Green Analytical Toxicology procedure for determination of ketamine, its metabolites and analogues in oral fluid samples using dispersive liquid-liquid microextraction (DLLME).

Abstract

New psychoactive substances (NPS) are often synthesized via small changes in the molecular structure, producing drugs whose effect and potency are not yet fully known. Ketamine is one of the oldest NPS, with therapeutic use in human and veterinary medicine authorized in several countries, being metabolized mainly into norketamine and 6-hydroxy-norketamine. Furthermore, two structural analogues of ketamine have recently been identified, deschloroketamine and 2-fluorodeschloroketamine, marketed as drugs of abuse. To comply with Green Analytical Toxicology (GAT) fundamentals, miniaturized techniques such as dispersive liquid-liquid microextraction (DLLME) were employed to determine toxicants in biological fluids. An analytical method for determining ketamine, its metabolites and its analogues in oral fluid was fully developed and validated by using DLLME and liquid chromatography-tandem mass spectrometry (LC-MS-MS). The extraction parameters were optimized by multivariate analysis, obtaining the best conditions with 200 μL of sample, 100 μL of methanol as dispersive solvent and 50 μL of chloroform as extractor solvent. Linearity was obtained from 10 to 1,000 ng/mL, with limit of detection (LOD) and lower limit of quantification (LLOQ) at 10 ng/mL. Imprecision (% relative standard deviation) and bias (%) were less than 8.2% and 9.5%, respectively. The matrix effect did not exceed 10.6%, and the recovery values varied from 24% to 42%. No matrix interference and good selectivity in the evaluation of 10 different sources of oral fluid and 42 drugs at 500 ng/mL, respectively, were observed. The method was applied in the analysis of 29 authentic oral fluid samples and had its green characteristic evaluated by three different tools: the Green Analytical Procedure Index (GAPI), the Analytical Eco-Scale and the Analytical GREEnness (AGREE) metrics.