MS-Based Molecular Networking of Designer Drugs as an Approach for the Detection of Unknown Derivatives for Forensic and Doping Applications: A Case of NBOMe Derivatives

High-resolution mass spectrometry-based suspect and nontarget screening of natural toxins in foodstuffs and risk assessment of dietary exposure

Daily dietary intake inevitably exposes individuals to various natural toxins, which may pose potential health threats. Focusing only on specific toxins could underestimate dietary risks. Therefore, we have developed a suspect and nontarget method based on ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) to screen both known and unknown natural toxins in various foodstuffs. An in-house database containing 2952 natural toxins including fungal toxins, phytotoxins, animal toxins and cyanotoxins was established, facilitating suspect screening. Predicted retention time and mass spectrometry data were employed to enhance the confidence levels. Subsequently, Nontarget screening method was conducted based on molecular network analysis, annotating the edges and nodes through modified types and fragmentation characteristics. Finally, we analyzed 102 foodstuff samples and identified a total of 90 natural toxins, including mycotoxins and phytotoxins, with 65 identified by suspect screening and 25 by nontarget screening. Based on measured concentrations, the daily per capita dietary intake of total natural toxins was estimated, it was below risk doses for natural toxins with available reference values. Overall, this work established a novel method for the comprehensive identification of natural toxins in foodstuffs and emphasized the importance of dietary risk assessment for natural toxins.

Antivirulence and antipathogenic activity of Mayan herbal remedies against Pseudomonas aeruginosa

ETHNOPHARMACOLOGICAL RELEVANCE

The Yucatan Peninsula has a privileged wealth of vascular plants with which various Mayan herbal formulations have been developed. However, studies on their antipathogenic and antivirulence properties are scarce.

AIM OF THE STUDY

Identify antivirulence properties in Mayan herbal remedies and determine their antipathogenic capacity in burn wounds infected with Pseudomonas aeruginosa.

MATERIALS AND METHODS

An ethnobotanical study was conducted in Mayan communities in central and southern Quintana Roo, Mexico. Furthermore, the antipathogenic capacity of three Mayan herbal remedies was analyzed using an animal model of thermal damage and P. aeruginosa infection. Antivirulence properties were determined by inhibiting phenotypes regulated by quorum sensing (pyocyanin, biofilm, and swarming) and by the secretion of the ExoU toxin. The chemical composition of the most active herbal remedy was analyzed using molecular network analysis.

RESULTS

It was found that topical administration of the remedy called "herbal soap" (HS) for eleven days maintained 100% survival of the animals, reduced establishment of the bacteria in the burn and prevented its systemic dispersion. Although no curative effect was recorded on tissue damaged by HS treatment, its herbal composition strongly reduced swarming and ExoU secretion. Through analysis of Molecular Networks, it was possible to carry out a global study of its chemical components, and identify the family of oxindole monoterpenoid alkaloids and carboline and tetrahydropyrididole alkaloids. In addition, flavonols, flavan-3-ols, and quinic acid derivatives were detected.

CONCLUSIONS

The antipathogenic and antivirulence capacity of ancient Mayan remedies makes them a potential resource for developing new antibacterial therapies to treat burns infected by P. aeruginosa.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Feature-Based Molecular Networking Combined with Multivariate Analysis for the Characterization of Glutathione Adducts as a Smoking Gun of Bioactivation

Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.

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.

Identification of a metabolite for the detection of the hydrophilic drug diisopropylamine for doping control

Diisopropylamine (DIPA), a hydrophilic chemical compound, is used as an intravenous antihypertensive agent. DIPA is prohibited for use in the horse racing industry due to its performance enhancing effects. A cyano (CN) hydrophilic interaction liquid chromatography (HILIC) column was used for the separation of DIPA from its metabolite. Ammonium formate was added to the mobile phase to increase the ionization of the basic substance. The metabolite was identified as an N-oxidized metabolite of DIPA, which eluted earlier than the parent drug and was less polar on the HILIC column. The main finding of the study was the identification of a metabolite with a mass shift of 15.9944. The in vitro experiment showed that the metabolite was produced as a result of N-oxidation processes, mainly mediated by flavin-containing monooxygenase (FMO). Methimazole was used to inhibit the FMO enzyme-mediated N-oxidation metabolism and metabolite production in a concentration-dependent manner. The metabolite was confirmed to be present in an actual horse urine sample that tested positive for DIPA. This study demonstrated that the metabolite could be screened using in vitro samples and their presence corresponded to a positive result in actual samples. This metabolite screening could therefore find application as a flexible way to test for new and modified banned substances in the racing industry.

