Simultaneous screening of 239 synthetic cannabinoids and metabolites in blood and urine samples using liquid chromatography-high resolution mass spectrometry

High-performance liquid chromatography coupled to Orbitrap mass spectrometry for screening of common new psychoactive substances and other drugs in biological samples

The complexity of the drug market and the constant updating of drugs have been challenging issues for drug regulatory authorities. With the emergence of new psychoactive substances (NPS) and the nonmedical use of prescription drugs, forensic and toxicology laboratories have had to adopt new drug screening methods and advanced instrumentation. Using high-performance liquid chromatography coupled with Orbitrap mass spectrometry, we developed a screening method for common NPS and other drugs. Two milliliters of mixed solvent of n-hexane and ethyl acetate (1:1, v:v) were added to 500 μL of blood or urine sample for liquid-liquid extraction, and methanol extraction was used for hair samples. The developed method was applied to 3897 samples (including 332 blood samples, 885 urine samples, and 2680 hair samples) taken from drug addicts in a province of China during 2019-2021. For urine and blood samples, the limits of detection (LODs) ranged from 1.68 pg/mL to 10.7 ng/mL. For hair samples, the LODs ranged from 3.30 × 10-5 to 4.21 × 10-3 ng/mg. The matrix effects of urine, blood, and hair samples were in the range of 47.6%-121%, 39.8%-139%, and 6.35%-118%, respectively. And the intra-day precision was 3.5%-6.0% and the inter-day precision was 4.18%-9.90%. Analysis of the actual samples showed an overall positive detection rate of 58.9%, with 5.32% of the samples indicating the use of multiple drugs.

A critical review of workplace drug testing methods for old and new psychoactive substances: Gaps, advances, and perspectives

Workplace drug testing (WDT) is essential to prevent drug abuse disorders among the workforce because it can impair work performance and safety. However, WDT is limited by many challenges, such as urine adulteration, specimen selection, and new psychoactive substances (NPS). This review examined the issues related to WDT. Various scientific databases were searched for articles on WDT for drug detection published between 1986 (when WDT started) and January 2024. The review discussed the history, importance, and challenges of WDT, such as time of specimen collection/testing, specimen adulteration, interference in drug testing, and detection of NPS. It evaluated the best methods to detect NPS in forensic laboratories. Moreover, it compared different techniques that can enhance WDT, such as immunoassays, targeted mass spectrometry, and nontargeted mass spectrometry. These techniques can be used to screen for known and unknown drugs and metabolites in biological samples. This review assessed the strengths and weaknesses of such techniques, such as their validation, identification, library search, and reference standards. Furthermore, this review contrasted the benefits and drawbacks of different specimens for WDT and discussed studies that have applied these techniques for WDT. WDT remains the best approach for preventing drug abuse in the workplace, despite the challenges posed by NPS and limitations of the screening methods. Nontargeted techniques using high-resolution liquid chromatography-mass spectrometry (MS)/gas chromatography-tandem MS can improve the detection and identification of drugs during WDT and provide useful information regarding the prevalence, trends, and toxicity of both traditional and NPS drugs. Finally, this review suggested that WDT can be improved by using a combination of techniques, multiple specimens, and online library searches in case of new NPS as well as by updating the methods and databases to include new NPS and metabolites as they emerge. To the best of the author's knowledge, this is the first review to address NPS as an issue in WDT and its application and propose the best methods to detect these substances in the workplace environment.

Computer-assisted multifactorial method development for the streamlined separation and analysis of multicomponent mixtures in (Bio)pharmaceutical settings

The (bio)pharmaceutical industry is rapidly moving towards complex drug modalities that require a commensurate level of analytical enabling technologies that can be deployed at a fast pace. Unsystematic method development and unnecessary manual intervention remain a major barrier towards a more efficient deployment of meaningful analytical assay across emerging modalities. Digitalization and automation are key to streamline method development and enable rapid assay deployment. This review discusses the use of computer-assisted multifactorial chromatographic method development strategies for fast-paced downstream characterization and purification of biopharmaceuticals. Various chromatographic techniques such as reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC), and supercritical fluid chromatography (SFC) are addressed and critically reviewed. The most significant parameters for retention mechanism modelling, as well as mapping the separation landscape for optimal chromatographic selectivity and resolution are also discussed. Furthermore, several computer-assisted approaches for optimization and development of chromatographic methods of therapeutics, including linear, nonlinear, and multifactorial modelling are outlined. Finally, the potential of the chromatographic modelling and computer-assisted optimization strategies are also illustrated, highlighting substantial productivity improvements, and cost savings while accelerating method development, deployment and transfer processes for therapeutic analysis in industrial settings.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach

Background

Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data.

Methods

In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods.

Results

The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R² value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%.

Conclusions

Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.