Application of a UPLC-MS/MS method for quantitative analysis of 29 synthetic cannabinoids and their metabolites, such as ADB-BUTINACA and MDMB-4en-PINACA in human hair in real cases

In recent years, an increasing number of new synthetic cannabinoids have appeared on the drug trade market. Many of the new synthetic cannabinoids have not previously been reported. At present, there are relatively few methods available for detecting synthetic cannabinoids and their metabolites in hair matrices. Therefore, we established a simple and fast method to simultaneously identify 29 synthetic cannabinoids and their metabolites in human hair by UPLC-MS/MS. Twenty milligrams of hair was used and processed by cryo-grinding and extraction with methanol. A Waters Acquity UPLC HSS T3 column (100 mm x 2.1 mm, 1.8 mu m) was used for chromatographic separation. Mobile phase A was comprised of 20 mmol/L ammonium acetate, 0.1% formic acid, and 5% acetonitrile and water, and mobile phase B was acetonitrile. The method was fully validated and proved to have good selectivity, accuracy, precision, and satisfactory linearity within the calibrated range. The limit of detection (LOD) ranged from 0.5 to 5 pg/mg, and the lower limit of quantitation (LLOQ) ranged from 1 to 10 pg/mg. The extraction recovery was 36.1-93.3%, and the matrix effect was 19.1-110.0%. The validated method was successfully used to qualitatively and quantitatively analyze 29 synthetic cannabinoids and their metabolites in 59 actual hair samples. MDMB-4en-PINACA had the highest positive detection rate followed by ADB-BUTINACA, and there are multiple synthetic cannabinoid mixed ingestions. This methodology has great potential for the detection of 29 synthetic cannabinoids and their metabolites in forensic cases. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study established a fast and efficient method to simultaneously identify 29 synthetic cannabinoids and their metabolites in hair samples. The method was successfully validated in actual samples and showed good selectivity, accuracy, and precision.