Molecular Networking: A Useful Tool for the Identification of New Psychoactive Substances in Seizures by LC-HRMS

Characterization of banned colorants in cosmetics: A tandem mass-based molecular networking approach

Colorants have been a staple in the cosmetics industry for a considerable time, although certain varieties have been banned owing to health risks. Detecting and confirming these banned colorants simultaneously poses several challenges when employing LC-MS/MS. Molecular networking is a promising analytical technology that can be used to predict the structure of components and the correlation between them using structural and MS/MS spectral similarities. Molecular networking entails assessing the number of fragmented ions and the cosine score (the closer it is to one, the higher the similarity). In this study, we developed and verified a method for the simultaneous quantitative analysis of the 26 banned colorants in cosmetics using LC-MS/MS. Additionally, we propose a novel approach that combines LC-Q-TOF-MS and molecular networking technology to detect banned colorants in cosmetics. For successful molecular networking, a minimum of six fragment ions with cosine scores exceeding 0.5 is required. We developed a screening method for characterizing banned colorants using molecular networking based on LC-TOF-MS results for 26 banned colorants. Furthermore, we demonstrated that our established method can be used for screening by analyzing actual cosmetics (eyebrow tattoo, lipstick tattoo, and hair tint) spiked with three non-targeted banned colorants with similar structures (m/z 267.116, 315.149, and 345.157) in cosmetics. The combination of molecular networking techniques and LC-MS/MS proves highly advantageous for the swift characterization and screening of non-targeted colorants in cosmetics.

Charting the Cannabis plant chemical space with computational metabolomics

INTRODUCTION

The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.

OBJECTIVES

Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.

METHODS

Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).

RESULTS

The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.

CONCLUSION

These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

Target and suspect screening of psychoactive substances in seizures and oral fluid exploiting retention time prediction and LC-MS/MS analysis

BACKGROUND

Novel psychoactive substances (NPS) are a group of substances, mainly of synthetic origin, characterized by toxicological properties extremely dangerous. The main difficulty in recognizing NPS in seizures and biological samples lies in their dynamic nature, related to the continuous synthesis and introduction on the market of new drugs, often with very similar structures to existing ones. The aim of this study was the creation of a robust and versatile method for the analysis of traditional drugs and NPS in different matrices.

RESULTS

Both target analysis and suspect screening methodologies were developed. The strategy used for suspect screening allowed to collect data through a scheduled multi reaction monitoring (sMRM) survey which triggered the collection of enhanced product ion (EPI) spectra when a compound met information dependent acquisition (IDA) criteria. The retention time of the different drugs, which was crucial to define the sMRM survey scan parameters, was predicted with a Quantitative Structure Retention (Chromatographic) Relationship (QSRR) model by Multiple Linear Regression. The model was validated through the evaluation of training set predictions, an external validation set and a leave-one out strategy; the results showed that the method fit for its purpose. The target method was validated in oral fluid as a testing matrix, with excellent results in term of recovery, accuracy, precision and matrix effect. Finally, the performances of the suspect method were evaluated by analysing a mixture containing 8 reference standards not included in the initial dataset, as well as seizures and real oral fluid samples. Four NPS were putatively identified in the analysed samples.

SIGNIFICANCE

The advantage of the proposed approach is the possibility of quantifying 65 classical drugs of abuse and NPS and, at the same time, detect and putatively identify 146 additional drugs in one single LC-MS/MS run. This is an innovative strategy for multi analyte detection and enables detection of low concentrations of drugs in complex biological matrices such as oral fluid. Considering the highly dynamic drug market, a strength of this strategy is that the analytical method can be kept up to date through the addition of new compounds based on the last drug monitoring bodies alerts without the need of authentic standards.

Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models

Novel psychoactive substances (NPS) represent a broad class of drugs new to the illicit market that often allow passing drug-screening tests. They are characterized by a variety of structures, rapid transience on the drug scene and mostly unknown metabolic profiles, thus creating an ever-changing scenario with evolving analytical targets. The present study aims at developing an indirect screening strategy for NPS monitoring, and specifically for new synthetic opioids (NSOs), based on assessing changes in endogenous urinary metabolite levels as a consequence of the systemic response following their intake. The experimental design involved in-vivo mice models: 16 animals of both sex received a single administration of morphine or fentanyl. Urine was collected before and after administration at different time points; the samples were then analysed with an untargeted metabolomics LC-HRMS workflow. According to our results, the intake of opioids resulted in an elevated energy demand, that was more pronounced on male animals, as evidenced by the increase in medium and long chain acylcarnitines levels. It was also shown that opioid administration disrupted the pathways related to catecholamines biosynthesis. The observed alterations were common to both morphine and fentanyl: this evidence indicate that they are not related to the chemical structure of the drug, but rather on the drug class. The proposed strategy may reinforce existing NPS screening approaches, by identifying indirect markers of drug assumption.

Exploring Metabolic Pathways of Anamorelin, a Selective Agonist of the Growth Hormone Secretagogue Receptor, via Molecular Networking

In this study, we delineated the poorly characterized metabolism of anamorelin, a growth hormone secretagogue receptor agonist, in vitro using human liver microsomes (HLM), based on classical molecular networking (MN) and feature-based molecular networking (FBMN) from the Global Natural Products Social Molecular Networking platform. Following the in vitro HLM reaction, the MN analysis showed 11 neighboring nodes whose information propagated from the node corresponding to anamorelin. The FBMN analysis described the separation of six nodes that the MN analysis could not achieve. In addition, the similarity among neighboring nodes could be discerned via their respective metabolic pathways. Collectively, 18 metabolites (M1-M12) were successfully identified, suggesting that the metabolic pathways involved were demethylation, hydroxylation, dealkylation, desaturation, and N-oxidation, whereas 6 metabolites (M13a*-b*, M14a*-b*, and M15a*-b*) remained unidentified. Furthermore, the major metabolites detected in HLM, M1 and M7, were dissimilar from those observed in the CYP3A4 isozyme assay, which is recognized to be markedly inhibited by anamorelin. Specifically, M7, M8, and M9 were identified as the major metabolites in the CYP3A4 isozyme assay. Therefore, a thorough investigation of metabolism is imperative for future in vivo studies. These findings may offer prospective therapeutic opportunities for anamorelin.

Metabolomics and molecular networking approach for exploring the anti-diabetic activity of medicinal plants

Metabolomics and molecular networking approaches have expanded rapidly in the field of biological sciences and involve the systematic identification, visualization, and high-throughput characterization of bioactive metabolites in natural products using sophisticated mass spectrometry-based techniques. The popularity of natural products in pharmaceutical therapies has been influenced by medicinal plants with a long history of ethnobotany and a vast collection of bioactive compounds. Here, we selected four medicinal plants Cleistocalyx operculatus, Terminalia chebula, Ficus lacor, and Ficus semicordata, the biochemical characteristics of which remain unclear owing to the inherent complexity of their plant metabolites. In this study, we aimed to evaluate the potential of these aforementioned plant extracts in inhibiting the enzymatic activity of α-amylase and α-glucosidase, respectively, followed by the annotation of secondary metabolites. The methanol extract of Ficus semicordata exhibited the highest α-amylase inhibition with an IC50 of 46.8 ± 1.8 μg mL-1, whereas the water fraction of Terminalia chebula fruits demonstrated the most significant α-glucosidase inhibition with an IC50 value of 1.07 ± 0.01 μg mL-1. The metabolic profiling of plant extracts was analyzed through Liquid Chromatography-Mass Spectrometry (LC-HRMS) of the active fractions, resulting in the annotation of 32 secondary metabolites. Furthermore, we applied the Global Natural Product Social Molecular Networking (GNPS) platform to evaluate the MS/MS data of Terminalia chebula (bark), revealing that there were 205 and 160 individual ion species observed as nodes in the methanol and ethyl acetate fractions, respectively. Twenty-two metabolites were tentatively identified from the network map, of which 11 compounds were unidentified during manual annotation.

