Validation of a fully automated solid‐phase extraction and ultra‐high‐performance liquid chromatography–tandem mass spectrometry method for quantification of 30 pharmaceuticals and metabolites in post‐mortem blood and brain samples

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In this study, we present the validation of an analytical method capable of quantifying 30 commonly encountered pharmaceuticals and metabolites in whole blood and brain tissue from forensic cases. Solid‐phase extraction was performed by a fully automated robotic system, thereby minimising manual labour and human error while increasing sample throughput, robustness, and traceability. The method was validated in blood in terms of selectivity, linear range, matrix effect, extraction recovery, process efficiency, carry‐over, stability, precision, and accuracy. Deuterated analogues of each analyte were used as internal standards, which corrected adequately for any inter‐individual variability in matrix effects on analyte accuracy and precision. The lower limit of quantification (LLOQ) spanned from 0.0008 to 0.010 mg/kg, depending on the analyte, while the upper LOQ ranged between 0.40 and 2.0 mg/kg. Thus, the linear range covered both therapeutic and toxic levels. The method showed acceptable accuracy and precision, with accuracies ranging from 80 to 118% and precision below 19% for the majority of the analytes. Linear range, matrix effect, extraction recovery, process efficiency, precision, and accuracy were also tested in brain homogenate and the results agreed with those from blood. An additional finding was that the analyte concentrations in brain samples could be quantified by calibration curves obtained from spiked blood samples with acceptable precision and accuracy when using deuterated analogues of each analyte as internal standards. This method has been successfully implemented as a routine analysis procedure for quantification of pharmaceuticals in both blood and brain tissue since 2015.
Original languageEnglish
JournalDrug Testing and Analysis
Issue number7
Pages (from-to)1147-1157
Publication statusPublished - Jul 2018

ID: 193661272