Software Assisted Metabolite Identification – A Tool for the Rapid Updating of Screening Methods for Synthetic Cannabinoids in Human Urine by LC QToF-MS

Dirk Wunderlich, Bruker Daltonik GmbH, Bremen, Germany
Laura M. Huppertz, Institute of Forensic Medicine, Forensic Toxicology, Medical Center – University of Freiburg, Germany
Bjoern Moosmann, Institute of Forensic Medicine, Forensic Toxicology, Medical Center – University of Freiburg, Germany
Florian Franz, Institute of Forensic Medicine, Forensic Toxicology, Medical Center – University of Freiburg, Germany
Sebastian Götz, Bruker Daltonik GmbH, Bremen, Germany
Volker Auwärter, Institute of Forensic Medicine, Forensic Toxicology, Medical Center – University of Freiburg, Germany
Martina Macht, Bruker Daltonik GmbH, Germany

Introduction
Synthetic cannabinoids (SC) pose a great challenge for the forensic field. Perceived as a legal alternative to cannabis a growing number of SCs is available. Consequently, analytical methods have to be adapted frequently to include newly emerging compounds. Since most SCs are metabolized extensively prior to excretion, metabolite identification is inevitable for urine analysis.

The presented workflow allows identification of the major in vitro phase I metabolites of new compounds by using dedicated software to mine analytical data of pooled human liver microsome (pHLM) incubations. The identified metabolites along with their MS/MS fragment information are utilized to update analytical methods for the reliable identification of SC uptake in human urine samples.

Methods
MDMB-CHMICA, a highly potent and prevalent synthetic cannabinoid, was chosen as a model compound and pHLM incubated. LC-QToF-MS analysis was performed in positive ESI mode using data-dependent MSMS fragmentation. MassMetaSite software (Molecular Discovery) was used to analyze LC-MS/MS datasets of the incubations sampled at time points zero and one hour. Precursor and fragment information of the identified metabolites were used to create a screening method. Authentic urine samples were screened using data independent broad-band-CID mode to enable retrospective data evaluation.

Results
Analysis of the pHLM incubations of MDMB-CHMICA with MassMetaSite software revealed 10 metabolites with at least two fragment masses each. By manual data analysis 15 potential metabolites were identified. However, all main in vitro phase I metabolites were detected with the software. The metabolite and fragment information was used to generate a screening method in Bruker TASQ software. Authentic forensic case samples were screened and processed with the updated TASQ method. Despite varying relative abundances of the detected metabolites, the in vitro and in vivo data showed good agreement with respect to the chosen MDMB-CHMICA metabolites