A Systematic Review of Polycyclic Aromatic Hydrocarbon Derivatives: Occurrences, Levels, Biotransformation, Exposure Biomarkers, and Toxicity

Polycyclic aromatic hydrocarbon (PAH) derivatives constitute a significant class of emerging contaminants that have been ubiquitously detected in diverse environmental matrixes, with some even exhibiting higher toxicities than their corresponding parent PAHs. To date, compared with parent PAHs, fewer systematic summaries and reanalyses are available for PAH derivatives with great environmental concerns. This review summarizes the current knowledge on the chemical species, levels, biotransformation patterns, chemical analytical methods, internal exposure routes with representative biomarkers, and toxicity of PAH derivatives, primarily focusing on nitrated PAHs (NPAHs), oxygenated PAHs (OPAHs), halogenated PAHs (XPAHs), and alkylated PAHs (APAHs). A collection of 188 compounds from four categories, 44 NPAHs, 36 OPAHs, 56 APAHs, and 52 XPAHs, has been compiled from 114 studies that documented the environmental presence of PAH derivatives. These compounds exhibited weighted average air concentrations that varied from a lower limit of 0.019 pg/m3 to a higher threshold of 4060 pg/m3. Different analytical methods utilizing comprehensive two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC × GC-TOF-MS), gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS), comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC-QQQ-MS), and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), that adopted untargeted strategies for the identification of PAH derivatives are also reviewed here. Additionally, an in-depth analysis of biotransformation patterns for each category is provided, including the likelihood of specific biotransformation reaction types. For the toxicity, we primarily summarized key metabolic activation pathways, which could result in the formation of reactive metabolites capable of covalently bonding with DNA and tissue proteins, and potential health outcomes such as carcinogenicity and genotoxicity, oxidative stress, inflammation and immunotoxicity, and developmental toxicity that might be mediated by the aryl hydrocarbon receptor (AhR). Finally, we pinpoint research challenges and emphasize the need for further studies on identifying PAH derivatives, tracking external exposure levels, evaluating internal exposure levels and associated toxicity, clarifying exposure routes, and considering mixture exposure effects. This review aims to provide a broad understanding of PAH derivatives' identification, environmental occurrence, human exposure, biotransformation, and toxicity, offering a valuable reference for guiding future research in this underexplored area.

A Relook into the Flavonoid Chemical Space of Moringa oleifera Lam. Leaves through a Combination of LC-MS and Molecular Networking

Moringa oleifera Lam. is a functional tree that is known to produce a variety of metabolites with purported pharmacological activities. It is frequently called the "miracle tree" due to its utilization in numerous nutraceutical and pharmacological contexts. This study was aimed at studying the chemical space of M. oleifera leaf extracts through molecular networking (MN), a tool that identifies metabolites by classifying them based on their MS-based fragmentation pattern similarities and signals. In this case, a special emphasis was placed on the flavonoid composition. The MN unraveled different molecular families such as flavonoids, carboxylic acids and derivatives, lignin glycosides, fatty acyls, and macrolactams that are found within the plant. In silico annotation tools such as network annotation propagation (NAP) and DEREPLICATOR, an unsupervised substructure identification tool (MS2LDA), and MolNet enhancer were also explored to further compliment the classic molecular networking output within the Global Natural Product Social (GNPS) site. In this study, common flavonoids found within Moringa oleifera were further annotated using MS2LDA. Utilizing computational tools allowed for the discovery of a wide range of structurally diverse flavonoid molecules within M. oleifera leaf extracts. The expansion of the flavonoid chemical repertoire in this plant arises from intricate glycosylation modifications, leading to the creation of structural isomers that manifest as isobaric ions during mass spectrometry (MS) analyses.

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials

While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. We report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of "unknown unknown" trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.