A molecular analysis of substituted phenylethylamines as potential microtubule targeting agents through in silico methods and in vitro microtubule-polymerization activity

Natural phenethylamines are trace amine neurotransmitters associated with dopamine transmission and related illnesses such Parkinson's disease, and addiction. Synthetic phenethylamines can have psychoactive and hallucinogenic effects due to their high affinity with the 5-HT2A receptor. Evidence indicates phenethylamines can directly alter the microtubule cytoskeleton being structurally similar to the microtubule destabilizing agent colchicine, however little work has been done on this interaction. As microtubules provide neuron structure, intracellular transport, and influence synaptic plasticity the interaction of phenethylamines with microtubules is important for understanding the potential harms, or potential pharmaceutical use of phenethylamines. We investigated 110 phenethylamines and their interaction with microtubules. Here we performed molecular docking of these compounds at the colchicine binding site and ranked them via binding energy. The top 10% of phenethylamines were further screened based on pharmacokinetic and physicochemical properties derived from SwissADME and LightBBB. Based on these properties 25B-NBF, 25C-NBF, and DMBMPP were tested in in vitro microtubule polymerization assays showing that they alter microtubule polymerization dynamics in a dose dependent manner. As these compounds can rapidly cross the blood brain barrier and directly affect cytoskeletal dynamics, they have the potential to modulate cytoskeletal based neural plasticity. Further investigations into these mechanisms are warranted.

Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexpected from large MS data. Molecular networking is a good tool for exploring, visualizing, and organizing MS/MS spectra, and moreover, it employs shifted peak match to calculate spectral similarity, making it capable of identifying adulteration that is not included in the library. The key of molecular networking is spectral similarity algorithms, and therefore, in this study, we compared the performance of four cutting-edge similarity algorithms, modified cosine similarity (shifted peak match), entropy similarity, and two deep-learning-based algorithms, MS2DeepScore and Spec2Vec, in building molecular networking for identification of adulteration that is not included in the library. We conducted an analysis of excluded-query-compound on all MS/MS spectra in test library and performed a large-scale false discovery rate estimation to investigate whether the spectral similarity calculated by each algorithm could represent the actual structural similarity well. The obtained results demonstrated Spec2Vec exhibited good performance in both detection capability and false discovery rate. Further comprehensive evaluation of the performance of Spec2Vec in the identification of adulteration that is not included in the library or is unexpected in different matrices and in application to real samples proved the approach studied here is a promising and powerful tool for adulterant inspection and improved the capability of analyzing large MS data.

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.

Analysis of 2,5-dimethoxy-amphetamines and 2,5-dimethoxy-phenethylamines aiming their determination in biological matrices: a review

PURPOSE

The present review aims to provide an overview of methods for the quantification of 2,5-dimethoxy-amphetamines and -phenethylamines in different biological matrices, both traditional and alternative ones.

METHODS

A complete literature search was carried out with PubMed, Scopus and the World Wide Web using relevant keywords, e.g., designer drugs, amphetamines, phenethylamines, and biological matrices.

RESULTS

Synthetic phenethylamines represent one of the largest classes of "designer drugs", obtained through chemical structure modifications of psychoactive substances to increase their pharmacological activities. This practice is also favored by the fact that every new synthetic compound is not considered illegal by existing legislation. Generally, in a toxicological laboratory, the first monitoring of drugs of abuse is made by rapid screening tests that sometimes can occur in false positive or false negative results. To reduce evaluation errors, it is mandatory to submit the positive samples to confirmatory methods, such as gas chromatography or liquid chromatography combined to mass spectrometry, for a more specific qualitative and quantitative analysis.

CONCLUSIONS

This review highlights the great need for updated comprehensive analytical methods, particularly when analyzing biological matrices, both traditional and alternative ones, for the search of newly emerging designer drugs.

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.

