Abstract
An LC–MS-MS method for the determination of 14 benzodiazepines (BZDs) (alprazolam, α-hydroxyalprazolam, clonazepam, bromazepam, diazepam, nordiazepam, lorazepam, lormetazepam, oxazepam, flunitrazepam, 7-aminoflunitrazepam, triazolam, midazolam and zolpidem) and 15 antidepressants (ADs) (amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, norclomipramine, fluoxetine, norfluoxetine, sertraline, norsertraline, paroxetine, venlafaxine, desmethylvenlafaxine, citalopram and desmethylcitalopram) in meconium was developed and validated. Meconium samples (0.25 ± 0.02 g) were homogenized in methanol and subjected to mixed-mode cation exchange solid-phase extraction. Chromatographic separation was performed in reversed phase, with a gradient of 0.1% formic acid in 2 mM ammonium formate and acetonitrile. Two different chromatographic gradient methods were employed, one for the separation of ADs and another for BZDs. Analytes were monitored by tandem mass spectrometry employing electrospray positive mode in MRM mode (2 transitions per compound). Method validation included: linearity [n = 5, limit of quantification (LOQ) to 400 ng/g], limits of detection (n = 6, 1–20 ng/g), LOQ (n = 9, 5–20 ng/g), selectivity (no endogenous or exogenous interferences), accuracy (n = 15, 90.6–111.5%), imprecision (n = 15, 0–14.6%), matrix effect (n = 10, −73 to 194.9%), extraction efficiency (n = 6, 35.9–91.2%), process efficiency (n = 6, 20.1–188.2%), stability 72 h in the autosampler (n = 3, −8.5 to 9%) and freeze/thaw stability (n = 3, −1.2 to −47%). The method was applied to four meconium specimens, which were analyzed with and without hydrolysis (enzymatic and alkaline). The authentic meconium samples tested positive for alprazolam, α-hydroxyalprazolam, clonazepam, diazepam, nordiazepam, fluoxetine, norfluoxetine, clomipramine and norclomipramine. Therefore, the present LC–MS-MS method allows a high throughput determination of the most common BZDs and ADs in meconium, which could be useful in clinical and forensic settings.
Gradient Methods for Problems with Inexact Model of the Objective