Computation-aided studies related to the induction of specialized metabolite biosynthesis in microbial co-cultures: An introductory overview

Co-cultivation is an effective method of inducing the production of specialized metabolites (SMs) in microbial strains. By mimicking the ecological interactions that take place in natural environment, this approach enables to trigger the biosynthesis of molecules which are not formed under monoculture conditions. Importantly, microbial co-cultivation may lead to the discovery of novel chemical entities of pharmaceutical interest. The experimental efforts aimed at the induction of SMs are greatly facilitated by computational techniques. The aim of this overview is to highlight the relevance of computational methods for the investigation of SM induction via microbial co-cultivation. The concepts related to the induction of SMs in microbial co-cultures are briefly introduced by addressing four areas associated with the SM induction workflows, namely the detection of SMs formed exclusively under co-culture conditions, the annotation of induced SMs, the identification of SM producer strains, and the optimization of fermentation conditions. The computational infrastructure associated with these areas, including the tools of multivariate data analysis, molecular networking, genome mining and mathematical optimization, is discussed in relation to the experimental results described in recent literature. The perspective on the future developments in the field, mainly in relation to the microbiome-related research, is also provided.

Metabolome Mining of Curcuma longa L. Using HPLC-MS/MS and Molecular Networking

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world's tropical and subtropical climates, especially in South Asia. Its rhizomes have been highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by ESI-MS and low-energy tandem mass spectrometry analyses. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. To sum it up, a total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among the 16 diarylheptanoids identified in this study, 5 of them are reported for the first time in this species.

Unearthed opium: development of a UHPLC-MS/MS method for the determination of Papaver somniferum alkaloids in Daunian vessels

Introduction: The analysis of organic residue in ancient vessels to investigate early-age civilization habits is an important archeological application that needs advanced analytical methods. However, these procedures should meet inherent requisites such as low sampling invasiveness and high sensitivity for trace analysis. This study deals with the development of advanced analytical methods for the detection of opium alkaloids in ceramic vessels and its first application to the study of Daunian pots dating back to the VIII-IV sec BC. Methods: All the stages of the analytical procedure, from sampling to analysis, were carefully optimized. Concerning sampling, the traditional scraping approach was compared with a swabbing strategy which permitted minimizing sample encroachment. Extraction was based on pressurized liquid extraction or ultrasound-assisted liquid extraction, followed by dispersive liquid-liquid microextraction, which allowed concentration enrichment. On the other hand, a UHPLC-MS/MS method was specifically developed and validated to obtain reliable data. Some Daunian pots, belonging to the Ceci-Macrini private archeological collection, were selected for sample withdrawal as their iconography could suggest opium usage. Results: Several of the analyzed samples resulted positive to thebaine and less frequently to morphine and codeine; furthermore, 70% of the analyzed items tested positive for at least one opium alkaloid. Positive findings were common to all the samples collected in the pots, suggesting that scraping and swabbing provided comparable results and validating this unusual sampling strategy. All samples were additionally analyzed by UHPLC-HRMS to further improve the confidence level of the identified compounds. The obtained results shed new light on the hypothesis of opium usage by the ancient Daunian civilization. Furthermore, this study provided suitable analytical tools for further investigations on the same topic, with a good level of confidence in the quality of the results.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

Multiple approaches to characterize and visualize the chemical composition of Sijunzi Decoction comprehensively

Sijunzi Decoction is composed of Ginseng Radix et Rhizoma, Atractylodes Macrocephalae Rhizoma, Poria, and Glycyrrhizae Radix Et Rhizoma Praeparata Cum Melle, and it is a classic formula for treating spleen deficiency syndrome in Chinese medicine. Clarifying the active substances is an effective way to develop Traditional Chinese medicine and innovative medicines. Carbohydrates, proteins, amino acids, saponins, flavonoids, phenolic acids, and inorganic elements in the decoction were analyzed by multiple approaches. A molecular network was also used for visualizing the ingredients in Sijunzi Decoction, and representative components were also quantified. The detected components accounted for 74.544% of the Sijunzi Decoction freeze-dried powder, including 41.751% crude polysaccharides, 17.826% sugars (degree of polymerization 1-2), 8.181% total saponins, 2.427% insoluble precipitates, 2.154% free amino acids, 1.177% total flavonoids, 0.546% total phenolic acids, and 0.483% inorganic elements. Molecular network and quantitative analysis used to characterize the chemical composition of Sijunzi Decoction. The present study systematically characterized the constituents of Sijunzi Decoction, revealed the composition ratio of each type of constituent, and provided a reference for study on the substance basis of other Chinese medicine.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Anticonvulsant Activity of Bombyx batryticatus and Analysis of Bioactive Extracts Based on UHPLC-Q-TOF MS/MS and Molecular Networking