An integrated strategy to reveal the potential anti-asthma mechanism of peimine by metabolite profiling, network pharmacology, and molecular docking

Peimine, one of the major quality markers in Fritillaria Cirrhosae Bulbus, was expected to become a new anti-asthma drug. However, its metabolic profiles and anti-asthma mechanism have not been clarified previously. In this study, a method was developed for the detection of peimine metabolites in vitro by ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry. The potential anti-asthma mechanism was predicted by an integrated analysis of network pharmacology and molecular docking. A total of 19 metabolites were identified with the aid of software and molecular networking. The metabolic profiles of peimine elucidated that the metabolism was a multi-pathway process with characteristics of species difference. The network pharmacology results showed that peimine and its metabolites could regulate multiple asthma-related targets. The above targets were involved in various regulatory pathways linked to asthma. Moreover, the results of molecular docking showed that both peimine and its metabolites had a certain affinity with the β₂ adrenergic receptor. The results provided not only important references to understand the metabolism and pharmacodynamic changes of peimine in vitro, but also supporting data for further pharmacological evaluation. It also provided a new perspective for clarifying the functional changes of traditional Chinese medicine in vitro.

Molecular networking-guided strategy for the pharmacokinetic study of herbal medicines: Cudrania tricuspidata leaf extracts

In this study, the pharmacokinetic profiles of the bioactive components in the leaf extract of the medicinal herb, Cudrania tricuspidate, were investigated using an MS/MS-based molecular networking system. To identify the major active components of the C. tricuspidate leaf extract (CLE), HPLC-DAD analysis was conducted with a standard mixture of six flavonoids (rutin, isoquercitrin, nicotiflorin, kaempferol 3-O-glucoside, quercetin, and kaempferol). The unknown peaks were determined via molecular networking analysis using the mass dataset obtained by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). For the subsequent pharmacokinetic study, CLE (1 g/kg) was orally administered to rats, and plasma samples were collected. The product ion mass data of plasma samples using LC-QTOF/MS were obtained and subjected to molecular networking analysis. The resulting molecular networking map indicated that the glucuronide metabolites of quercetin and kaempferol were the major circulating species. Accordingly, quercetin and kaempferol were determined following β-glucuronidase treatment, and their pharmacokinetic parameters were calculated. These findings indicate that the proposed molecular network-based approaches are potential and efficient methods for the pharmacokinetic study of herbal medicines.

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.

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.

In Vitro Metabolism of Donepezil in Liver Microsomes Using Non-Targeted Metabolomics

Donepezil is a reversible acetylcholinesterase inhibitor that is currently the most commonly prescribed drug for the treatment of Alzheimer’s disease. In general, donepezil is known as a safe and well-tolerated drug, and it was not associated with liver abnormalities in several clinical trials. However, rare cases of drug-related liver toxicity have been reported since it has become commercially available. Few studies have investigated the metabolic profile of donepezil, and the mechanism of liver damage caused by donepezil has not been elucidated. In this study, the in vitro metabolism of donepezil was investigated using liquid chromatography–tandem mass spectrometry based on a non-targeted metabolomics approach. To identify metabolites, the data were subjected to multivariate data analysis and molecular networking. A total of 21 donepezil metabolites (17 in human liver microsomes, 21 in mice liver microsomes, and 17 in rat liver microsomes) were detected including 14 newly identified metabolites. One potential reactive metabolite was identified in rat liver microsomal incubation samples. Metabolites were formed through four major metabolic pathways: (1) O-demethylation, (2) hydroxylation, (3) N-oxidation, and (4) N-debenzylation. This study indicates that a non-targeted metabolomics approach combined with molecular networking is a reliable tool to identify and detect unknown drug metabolites.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs

BACKGROUND

Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.

PURPOSE

To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.

METHODS

A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.

RESULTS

A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.

CONCLUSION

These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.

