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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]

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]

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]

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.)