Bombyx batryticatus (BB) is an anticonvulsant animal medicine in traditional Chinese medicine (TCM) and acts on the central nervous system. This research aimed to study the anticonvulsant effects of different polarity fractions of extracts from BB and to explore the components conferring anticonvulsant activity. Materials and methods: Crude extracts of BB at 20 g/kg were divided into different polarity fractions (petroleum ether, chloroform, ethyl acetate, water) and were administered to groups of mice before injecting pentylenetetrazol (PTZ) to induce convulsions. The animals were placed in chambers, and their behaviors were recorded for 30 min following the injection. Latency time, percent of protection, convulsion, convulsion rate, and convulsion score were determined for these mice. The compounds present in the different fractions were analyzed, and those from the fraction that conferred anticonvulsant activity were identified by high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF MS) and molecular networking (MN). The chloroform extract fractions (B-C) clearly increased the seizure latency time and protection percentage and decreased the convulsion percentage compared to the control group. The anticonvulsant effect of other extract fractions was not significant. Our study shows that the chloroform extract fractions (B-C) of BB have a significant anticonvulsant effect. We also identified 17 compounds including lumichrome, pheophorbide A, and episyringaresinol 4'-O-beta-d-glucopyranose that were found for the first time. The results of this study may lay the groundwork for studying compounds derived from Bombyx batryticatus and their anticonvulsant effect.

An Aminopyrimidone and Aminoimidazoles Alkaloids from the Rodrigues Calcareous Marine Sponge Ernsta naturalis

A chemical study of the CH₂Cl₂−MeOH (1:1) extract from the sponge Ernsta naturalis collected in Rodrigues (Mauritius) based on a molecular networking dereplication strategy highlighted one novel aminopyrimidone alkaloid compound, ernstine A (1), seven new aminoimidazole alkaloid compounds, phorbatopsins D–E (2, 3), calcaridine C (4), naamines H–I (5, 7), naamidines J–K (6, 8), along with the known thymidine (9). Their structures were established by spectroscopic analysis (1D and 2D NMR spectra and HRESIMS data). To improve the investigation of this unstudied calcareous marine sponge, a metabolomic study by molecular networking was conducted. The isolated molecules are distributed in two clusters of interest. Naamine and naamidine derivatives are grouped together with ernstine in the first cluster of twenty-three molecules. Phorbatopsin derivatives and calcaridine C are grouped together in a cluster of twenty-one molecules. Interpretation of the MS/MS spectra of other compounds of these clusters with structural features close to the isolated ones allowed us to propose a structural hypothesis for 16 compounds, 5 known and 11 potentially new.

Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning

Identification of xenobiotics and their phase I/II metabolites in poisoned patients remains challenging. Systematic approaches using bioinformatic tools are needed to detect all compounds as exhaustively as possible. Here, we aimed to assess an analytical workflow using liquid chromatography coupled to high-resolution mass spectrometry with data processing based on a molecular network to identify tramadol metabolites in urine and plasma in poisoned patients. The generated molecular network from liquid chromatography coupled to high-resolution tandem mass spectrometry data acquired in both positive and negative ion modes allowed for the identification of 25 tramadol metabolites in urine and plasma, including four methylated metabolites that have not been previously reported in humans or in vitro models. While positive ion mode is reliable for generating a network of tramadol metabolites displaying a dimethylamino radical in their structure, negative ion mode was useful to cluster phase II metabolites. In conclusion, the combined use of molecular networks in positive and negative ion modes is a suitable and robust tool to identify a broad range of metabolites in poisoned patients, as shown in a fatal tramadol-poisoned patient.

