Mass spectral similarity mapping applied to fentanyl analogs

Comprehensive Data Evaluation Methods Used in Developing the SWGDRUG Mass Spectral Reference Library for Seized Drug Identification

The mass spectral library of the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) is the most comprehensive free reference database of its kind in the world. It provides reliable mass spectra for identification of seized drugs, their metabolites, and related forensic compounds when using gas chromatography/mass spectrometry (GC/MS). The SWGDRUG library (version 3.13) contains spectra for 3598 compounds. All spectra are evaluated by the Mass Spectrometry Data Center (MSDC) at the National Institute of Standards and Technology (NIST). Over the past few years, new evaluation methods aided by improved software have been developed. First, all chemical information, such as chemical structure and name, is confirmed. Second, the product ions in each spectrum are verified to match the compound structure using the NIST MS Interpreter software tool. Subsequently, the mass spectra are compared to the same or similar compounds across six different mass spectral reference libraries using three distinct library search methods. Additionally, the NIST Artificial Intelligence Retention Indices (AIRI) software is used to help confirm the corresponding compounds of spectra, especially for those without molecular ions. Low-quality and incorrect spectra are rejected for inclusion in the library.

Dependency of fentanyl analogue protomer ratios on solvent conditions as measured by ion mobility-mass spectrometry

Recently, our group has shown that fentanyl and many of its analogues form prototropic isomers ("protomers") during electrospray ionization. These different protomers can be resolved using ion mobility spectrometry and annotated using mobility-aligned tandem mass spectrometry fragmentation. However, their formation and the extent to which experimental variables contribute to their relative ratio remain poorly understood. In the present study, we systematically investigated the effects of mixtures of common chromatographic solvents (water, methanol, and acetonitrile) and pH on the ratio of previously observed protomers for 23 fentanyl analogues. Interestingly, these ratios (N-piperidine protonation vs. secondary amine/O = protonation) decreased significantly for many analogues (e.g., despropionyl ortho-, meta-, and para-methyl fentanyl), increased significantly for others (e.g., cis-isofentanyl), and remained relatively constant for the others as solvent conditions changed from 100% organic solvent (methanol or acetonitrile) to 100% water. Interestingly, pH also had significant effects on this ratio, causing the change in ratio to switch in many cases. Lastly, increasing conditions to pH ≥ 4.0 also prompted the appearance of new mobility peaks for ortho- and para-methyl acetyl fentanyl, where all previous studies had only showed one single distribution. Because these ratios have promise to be used qualitatively for identification of these (and emerging) fentanyl analogues, understanding how various conditions (i.e., mobile phase selection and/or chromatographic gradient) affect their ratios is critically important to the development of advanced ion mobility and mass spectrometry methodologies to identify fentanyl analogues.

Identification of Unique Fragmentation Patterns of Fentanyl Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry

The opioid crisis in the United States is being fueled by the rapid emergence of new fentanyl analogs and precursors that can elude traditional library-based screening methods, which require data from known reference compounds. Since reference compounds are unavailable for new fentanyl analogs, we examined if fentanyls (fentanyl + fentanyl analogs) could be identified in a reference-free manner using a combination of electrospray ionization (ESI), high-resolution ion mobility (IM) spectrometry, high-resolution mass spectrometry (MS), and higher-energy collision-induced dissociation (MS/MS). We analyzed a mixture containing nine fentanyls and W-15 (a structurally similar molecule) and found that the protonated forms of all fentanyls exhibited two baseline-separated IM distributions that produced different MS/MS patterns. Upon fragmentation, both IM distributions of all fentanyls produced two high intensity fragments, resulting from amine site cleavages. The higher mobility distributions of all fentanyls also produced several low intensity fragments, but surprisingly, these same fragments exhibited much greater intensities in the lower mobility distributions. This observation demonstrates that many fragments of fentanyls predominantly originate from one of two different gas-phase structures (suggestive of protomers). Furthermore, increasing the water concentration in the ESI solution increased the intensity of the lower mobility distribution relative to the higher mobility distribution, which further supports that fentanyls exist as two gas-phase protomers. Our observations on the IM and MS/MS properties of fentanyls can be exploited to positively differentiate fentanyls from other compounds without requiring reference libraries and will hopefully assist first responders and law enforcement in combating new and emerging fentanyls.

