Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

Sensitive Screening of New Psychoactive Substances in Serum Using Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry.

Analysis of new psychoactive substances (NPS) still poses a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study we developed a qualitative screening approach for serum samples by means of liquid chromatography--quadrupole time-of-flight mass spectrometry. Samples were measured in data-dependent auto tandem mass spectrometry mode and identified by fragment spectra comparison, retention time and accurate mass. Approximately 500 NPS, including 195 synthetic cannabinoids, 180 stimulants, 86 hallucinogens, 26 benzodiazepines and 7 others were investigated. Serum samples were fortified to 1 ng/mL and 10 ng/mL concentrations to estimate approximate limits of identification (LOIs). Samples were extracted using solid-phase extraction with non-endcapped C18 material and elution in two consecutive steps. Benzodiazepines were eluted in the first step, while substances of other NPS subclasses were distributed among both extracts. To determine LOIs, both extracts were combined. Ninety-six percent (470/492) of investigated NPS were detected in 10 ng/mL samples and 88% (432/492) were detected in 1 ng/mL samples. Stimulants stood out with higher LOIs, possibly due to instability of certain methcathinone derivatives. However, considering relevant blood concentrations, the method provided sufficient sensitivity for stimulants as well as other NPS subclasses. Data-dependent acquisition was proven to provide high sensitivity and reliability when combined with an information-dependent preferred list, without losing its untargeted operation principle. Summarizing, the developed method fulfilled its purpose as a sensitive untargeted screening for serum samples and allows uncomplicated expansion of the spectral library to include thousands of targets.

Sensitive screening of synthetic cannabinoids using liquid chromatography quadrupole time-of-flight mass spectrometry after solid phase extraction.

Analysis of synthetic cannabinoids still poses a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study, we developed a qualitative screening approach for synthetic cannabinoids and their metabolites by means of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were measured in data-dependent auto-MS/MS mode and identified by fragment spectra, retention time and accurate mass. Two established solid phase extractions were compared using fortified serum and urine samples. Mixes of 199 synthetic cannabinoids and 110 metabolites were used in 1- and 10-ng/ml concentrations. Up to 93% of synthetic cannabinoids and 74% of metabolites were detected in fortified 1-ng/ml samples. From February 2018 to October 2020, we analyzed 1492 cases, of which 73 cases were positive for synthetic cannabinoids or metabolites. 5F-MDMB-PICA, 4F-MDMB-BINACA, MDMB-4en-PINACA, and 4F-MDMB-BICA were most frequently detected. Hydrolysis metabolites were detected in many blood samples, providing a longer detection window. Quantification was conducted via liquid chromatography triple quadrupole mass spectrometry after liquid-liquid extraction. Concentrations were mostly close to 1 ng/ml in blood samples. LC-QTOF-MS was able to detect substances above trace quantities (< 0.1 ng/ml) in most cases, therefore fulfilling its purpose as a sensitive general screening approach. Expansion of the screening library was uncomplicated and enables future additions for up to thousands of targets.