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Available literature demonstrates that methadone is prone to moderate postmortem redistribution, but subject to high interindividual variability in the central to peripheral blood concentration ratios (C/P). In this case series, 10 cases of chronic methadone users displaying C/P < 1 (range 0.26–0.82) are described. Femoral, cardiac and ante-mortem blood concentrations of methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) are reported for all cases, as well as sex, age, case history, results of the pathological investigation, other toxicological findings and cause and manner of death. EDDP blood concentrations, similar in both central and peripheral blood, as well as antemortem blood concentration results in Case 4, demonstrate that this atypical C/P < 1 finding is attributable to postmortem changes and not analytical or pre-analytical artifacts. Case 4 is a particularly instructive example, with femoral blood concentration (966 ng/mL) approximately twice as high as cardiac blood (499 ng/mL) and ante-mortem blood (418 ng/mL, collected 38 min prior to death)—clearly demonstrating that cardiac blood methadone concentration is more representative of the antemortem blood concentration in this case. In Case 4 and four others, toxicological interpretation based on femoral blood concentration alone would have been misleading. Based on these results and evidence from the literature, it is hypothesized that methadone bioaccumulates in the tissues of chronic users and redistributes from thigh tissues into femoral blood, increasing the concentration postmortem. This case series highlights how femoral blood is not always preserved from postmortem changes and that the analysis of multiple blood sources is necessary to avoid a misleading toxicological interpretation—particularly for cases of chronic methadone users. [ABSTRACT FROM AUTHOR]

Over the past decades, the calls to improve the robustness of interpretation in forensic science have increased in magnitude. Forensic toxicology has seen limited progress in this regard. In this work, we propose a transparent interpretive pathway for use in postmortem forensic toxicology cases. This process allows the selection of the interpretive methodology based on the amount of previous information that is available for the drug(s) in question. One approach is an assessment of various pharmacological and circumstantial considerations resulting in a toxicological significance score (TSS), which is particularly useful in situations where limited information about a drug is available. When there is a robust amount of case data available, then a probabilistic approach, through the evaluation of likelihood ratios by the forensic toxicologist and of prior probabilities by the fact finder, is utilized. This methodology provides a transparent means of making an interpretive decision on the role of a drug in the cause of death. This will allow the field of forensic toxicology to take a step forward in using best practice in evaluative reporting, a tool already used by many other forensic science disciplines. [ABSTRACT FROM AUTHOR]

The coefficient of correlation (r) and the coefficient of determination (R 2 or r 2) have long been used in analytical chemistry, bioanalysis and forensic toxicology as figures demonstrating linearity of the calibration data in method validation. We clarify here what these two figures are and why they should not be used for this purpose in the context of model fitting for prediction. R 2 evaluates whether the data are better explained by the regression model used than by no model at all (i.e. a flat line of slope = 0 and intercept |$\bar y$|⁠), and to what degree. Hopefully, in the context of calibration curves, the fact that a linear regression better explains the data than no model at all should not be a point of contention. Upon closer examination, a series of restrictions appear in the interpretation of these coefficients. They cannot indicate whether the dataset at hand is linear or not, because they assume that the regression model used is an adequate model for the data. For the same reason, they cannot disprove the existence of another functional relationship in the data. By definition, they are influenced by the variability of the data. The slope of the calibration curve will also change their value. Finally, when heteroscedastic data are analyzed, the coefficients will be influenced by calibration levels spacing within the dynamic range, unless a weighted version of the equations is used. With these considerations in mind, we suggest to stop using r and R 2 as figures of merit to demonstrate linearity of calibration curves in method validations. Of course, this does not preclude their use in other contexts. Alternative paths for evaluation of linearity and calibration model validity are summarily presented. [ABSTRACT FROM AUTHOR]

Qualitative methods hold an important place in drug testing, filling central needs in screening and analyses, among others, linked to per se legislation. Nevertheless, the bioanalytical method validation guidelines do not discuss this type of method or describe method validation procedures ill‐adapted to qualitative methods. The output of qualitative methods are typically categorical, binary results, such as presence/absence or above cut‐off/below cut‐off. As the goal of any method validation is to demonstrate fitness for use under production conditions, qualitative validation guidelines should evaluate performance based on discrete, binary results instead of the continuous measurements obtained from the instrument (e.g. area). A tentative validation guideline for threshold qualitative methods was developed by in silico modelling of measurements and derived binary results. This preliminary guideline was applied to a liquid chromatography–tandem mass spectrometry method for 40 analytes, each with a defined threshold concentration. Validation parameters calculated from the analysis of 30 samples spiked above and below the threshold concentration (false negative rate, false positive rate, selectivity rate, sensitivity rate and reliability rate) showed a surprisingly high failure rate. Overall, 13 out of the 40 analytes were not considered validated. A subsequent examination found that this was attributable to an appreciable shift in the standard deviation of the area ratio on a day‐to‐day basis, a previously undescribed and unaccounted‐for behaviour in the qualitative threshold method validation literature. Consequently, the developed guideline was modified and used to validate a qualitative threshold method, based on the binary results for performance evaluation and incorporating measurement uncertainty. [ABSTRACT FROM AUTHOR]

