(2) tests to explore the EAAS data obtained from 60 sequential urine samples collected from six volunteers, in comparison with a reference population of single urine samples collected from 96 volunteers. The new approach proved capable of identifying anomalous results, including (i) the recognition of samples extraneous to each of the individual urine series and (ii) the discrimination of the urine samples collected from individuals to whom "endogenous" steroids had been administrated with respect to the rest of the samples population. The proof-of-concept results presented in this study will need further extension and validation on a population of sport professionals.