Effective validation of chromatographic analytical methods: The illustrative case of androgenic steroids

Alladio, Eugenio; Amante, Eleonora; Bozzolino, Cristina; Seganti, Fabrizio; Salomone, Alberto; Vincenti, Marco; Desharnais, Brigitte

doi:10.1016/j.talanta.2020.120867

Cited by 32 publications

(31 citation statements)

References 28 publications

(1 reference statement)

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“…For method validation, different parameters were evaluated: selectivity, recovery, carry-over, intra-run accuracy and precision, limit of quantitation (LOQ), lower LOQ (LLOQ), upper LOQ (ULOQ), stability to freeze-thaw cycle and calibration model. The last one, in particular, was evaluated using a stepwise approach as described elsewhere [42], and the linearity of the calibration curves using an R routine developed by Desharnais et al [43,44]: Firstly, the heteroscedasticity of data points was tested by performing an F-test on the variance of the area ratios at the lowest and highest calibration levels. The heteroscedasticity study was also integrated with the Levene test (in the version modified by Brown and Forsythe).…”

Section: Hplc-tq Ms Methods Validationmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Hplc-tq Ms Methods Validationmentioning

confidence: 99%

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

et al. 2020

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“…The calibration process was conducted with an optimized procedure, requiring the preparation of three replicates of the calibration curves for the targeted compounds in three different days for a total of nine calibration curves [22,23]. Several validation parameters were determined from these data, including linearity range and calibration model, selectivity, specificity, limit of detection (LOD), limit of quantification (LOQ), trueness, intra and inter-assay precision, and repeatability.…”

Section: Validationmentioning

confidence: 99%

“…The best calibration model was determined using the RStudio routine developed by Desharnais et al [24,25], which included the study of homo-vs. heteroscedasticity (and the correction of it by means of the proper weight) and order of the calibration model (linear or quadratic). The LOD and LOQ were estimated by means of the Hubaux-Vos' algorithm [26] applied in the linear dynamic range and corrected for the heteroscedasticity weights [22].…”

Section: Validationmentioning

confidence: 99%

“…The presence of interfering peaks around the retention time of the analytes was identified by S/N values higher than 3. Intraand inter-day precision and accuracy were evaluated using two dedicated back-calculation approaches, described elsewhere [22]. Optimal percent coefficient of variation (CV%) and percent bias were expected to lie within ± 15%, while results within ± 25% were still considered satisfactory.…”

Section: Validationmentioning

confidence: 99%

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

Salomone

Corcia²,

Negri³

et al. 2020

Anal Bioanal Chem

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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 β-hydroxyfentanyl and methoxyacetylfentanyl, which were not originally included in the panel of targeted analytes.

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“…Concerning univariate calibration, linear regression by LS has been used with the response weighted by the variance of the experimental signal. 5 Recently, [6][7][8] different weights have been suggested (1/y 2 , 1/y,1/y 0.5 ,1/x 2 ,1/x,1/x 0.5 , x being the concentration and y the signal) aiming at selecting the one that best fit the data. Nevertheless, this does not result in the univariate version of LSRE, since relative errors are not those of the concentration but those of the experimental signal.…”

Section: Introductionmentioning

confidence: 99%

Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models

Valencia

Ortiz

Sarabia

2021

Journal of Chemometrics

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Relative errors are typically used in chemometrics to evaluate the performance of a multivariate predictive model. However, these models are not obtained through the criterion of minimizing relative errors, as would be expected in a model whose response is the concentration of an analyte. There are no studies in chemometrics on the use of a principal component regression that minimizes the sum of the squares of the relative errors. This work proposes a model, which serves this purpose. The suggested model, wPCR, has been applied to 7 datasets with 12 predicted responses, 10 of which are multivariate calibrations of analytes in complex mixtures based on instrumental signals coming from various analytical techniques. As PCR and wPCR are methods seeking to optimize different criteria, each one achieves a better performance with respect to its own criterion. Therefore, the new model wPCR leads to better results insofar as the relative errors are considered, especially for the smallest responses. In this sense, the wPCR model also outperforms PCR with logarithmic transformation of the response (logPCR). In addition, as for the performance of the method using Joint Confidence Regions for the intercept and the slope of the accuracy line, it is shown that the application of wPCR does not introduce bias, neither constant nor proportional for the models built, nor a systematic alteration of the achievable accuracy.

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Effective validation of chromatographic analytical methods: The illustrative case of androgenic steroids

Cited by 32 publications

References 28 publications

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

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

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

Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models

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