In the last two decades, a large increase in opioid overdose death rates has been recorded in North America. This phenomenon, related to the misuse of prescription opioids, has been dubbed an "opioids crisis". Recent years have seen the entrance of novel synthetic opioids (NSO) on the market, compounding the fatal intoxications issue. This brings several challenges for forensic toxicology laboratories: an increased number of cases, a large number of novel structurally similar compounds to include in screening analytical methods, the low concentration of drugs in biological fluids, and the challenging interpretation in the absence of sufficient literature. Three cases of fatal intoxication highlighting those challenges are presented, complete with post-mortem concentrations in cardiac blood, femoral blood and urine. Toxicological screening and quantitative analyses were performed on the biological specimens. In the first and second cases, furanylfentanyl, U-47700 and 4-anilino-N-phenethylpiperidine (4-ANPP) were detected at similar concentrations in cardiac blood. In the third case, a total of seventeen different NSO were detected. All intoxications showed a combination of NSO and other drugs. These three cases appear to be the harbinger of an increased NSO prevalence in the province of Québec, Canada. [ABSTRACT FROM AUTHOR]

Several substances relevant for forensic toxicology purposes have an endogenous presence in biological matrices: beta-hydroxybutyric acid (BHB), gamma-hydroxybutyric acid (GHB), steroids and human insulin, to name only a few. The presence of significant amounts of these endogenous substances in the biological matrix used to prepare calibration standards and quality control samples (QCs) can compromise validation steps and quantitative analyses. Several approaches to overcome this problem have been suggested, including using an analog matrix or analyte, relying entirely on standard addition analyses for these analytes, or simply ignoring the endogenous contribution provided that it is small enough. Although these approaches side-step the issue of endogenous analyte presence in spiked matrix-matched samples, they create serious problems with regards to the accuracy of the analyses or production capacity. We present here a solution that addresses head-on the problem of endogenous concentrations in matrices used for calibration standards and quality control purposes. The endogenous analyte concentration is estimated via a standard-addition type process. This estimated concentration, plus the spiked concentration are then used as the de facto analyte concentration present in the sample. These de facto concentrations are then used in data analysis software (MultiQuant, Mass Hunter, etc.) as the sample's concentration. This yields an accurate quantification of the analyte, free from interference of the endogenous contribution. This de facto correction has been applied in a production setting on two BHB quantification methods (GC-MS and LC–MS-MS), allowing the rectification of BHB biases of up to 30 μg/mL. The additional error introduced by this correction procedure is minimal, although the exact amount will be highly method-dependent. The endogenous concentration correction process has been automated with an R script. The final procedure is therefore highly efficient, only adding four mouse clicks to the data analysis operations. [ABSTRACT FROM AUTHOR]

Calibration model selection is required for all quantitative methods in toxicology and more broadly in bioanalysis. This typically involves selecting the equation order (quadratic or linear) and weighting factor correctly modelizing the data. A mis-selection of the calibration model will generate lower quality control (QC) accuracy, with an error up to 154%. Unfortunately, simple tools to perform this selection and tests to validate the resulting model are lacking. We present a stepwise, analystindependent scheme for selection and validation of calibration models. The success rate of this scheme is on average 40% higher than a traditional "fit and check the QCs accuracy" method of selecting the calibration model. Moreover, the process was completely automated through a script (available in Supplemental Data 3) running in R Studio (free, open-source software). The need for weighting was assessed through an F-test using the variances of the upper limit of quantification and lower limit of quantification replicate measurements. When weighting was required, the choice between 1/x and 1/x2 was determined by calculating which option generated the smallest spread of weighted normalized variances. Finally, model order was selected through a partial F-test. The chosen calibration model was validated through Cramer-von Mises or Kolmogorov-Smirnov normality testing of the standardized residuals. Performance of the different tests was assessed using 50 simulated data sets per possible calibration model (e.g., linear-no weight, quadratic-no weight, linear-1/x, etc.). This first of two papers describes the tests, procedures and outcomes of the developed procedure using real LC-MS-MS results for the quantification of cocaine and naltrexone. [ABSTRACT FROM AUTHOR]