Inferring the Nominal Molecular Mass of an Analyte from Its Electron Ionization Mass Spectrum

The performance of three algorithms for predicting nominal molecular mass from an analyte's electron ionization mass spectrum is presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method (SS-HM) and iterative Hybrid Search Hitlist Method (iHS-HM) leverage results from mass spectral library searching. These predictions can be employed in combination (recommended) or independently. The methods were tested on two sets of query mass spectra searched against libraries that did not contain the reference mass spectra of the same compounds: 19,074 spectra of various organic molecules searched against the NIST17 mass spectral library and 162 spectra of small molecule drugs searched against SWGDRUG version 3.3. Individually, each molecular mass prediction method had computed precisions (the fraction of positive predictions that were correct) of 91, 89, and 74%, respectively. The methods become more valuable when predictions are taken together. When all three predictions were identical, which occurred in 33% of the test cases, the predicted molecular mass was almost always correct (>99%).

Identifying reliable ions for the statistical differentiation of structurally similar fentanyl analogs

Definitive identification of fentanyl analogs based on mass spectral comparison is challenging given the high degree of structural and, hence, spectral similarity. To address this, a statistical method was previously developed in which two electron-ionization (EI) mass spectra are compared using the unequal variance t-test. Normalized intensities of corresponding ions are compared, testing the null hypothesis (H0 ) that the difference in intensity is equal to zero. If H0 is accepted at all m/z values, the two spectra are statistically equivalent at the specified confidence level. If H0 is not accepted at any m/z value, then there is a significant difference in intensity at that m/z value between the two spectra. In this work, the statistical comparison method is applied to distinguish EI spectra of valeryl fentanyl, isovaleryl fentanyl, and pivaloyl fentanyl. Spectra of the three analogs were collected over a 9-month period and at different concentrations. At the 99.9% confidence level, the spectra of corresponding isomers were statistically associated. Spectra of different isomers were statistically distinct, and ions responsible for discrimination were identified in each comparison. To account for inherent instrument variations, discriminating ions for each pairwise comparison were ranked based on the magnitude of the calculated t-statistic (tcalc ) value. For a given comparison, ions with higher tcalc values are those with the greatest difference in intensity between the two spectra and, therefore, are considered more reliable for discrimination. Using these methods, objective discrimination among the spectra was achieved and ions considered most reliable for discrimination of these isomers were identified.

NIST Mass Spectrometry Data Center standard reference libraries and software tools: Application to seized drug analysis

The standard reference libraries and associated custom software provided by the National Institute of Standards and Technology's Mass Spectrometry Data Center (NIST MSDC) are described with a focus on assisting the seized drug analyst with the identification of fentanyl-related substances (FRS). These tools are particularly useful when encountering novel substances when no certified sample is available. The MSDC provides three standard reference mass spectral libraries, as well as six software packages for mass spectral analysis, reference library searching, data interpretation, and measurement uncertainty estimation. Each of these libraries and software packages are described with references to the original publications provided. Examples of fentanyl identification by gas chromatography-mass spectrometry (GC-MS) and by direct analysis in real-time (DART) mass spectrometry are given. A link to online tutorials is provided.

Expert Algorithm for Substance Identification Using Mass Spectrometry: Application to the Identification of Cocaine on Different Instruments Using Binary Classification Models