Molecular Networks and Macromolecular Molar Mass Distributions for Preliminary Characterization of Danish Craft Beers

Beer is one of the most widely consumed beverages containing up to 200,000 unique small molecules and a largely uncharacterized macromolecular and particulate space. The chemical profiling of beer is difficult due to its complex nature. To address this issue, we have used various state-of-the-art methods to determine the physicochemical characteristics of beer. Specifically, we have successfully generated an LC-MS-based molecular network with minimal sample preparation to profile indoles in beer and confirmed their presence using 1H-NMR. In addition, we have identified different macromolecular signatures in beer of different colors by utilizing AF4-MALS. These preliminary findings lay the foundation for further research on the physicochemical nature of beer.

Present-Day Practice of Non-Target Chemical Analysis

Abstract

We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.

Mass Spectrometry Metabolomics and Feature-Based Molecular Networking Reveals Population-Specific Chemistry in Some Species of the Sceletium Genus

The Sceletium genus has been of medicinal importance in southern Africa for millennia and Sceletium tortuosum (Aizoaceae), one of eight species in the genus has gained pharmaceutical importance as an anxiolytic and anti-depressant due to the presence of mesembrine alkaloids. S. tortuosum is used for the manufacture of herbal teas, dietary supplements and other phytopharmaceutical products. This study aimed to provide a metabolomic characterization of S. tortuosum and its sister species as these are not easy to distinguish using morphology alone. Plant samples were thus collected from various locations in the succulent Karoo (South Africa) and analyzed through liquid chromatography-mass spectrometry (LC-MS), using MS^E fragmentation as a putative tool for chemical identities. Metabolomics-based analyses in combination with molecular networking were able to distinguish between the four species of Sceletium based on the presence of 4-(3,4-dimethyoxyphenyl)-4-[2-acetylmethlamino)ethyl]cyclohexanone (m/z 334.2020; RT 6.60 min), mesembrine (m/z 290.1757; RT 5.10 min) and 4'-O-demethylmesembrenol (m/z 276.1597; RT 4.17 min). Metabolomic profiles varied according to the different localities and metabolites occurred at variable quantitative levels in Sceletium ecotypes. Molecular networking provided the added advantage of being able to observe mesembrine alkaloid isomers and coeluting metabolites (from the joubertiamine group) that were difficult to discern without this application. By combining high-throughput metabolomics together with global and feature based-molecular networking, a powerful metabolite profiling platform that is able to discern chemical patterns within and between populations was established. These techniques were able to reveal chemotaxonomic relationships and allowed for the discovery of chemical markers that may be used as part of monitoring protocols during the manufacture of phytopharmaceutical and dietary products based on Sceletium.

Comparison of Illicit Drug Seizures Products of Natural Origin Using a Molecular Networking Approach

Drug powder composition analysis is of particular interest in forensic investigations to identify illicit substance content, cutting agents and impurities. Powder profiling is difficult to implement due to multiple analytical methods requirement and remains a challenge for forensic toxicology laboratories. Furthermore, visualization tools allowing seizure products identification appear to be under-used to date. The aim of this study is to present the utility of molecular networking for the composition establishment of natural origin drugs. A powder suspected to contain heroin and three powders suspected to contain cocaine obtained from law enforcement agency seizures were analyzed using untargeted screening by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS). Molecular networking and metabolite annotation applied to suspected heroin sample allowed rapid confirmation of its illicit content (heroin), the identification of structurally related major impurities (6-monoacetylmorphine, 6-monoacetylcodeine, noscapine, and papaverine), as well as cutting agents (acetaminophen and caffeine). The cocaine powder profiling allowed the comparison of its constituents in a semi-quantitative manner (cocaine, benzoylecgonine, trans/cis-cinnamoylcocaine, trimethoxycocaine, hexanoylecgonine methylester, caffeine, hydroxyzine, levamisole, and phenacetin), bringing additional information for their identification, including geographically sourcing of natural product and their putative place in the supply chain. Although this approach does not replace the profiling techniques used by forensic laboratories, the use of molecular networks provides a visual overview of structurally related constituents which aids the comparison and investigation of seizure powders. Molecular networks offers here an ideal way to depict structurally related and unrelated compounds in these often complex mixtures of chemicals.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.