In the first part of this paper (I--Description and application), an automated, stepwise and analystindependent process for the selection and validation of calibration models was put forward and applied to two model analytes. This second part presents the mathematical reasoning and experimental work underlying the selection of the different components of this procedure. Different replicate analysis designs (intra/inter-day and intra/inter-extraction) were tested and their impact on test results was evaluated. For most methods, the use of intra-day/intra-extraction measurement replicates is recommended due to its decreased variability. This process should be repeated three times during the validation process in order to assess the time stability of the underlying model. Strategies for identification of heteroscedasticity and their potential weaknesses were examined and a unilateral F-test using the lower limit of quantification and upper limit of quantification replicates was chosen. Three different options for model selection were examined and tested: ANOVA lack-of-fit (LOF), partial F-test and significance of the second-order term. Examination of mathematical assumptions for each test and LC-MS-MS experimental results lead to selection of the partial F-test as being the most suitable. The advantages and drawbacks of ANOVA-LOF, examination of the standardized residuals graph and residuals normality testing (Kolmogorov-Smirnov or Cramer-Von Mises) for validation of the calibration model were examined with the last option proving the best in light of its robustness and accuracy. Choosing the correct calibration model improves QC accuracy, and simulations have shown that this automated scheme has a much better performance than a more traditional method of fitting with increasingly complex models until QC accuracies pass below a threshold. [ABSTRACT FROM AUTHOR]

Calculating the confidence interval is a common procedure in data analysis and is readily obtained from normally distributed populations with the familiar x¯±(t×s)/n formula. However, when working with non-normally distributed data, determining the confidence interval is not as obvious. For this type of data, there are fewer references in the literature, and they are much less accessible. We describe, in simple language, the percentile and bias-corrected and accelerated variations of the bootstrap method to calculate confidence intervals. This method can be applied to a wide variety of parameters (mean, median, slope of a calibration curve, etc.) and is appropriate for normal and non-normal data sets. As a worked example, the confidence interval around the median concentration of cocaine in femoral blood is calculated using bootstrap techniques. The median of the non-toxic concentrations was 46.7 ng/mL with a 95% confidence interval of 23.9–85.8 ng/mL in the non-normally distributed set of 45 postmortem cases. This method should be used to lead to more statistically sound and accurate confidence intervals for non-normally distributed populations, such as reference values of therapeutic and toxic drug concentration, as well as situations of truncated concentration values near the limit of quantification or cutoff of a method. [ABSTRACT FROM PUBLISHER]

Herein is described the full scope of a desulfinylative cross-coupling of aryl sulfinates with aryl bromides. Optimized conditions were established and a bidentate phosphine ligand was found to be the key in obtaining good cross-coupling yields. Preliminary efforts to elucidate the reaction mechanism suggest that a Pd(0)-catalyzed mechanism is operative. [ABSTRACT FROM AUTHOR]

The coefficient of correlation (r) and the coefficient of determination (R2 or r2) have long been used in analytical chemistry, bioanalysis and forensic toxicology as figures demonstrating linearity of the calibration data in method validation. We clarify here what these two figures are and why they should not be used for this purpose in the context of model fitting for prediction. R2 evaluates whether the data are better explained by the regression model used than by no model at all (i.e., a flat line of slope = 0 and intercept $\bar y$), and to what degree. Hopefully, in the context of calibration curves, the fact that a linear regression better explains the data than no model at all should not be a point of contention. Upon closer examination, a series of restrictions appear in the interpretation of these coefficients. They cannot indicate whether the dataset at hand is linear or not, because they assume that the regression model used is an adequate model for the data. For the same reason, they cannot disprove the existence of another functional relationship in the data. By definition, they are influenced by the variability of the data. The slope of the calibration curve will also change their value. Finally, when heteroscedastic data are analyzed, the coefficients will be influenced by calibration levels spacing within the dynamic range, unless a weighted version of the equations is used. With these considerations in mind, we suggest to stop using r and R2 as figures of merit to demonstrate linearity of calibration curves in method validations. Of course, this does not preclude their use in other contexts. Alternative paths for evaluation of linearity and calibration model validity are summarily presented., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The use of high-resolution mass spectrometry (HRMS) has increased over the past decade in clinical and forensic toxicology, especially for comprehensive screening approaches. Despite this, few guidelines in this field have specifically addressed HRMS issues concerning compound identification, validation, measurement uncertainty and quality assurance. To fully implement this technique, certainly in an era in which the quality demands for laboratories are ever-increasing due to various norms (e.g. the International Organization for Standardization's ISO 17025), these specific issues need to be addressed. This manuscript reviews 26 HRMSbased methods for qualitative systematic toxicological analysis (STA) published between 2011 and 2021. Key analytical data such as samples matrices, analytical platforms, numbers of analytes and employed mass spectral reference databases/libraries as well as the studied validation parameters are summarized and discussed. The article further includes a critical review of targeted and untargeted data acquisition approaches, available HRMS reference databases and libraries as well as current guidelines for HRMS data interpretation with a particular focus on identification criteria. Moreover, it provides an overview on current recommendations for the validation and determination of measurement uncertainty of qualitative methods. Finally, the article aims to put forward suggestions for method development, compound identification, validation experiments to be performed, and adequate determination of measurement uncertainty for this type of wide-range qualitative HRMSbased methods., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier B.V.)