A strategy combining solid-phase extraction, multiple mass defect filtering and molecular networking for rapid structural classification and annotation of natural products: characterization of chemical diversity in Citrus aurantium as a case study

Medicinal plants are complex chemical systems containing thousands of secondary metabolites. The rapid classification and characterization of the components in medicinal plants using mass spectrometry (MS) remains an immense challenge. Herein, a novel strategy is presented for MS through the combination of solid-phase extraction (SPE), multiple mass defect filtering (MMDF) and molecular networking (MN). This strategy enables efficient classification and annotation of natural products. When combined with SPE and MMDF, the improved analytical method of MN can perform the rapid annotation of diverse natural products in Citrus aurantium according to the tandem mass spectrometry (MS/MS) fragments. In MN, MS2LDA can be initially applied to recognize substructures of natural products, according to the common fragmentation patterns and neutral losses in multiple MS/MS spectra. MolNetEnhancer was adopted here to obtain chemical classifications provided by ClassyFire. The results suggest that the integrated SPE-MMDF-MN method was capable of rapidly annotating a greater number of natural products from Citrus aurantium than the classical MN strategy alone. Moreover, SPE and MMDF enhanced the effectiveness of MN for annotating, classifying and distinguishing different types of natural products. Our workflow provides the foundation for the automated, high-throughput structural classification and annotation of secondary metabolites with various chemical structures. The developed approach can be widely applied in the analysis of constituents in natural products.

Reaction Tracking and High-Throughput Screening of Active Compounds in Combinatorial Chemistry by Tandem Mass Spectrometry Molecular Networking

Combinatorial synthesis has been widely used as an efficient strategy to screen for active compounds. Mass spectrometry is the method of choice in the identification of hits resulting from high-throughput screenings due to its high sensitivity, specificity, and speed. However, manual data processing of mass spectrometry data, especially for structurally diverse products in combinatorial chemistry, is extremely time-consuming and one of the bottlenecks in this process. In this study, we demonstrated the effectiveness of a tandem mass spectrometry molecular networking-based strategy for product identification, reaction dynamics monitoring, and active compound targeting in combinatorial synthesis. Molecular networking connects compounds with similar tandem mass spectra into a cluster and has been widely used in natural products analysis. We show that both the expected and side products can be readily characterized using molecular networking based on their mass spectrometry fragmentation patterns. Additionally, time-dependent molecular networking was integrated to track reaction dynamics to determine the optimal reaction time to maximize target product yields. We also present a proof-of-concept experiment that successfully identified and isolated active molecules from a dynamic combinatorial library. These results demonstrated the potential of using molecular networking for identifying, tracking, and high-throughput screening of active compounds in combinatorial synthesis.

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

New Psychoactive Substances (NPS) are a global concern since they are spreading at an unprecedented rate. Despite their commerce still being limited compared to traditional illicit drugs, the identification of NPS in seizures may represent a challenge because of the variety of possible structures. In this study we report the successful application of molecular networking (MN) to identify unexpected fentanyl analogs in two seizures. The samples were extracted with 1 mL of methanol and analyzed with an untargeted data-dependent acquisition approach by LC–HRMS. The obtained data were examined using the MN workflow within the Global Natural Product Search (GNPS). A job was submitted to GNPS by including both seizures and standard mixtures containing synthetic cannabinoids and fentanyls raw files; spectra obtained from standards were used to establish representative networks for both molecular classes. All synthetic cannabinoids in the mixture were linked together resulting in a molecular network despite their different fragmentation spectra. Looking at fentanyls, all the molecules with the typical 188.143 and 105.070 fragments were combined in a representative network. By exploiting the standard networks two unexpected fentanyls were found in the analyzed seizures and were putatively annotated as para-fluorofuranylfentanyl and (iso)butyrylfentanyl. The identity of these two fentanyl analogs was confirmed by NMR analysis. Other m/z ratios in the seizures were compatible with fentanyl derivatives; however, they appeared to be minor constituents, probably impurities or synthetic byproducts. The latter might be of interest for investigations of common fingerprints among different seizures.