Simultaneous Quantification of 25 Fentanyl Derivatives and Metabolites in Oral Fluid by Means of Microextraction on Packed Sorbent and LC-HRMS/MS Analysis

Fentanyl and fentalogs’ intake as drugs of abuse is experiencing a great increase in recent years. For this reason, there are more and more cases in which it is important to recognize and quantify these molecules and related metabolites in biological matrices. Oral fluid (OF) is often used to find out if a subject has recently used a psychoactive substance and if, therefore, the person is still under the effect of psychotropics. Given its difficulty in handling, good sample preparation and the development of instrumental methods for analysis are essential. In this work, an analytical method is proposed for the simultaneous determination of 25 analytes, including fentanyl, several derivatives and metabolites. OF was collected by means of passive drool; sample pretreatment was developed in order to be fast, simple and possibly semi-automated by exploiting microextraction on packed sorbent (MEPS). The analysis was performed by means of LC–HRMS/MS obtaining good identification and quantification of all the analytes in less than 10 min. The proposed method was fully validated according to the Scientific Working Group for Forensic Toxicology (SWGTOX) international guidelines. Good results were obtained in terms of recoveries, matrix effect and sensitivity, showing that this method could represent a useful tool in forensic toxicology. The presented method was successfully applied to the analysis of proficiency test samples.

Variable Data Independent Acquisition and Data Mining Exploring Feature-Based Molecular Networking Analysis for Untargeted Screening of Synthetic Cannabinoids in Oral Fluid

Novel psychoactive substances (NPS) are constantly emerging in the drug market, and synthetic cannabinoids (SCs) are included in this NPS family. Forensic laboratories often struggle with these continually emerging SCs, forcing them to develop an untargeted workflow to incorporate these psychoactive drugs in their procedures. Usually, forensic laboratories select analytical methods based on targeted mass spectrometry (MS) technologies for strictly tracking already known NPS. The appropriate way to tackle unknown substances is to develop pipelines for untargeted analysis that include LC-HRMS analytical methods and data analysis. Once established, this strategy would allow drug testing laboratories to be always one step ahead of the new trends concerning the "designer drugs" market. To address this challenge an untargeted workflow based on mass spectrometry data acquisition and data analysis was developed to detect SCs in oral fluid (OF) samples at a low concentration range. The samples were extracted by mixed-mode solid-phase extraction and analyzed by Liquid Chromatography - High-Resolution Mass Spectrometry (LC-HRMS). Tandem mass spectra (MS2) were recorded performing a variable isolation width across a mass range of all theoretical precursor ions (vDIA) after the chromatographic separation. After raw data processing with the MSDial software, the deconvoluted features were sent to GNPS for Feature-Based Molecular Networking (FBMN) construction for nontargeted data mining. The FBMN analysis created a unique integrated network for most of the SCs assessed in the OF at a low level (20 ng/mL). These results demonstrate the potential of an untargeted approach to detect different derivatives of SCs at trace levels for forensic applications.

Cocaine profiling method retrospectively developed with nontargeted discovery of markers using liquid chromatography with time-of-flight mass spectrometry data

Illicit drug profiling performed by forensic laboratories assists law enforcement agencies through providing information about chemical and/or physical characteristics of seized specimens. In this article, a model was developed for the comparison of seized cocaine based on retrospective analysis of data generated from ultrahigh performance liquid chromatography with time‐of‐flight mass spectrometry (UHPLC‐TOF‐MS) comprehensive drug screening. A nontargeted approach to discover target compounds was employed, which generated 53 potential markers using data from cocaine positive samples. Twelve marker compounds were selected for the development of the final profiling model. The selection included a mixture of commonly used cocaine profiling targets and other cocaine‐related compounds. Combinations of pretreatments and comparison metrics were assessed using receiver operating characteristic curves to determine the combination with the best discrimination between linked and unlinked populations. Using data from 382 linked and 34,519 unlinked distances, a classification model was developed using a combination of the standardization and normalization transformations with Canberra distance, resulting in a linked cut‐off with a 0.5% false positive rate. The present study demonstrates the applicability of retrospectively developing a cocaine profiling model using data generated from UHPLC‐TOF‐MS nontargeted drug screening without pre‐existing information about cocaine impurities. The developed workflow was not specific to cocaine and thus could potentially be applied to any seized drug in which there are both sufficient data and impurities present.