This is the second of two manuscripts describing how general linear modeling (GLM) of a selection of the most abundant normalized fragment ion abundances of replicate mass spectra from one laboratory can be used in conjunction with binary classifiers to enable specific and selective identifications with reportable error rates of spectra from other laboratories. Here, the proof-of-concept uses a training set of 128 replicate cocaine spectra from one crime laboratory as the basis of GLM modeling. GLM models for the 20 most abundant fragments of cocaine were then applied to 175 additional test/validation cocaine spectra collected in more than a dozen crime laboratories and 716 known negative spectra, which included 10 spectra of three diastereomers of cocaine. Spectral similarity and dissimilarity between the measured and predicted abundances were assessed using a variety of conventional measures, including the mean absolute residual and NIST's spectral similarity score. For each spectral measure, GLM predictions were compared to the traditional exemplar approach, which used the average of the cocaine training set as the consensus spectrum for comparisons. In unsupervised models, EASI provided better than a 95% true positive rate for cocaine with a 0% false positive rate. A supervised binary logistic regression model provided 100% accuracy and no errors using EASI-predicted abundances of only four peaks at m/z 152, 198, 272, and 303. Regardless of the measure of spectral similarity, error rates for identifications using EASI were superior to the traditional exemplar/consensus approach. As a supervised binary classifier, EASI was more reliable than using Mahalanobis distances.

Trace residue identification, characterization, and longitudinal monitoring of the novel synthetic opioid β-U10, from discarded drug paraphernalia

Empirical data regarding dynamic alterations in illicit drug supply markets in response to the COVID-19 pandemic, including the potential for introduction of novel drug substances and/or increased poly-drug combination use at the "street" level, that is, directly proximal to the point of consumption, are currently lacking. Here, a high-throughput strategy employing ambient ionization-mass spectrometry is described for the trace residue identification, characterization, and longitudinal monitoring of illicit drug substances found within >6,600 discarded drug paraphernalia (DDP) samples collected during a pilot study of an early warning system for illicit drug use in Melbourne, Australia from August 2020 to February 2021, while significant COVID-19 lockdown conditions were imposed. The utility of this approach is demonstrated for the de novo identification and structural characterization of β-U10, a previously unreported naphthamide analog within the "U-series" of synthetic opioid drugs, including differentiation from its α-U10 isomer without need for sample preparation or chromatographic separation prior to analysis. Notably, β-U10 was observed with 23 other drug substances, most commonly in temporally distinct clusters with heroin, etizolam, and diphenhydramine, and in a total of 182 different poly-drug combinations. Longitudinal monitoring of the number and weekly "average signal intensity" (ASI) values of identified substances, developed here as a semi-quantitative proxy indicator of changes in availability, relative purity and compositions of street level drug samples, revealed that increases in the number of identifications and ASI for β-U10 and etizolam coincided with a 50% decrease in the number of positive detections and an order of magnitude decrease in the ASI for heroin.

A quantitative reliability metric for querying large database

A redesigned quantitative reliability metric based on the F-distribution (QRMf) is reported for evaluating the reliability of library search. The QRMf provides orthogonal information to the comparison metric (e.g., dot product) and yields a probabilistic result. An intralibrary search can be considered as an idealized search because the top hit, i.e., the closest matching object, will match perfectly. If the search of an unknown object yields the same hit list as the intralibrary search, it would indicate good reliability. For each object in the hit list, a QRMf compares the order of an intralibrary and interlibrary search results and calculates a variance of interlibrary similarity metrics between the records of the intralibrary search and records in the corresponding positions of the interlibrary search. This variance that measures the discordance of the intra and interlibrary search can simply be compared to the variance of the similarity metrics within the interlibrary search results. The ratio of these variances follows an F-distribution that can be used to determine if the discordance is statistically significant and generates the probability based on the cumulative distribution function. The QRMf works for both similarity and dissimilarity and can be used for any queried object and comparison metric that is searched against a database. In this work, the QRMf was used along with the dot product similarity to query the mass spectra of novel synthetic opioids measured by gas chromatography-mass spectrometry (GC/MS). An automated pipeline was devised that used a basis set correction to assist peak detection. The basis was constructed by mass spectra obtained from the blank measurement preceding the analytical run to remove interferences from column bleed and septum degradation. After peak detection, the pipeline applied multivariate curve resolution to the chromatographic peak window to remove background components from the mass spectra. The corrected mass spectra were searched against a customized library for identification. The QRMf can be used along with the similarity metric to detect misidentifications and assist in finding the correct identification when it is not the closest match.