Diagnostic fragmentation-assisted mass spectral networking coupled with in silico dereplication for deep annotation of steroidal alkaloids in medicinal Fritillariae Bulbus

Fully understanding the chemicals in an herbal medicine remains a challenging task. Molecular networking (MN) allows to organize tandem mass spectrometry (MS/MS) data in complex samples by mass spectral similarity, which yet suffers from low coverage and accuracy of compound annotation due to the size limitation of available databases and differentiation obstacle of similar chemical scaffolds. In this work, an enhanced MN-based strategy named diagnostic fragmentation-assisted molecular networking coupled with in silico dereplication (DFMN-ISD) was introduced to overcome these obstacles: the rule-based fragmentation patterns provide insights into similar chemical scaffolds, the generated in silico candidates based on metabolic reactions expand the available natural product databases, and the in silico annotation method facilitates the further dereplication of candidates by computing their fragmentation trees. As a case, this approach was applied to globally profile the steroidal alkaloids in Fritillariae bulbus, a commonly used antitussive and expectorant herbal medicine. Consequently, a total of 325 steroidal alkaloids were discovered, including 106 cis-D/E-cevanines, 142 trans-D/E-cevanines, 29 jervines, 23 veratramines, and 25 verazines. And 10 of them were confirmed by available reference standards. Approximately 70% of the putative steroidal alkaloids have never been reported in previous publications, demonstrating the benefit of DFMN-ISD approach for the comprehensive characterization of chemicals in a complex plant organism.

Rapid Detection of Anabolic and Narcotic Doping Agents in Saliva and Urine By Means of Nanostructured Silicon SALDI Mass Spectrometry

Novel doping agents and doping strategies are continually entering the market, placing a burden on analytical methods to detect, adapt, and respond to subtle changes in the composition of biological samples. Therefore, there is a growing interest in rapid, adaptable, and ideally confirmatory analytical methods for the fight against doping. Nanostructured silicon (nano-Si)-based surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) can effectively address this need, allowing fast and sensitive detection of prohibited compounds used in sport doping. Here, we demonstrate the detection of growth hormone peptides, anabolic-androgenic steroids, and narcotics at low concentrations directly from biological matrices. Molecular confirmation was performed using the fragmentation data of the structures, obtained with the tandem mass spectrometry capabilities of the SALDI instrument. The obtained data were in excellent agreement with those obtained using leading triple quadrupole liquid chromatography-mass spectrometry instruments. Furthermore, nano-Si SALDI-MS has the capacity for high-throughput analysis of hundreds of biological samples, providing opportunities for real-time MS analysis at sporting events.

Interpol review of controlled substances 2016-2019

This review paper covers the forensic-relevant literature in controlled substances from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

NBOMes-Highly Potent and Toxic Alternatives of LSD

Recently, a new class of psychedelic compounds named NBOMe (or 25X-NBOMe) has appeared on the illegal drug market. NBOMes are analogs of the 2C family of phenethylamine drugs, originally synthesized by Alexander Shulgin, that contain a N-(2-methoxy)benzyl substituent. The most frequently reported drugs from this group are 25I-NBOMe, 25B-NBOMe, and 25C-NBOMe. NBOMe compounds are ultrapotent and highly efficacious agonists of serotonin 5-HT_2A and 5-HT_2C receptors (Ki values in low nanomolar range) with more than 1000-fold selectivity for 5-HT_2A compared with 5-HT_1A. They display higher affinity for 5-HT_2A receptors than their 2C counterparts and have markedly lower affinity, potency, and efficacy at the 5-HT_2B receptor compared to 5-HT_2A or 5-HT_2C. The drugs are sold as blotter papers, or in powder, liquid, or tablet form, and they are administered sublingually/buccally, intravenously, via nasal insufflations, or by smoking. Since their introduction in the early 2010s, numerous reports have been published on clinical intoxications and fatalities resulting from the consumption of NBOMe compounds. Commonly observed adverse effects include visual and auditory hallucinations, confusion, anxiety, panic and fear, agitation, uncontrollable violent behavior, seizures, excited delirium, and sympathomimetic signs such mydriasis, tachycardia, hypertension, hyperthermia, and diaphoresis. Rhabdomyolysis, disseminated intravascular coagulation, hypoglycemia, metabolic acidosis, and multiorgan failure were also reported. This survey provides an updated overview of the pharmacological properties, pattern of use, metabolism, and desired effects associated with NBOMe use. Special emphasis is given to cases of non-fatal and lethal intoxication involving these compounds. As the analysis of NBOMes in biological materials can be challenging even for laboratories applying modern sensitive techniques, this paper also presents the analytical methods most commonly used for detection and identification of NBOMes and their metabolites.