Seizures of New Psychoactive Substances on the Italian territory during the COVID-19 pandemic

In recent years, the availability and the consequent consumption of New Psychoactive Substances (NPS) have proliferated at an unprecedented rate, posing a significant risk to the public health and challenging the law enforcement efforts to tackle the black market. In particular, large availability on Internet and unmonitored shipping have facilitated the diffusion of NPS on national territories. In this scenario, the forensic activity based on the process of drug detection, including investigation, seizure, recognition and analytical identification is crucial to get insights into the drug black market transformation. In this study, we describe the results obtained from the analysis of hundreds of packages seized during the months of year 2020, and suspected to contain NPS because not reacting with standard field test kits. We focused on the analysis by GC-MS and HPLC-HRMS, and NPS in particular, trying to underline the most common molecules present on the Italian territory during the COVID-19 pandemic. NPS were identified in 92.6% of the samples. The most prevalent compounds were synthetic cathinones, and 3-MMC in particular, which alone accounted for 18.6% of the total cases. Other prevalent molecules were 5F-MDMB-PICA, 2-FDCK, 1cp-LSD and 1P-LSD. Fentanyl was never detected. The information obtained from drug seizures is crucial to publish national alerts, which are in turn important to assist the legislative effort to ban new compounds and the update of toxicological and analytical methods.

New psychoactive substance cocktail in an intensive care intoxication case elucidated by molecular networking

BACKGROUND

The recreational use of new psychoactive substances (NPS) is increasing worldwide. Among them, the arylcyclohexylamine family including phencyclidine (PCP) and ketamine derivatives is increasing. We report a non-fatal intoxication mainly due to arylcyclohexylamine compounds illustrated by molecular networking (MN).

CASE DETAILS

A 37-year-old man with a history of drug abuse was discovered with several bags labeled as research chemicals around him and traces of powder on his nose. He was rehydrated, intubated, and admitted to the intensive care unit (ICU). Urine and drug were analyzed by liquid chromatography-mass spectrometry for NPS identification. Several NPS were quantified in urine: 3-OH-PCP at 12,085 mg/L, 3-MeO-PCP at 1100 mg/L, 2F-DCK at 147 mg/L, N-ethylhexedrone at 165 mg/L and CMC at 48 mg/L. Using a bioinformatic approach, a molecular network was built to confirm the consumption of powders contained in the bags by comparison with patient's urine.

DISCUSSION

This case illustrates the interest of MN to (i) perform sample-to-sample comparison, (ii) target quantification methods, and (iii) allow proper management to confirm the relevance of the treatment.

Recent Advances in the Heterologous Biosynthesis of Natural Products from Streptomyces

Streptomyces is a significant source of natural products that are used as therapeutic antibiotics, anticancer and antitumor agents, pesticides, and dyes. Recently, with the advances in metabolite analysis, many new secondary metabolites have been characterized. Moreover, genome mining approaches demonstrate that many silent and cryptic biosynthetic gene clusters (BGCs) and many secondary metabolites are produced in very low amounts under laboratory conditions. One strain many compounds (OSMAC), overexpression/deletion of regulatory genes, ribosome engineering, and promoter replacement have been utilized to activate or enhance the production titer of target compounds. Hence, the heterologous expression of BGCs by transferring to a suitable production platform has been successfully employed for the detection, characterization, and yield quantity production of many secondary metabolites. In this review, we introduce the systematic approach for the heterologous production of secondary metabolites from Streptomyces in Streptomyces and other hosts, the genome analysis tools, the host selection, and the development of genetic control elements for heterologous expression and the production of secondary metabolites.