Improvement of drug identification in urine by LC-QqTOF using a probability-based library search algorithm

Nontargeted screening method for detection of illicit adulterants in dietary supplements and herbal medicines using UHPLC-QTOF-MS with fine-tuned Spec2Vec-based spectral similarity and chemical classification filter

Liquid chromatography-mass spectrometry (LC-MS) is a widely utilized technique for inspecting adulteration. Unscrupulous businesses persistently introduce novel illegal adulterants, making it necessary to develop methods to screen compounds not present in the current library. Conventional cosine similarity for mass spectral libraries matching is limited in their ability to identify structurally similar compounds. In our previous study, comparison of performance among four advanced similarity algorithms revealed that Spec2Vec exhibited the best performance in terms of both detection capability and false discovery rate, making it the chosen method for identifying illegal adulterants. However, Spec2Vec still exhibited worse performance compared to MS2DeepScore and entropy similarity in the aspects of detection capability and false discovery rate, respectively. In this study, our objective was to optimize the performance of spectral similarity for a specific compound class by fine-tuning a pretrained Spec2Vec model. Additionally, we implemented the chemical classification tool CANOPUS to address the issue of similarities in backbone structures between illegal adulterants and compounds found in herbal medicine, which can lead to false positives. We utilized glucocorticoids as potentially illicit adulterants to provide a proof-of-concept, and the results demonstrated that the fine-tuned Spec2Vec model not only exhibits a significant improvement in detection ability compared to the original model but also achieves comparable performance to MS2Deepscore. Moreover, the fine-tuned Spec2Vec model shows notably fewer false positives in comparison to MS2Deepscore. Overall, this proposed pipeline demonstrates high effectiveness and competitiveness in inspecting illegal adulterants, enhancing the analysis of large-scale MS data.

Quadrupole Time-of-Flight Mass Spectrometry: A Paradigm Shift in Toxicology Screening Applications

The screening of biological samples for the presence of illicit or legal substances is an important frontline tool in both clinical and forensic toxicology. In the clinical setting, drug screening is a useful tool for the clinician in improving patient care and guiding treatment. Analytical approaches for the screening of drugs in biological samples are extensive and well documented, though many rapid screening techniques often lack appropriate sensitivity and specificity, requiring careful clinical interpretation. The continuous emergence of new psychoactive substances presents a considerable analytical challenge in maintaining up-to-date methods for the detection of relevant drugs. Adapting and validating methods for the detection of new substances can be a complicated and costly undertaking. There is also a considerable lag time between the emergence of new drugs and the release of commercial assays for detection. Quadrupole time-of-flight mass spectrometry (Q-TOF-MS) has gained considerable attention over the last decade as an analytical technique that is capable of meeting the challenges of a rapidly changing drug landscape. Exhibiting both high sensitivity and specificity in drug detection, Q-TOF-MS also allows methods to be rapidly updated for newly emerging psychoactive agents. The coupling of Q-TOF-MS with techniques such as liquid or gas chromatography can provide both rapid and comprehensive screening solutions that are gaining popularity in the clinical laboratory setting.

Prioritizing potential endocrine active high resolution mass spectrometry (HRMS) features in Minnesota lakewater

Liquid chromatography high-resolution mass spectrometry (LC-HRMS) shows great potential for expanding our understanding of relevant unknown chemical components present within complex environmental mixtures. This study identified potentially endocrine active components within Minnesota lakewater by prioritizing LC-HRMS features uniquely present at sunfish spawning habitats where male fish showed signs of estrogen agonism. Porewater samples from four locations within the same lake were analyzed using liquid chromatography quadrupole time of flight mass spectrometry (LC-QToF/MS) with positive (ESI+) and negative (ESI-) electrospray ionization. Plasma vitellogenin concentrations of captured male sunfish was used to designate sites as either endocrine active (ACT; 2 sites) or reference (REF; 2 sites). Assuming unique chemical presence at active sites contributed to endocrine activity, features at significantly higher intensities (p-value < 0.05, t-value > t-critical, log-fold change > 0.1; equal variance t-test of log2 transformed data) in ACT sites were then compiled into a suspect search list for feature identification. Adducts and isotopes of prioritized features were deprioritized using pattern recognizing algorithms using mass, retention time, and intensity. Feature identities were reported according to established confidence metrics using spectral libraries and elemental composition algorithms. This LC-HRMS approach identified a number of features omitted by targeted analysis with higher relative abundances in ACT sites, including plant essential oils, fatty acids, and mycotoxins. Multivariate analysis determined whether features were either present at both sites (AB) or unique to individual ACT sites (A or B). Detection frequency across datasets indicated bias in feature prioritization influenced by the chosen sampling method and sample acquisition mode. The majority of features prioritized by this workflow remain tentatively identified or unidentified masses of interest, reflective of current limitations in shared spectral libraries for soft ionization analyses. Strategies similar to this workflow have the potential to reduce bias in database-driven toxicological prioritization frameworks.

Suspect Screening Using LC-QqTOF Is a Useful Tool for Detecting Drugs in Biological Samples

High-resolution mass spectrometers (HRMS), including quadrupole time of flight mass analyzers (QqTOF), are becoming more prevalent as screening tools in clinical and forensic toxicology laboratories. Among other advantages, HRMS instruments can collect untargeted, full-scan mass spectra. These datasets can be analyzed retrospectively using a combination of techniques, which can extend the drug detection capabilities. Most laboratories using HRMS in production settings perform untargeted data collection, but analyze data in a targeted manner. To perform targeted analysis, a laboratory must first analyze a reference standard to determine the expected characteristics of a given compound. In an alternate technique known as suspect screening, compounds can be tentatively identified without the use of reference standards. Instead, predicted and/or intrinsic characteristics of a compound, such as the accurate mass, isotope pattern, and product ion spectrum are used to determine its presence in a sample. The fact that reference standards are not required a priori makes this data analysis approach very attractive, especially for the ever-changing landscape of novel psychoactive substances. In this work, we compared the performance of four data analysis workflows (targeted and three suspect screens) for a panel of 170 drugs and metabolites, detected by LC-QqTOF. We found that retention time was not required for drug identification; the suspect screen using accurate mass, isotope pattern, and product ion library matching was able to identify more than 80% of the drugs that were present in human urine samples. We showed that the inclusion of product ion spectral matching produced the largest decrease in false discovery and false negative rates, as compared to suspect screening using mass alone or using just mass and isotope pattern. Our results demonstrate the promise that suspect screening holds for building large, economical drug screens, which may be a key tool to monitor the use of emerging drugs of abuse, including novel psychoactive substances.