Use of Diagnostic Ions for the Detection of Fentanyl Analogs in Human Matrices by LC-QTOF

To combat the ongoing opioid epidemic, our laboratory has developed and evaluated an approach to detect fentanyl analogs in urine and plasma by screening LC-QTOF MS/MS spectra for ions that are diagnostic of the core fentanyl structure. MS/MS data from a training set of 142 fentanyl analogs were used to select the four product ions and six neutral losses that together provided the most complete coverage (97.2%) of the training set compounds. Furthermore, using the diagnostic ion screen against a set of 49 fentanyl analogs not in the training set resulted in 95.9% coverage of those compounds. With this approach, lower reportable limits for fentanyl and a subset of fentanyl-related compounds range from 0.25 to 2.5 ng/mL in urine and 0.5 to 5.0 ng/mL in plasma. This innovative processing method was applied to evaluate simulated exposure samples of remifentanil and carfentanil in water and their metabolites remifentanil acid and norcarfentanil in urine. This flexible approach enables a way to detect emerging fentanyl analogs in clinical samples.

Development and evaluation of a synthetic opioid targeted gas chromatography mass spectrometry (GC-MS) method

As seized drug casework becomes increasingly complex due to the continued prevalence of emerging drugs, laboratories are often looking for new analytical approaches including developing methods for the analysis of specific compounds classes. Recent efforts have focused on the development of targeted gas chromatography mass spectrometry (GC-MS) confirmation methods to compliment the information-rich screening results produced by techniques like direct analysis in real time mass spectrometry (DART-MS). In this work, a method for the confirmation of synthetic opioids and related compounds was developed and evaluated. An 11-component test solution was used to develop a method that focused on minimizing overlapping retention time acceptance windows and understanding the influence of instrument parameters on reproducibility and sensitivity. Investigated settings included column type, flow rate, temperature program, inlet temperature, source temperature, and tune type. Using a DB-200 column, a 35-min temperature ramped method was created. It was evaluated against a suite of 222 synthetic opioids and related compounds, and successfully differentiated all but four compound pairs based on nonoverlapping retention time acceptance windows or objectively different mass spectra. Compared to a general confirmatory method used in casework, the targeted method was up to 25 times more sensitive and provided at least a two-fold increase in retention time differences. Analysis of extracts from actual case samples successfully demonstrated utility of the method and showed no instance of carryover, although the high polarity column required wider retention time windows than other columns.

The Min-Max Test: An Objective Method for Discriminating Mass Spectra

Deciding whether the mass spectra of seized drug evidence and a reference standard are measurements of two different compounds is a central challenge in forensic chemistry. Normally, an analyst will collect mass spectra from the sample and a reference standard under identical conditions, compute a mass spectral similarity score, and make a judgment about the sample using both the similarity score and their visual interpretation of the spectra. This approach is inherently subjective and not ideal when a rapid assessment of several samples is necessary. Making decisions using only the score and a threshold value greatly improves analysis throughput and removes analyst-to-analyst subjectivity, but selecting an appropriate threshold is itself a nontrivial task. In this paper, we describe and evaluate the min-max test-a simple and objective method for classifying mass spectra that leverages replicate measurements from each sample to remove analyst subjectivity. We demonstrate that the min-max test has an intuitive interpretation for decision-making, and its performance exceeds thresholding with similarity scores even when the best performing threshold for a fixed dataset is prescribed. Determining whether the underlying framework of the min-max test can incorporate retention indices for objectively deciding whether spectra are measurements of the same compound is an ongoing work.

Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC-MS/MS Machine Learning Models and the Hybrid Similarity Search Algorithm

High-resolution LC-MS/MS tandem mass spectra-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPS's). Using a training set comprised of 770 LC-MS/MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPS's were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine, and others). Using 193 LC-MS/MS barcode spectra as an external test set, accuracy of the ANN, SVM, and k-NN models were evaluated as 72.5%, 90.0%, and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPS's whose data are unavailable in the database. When only 24 representative LC-MS/MS spectra of controlled substances and NPS's were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded AI-SNPS (artificial intelligence screener for narcotic drugs and psychotropic substances) standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPS's to be identified in a convenient manner.

Gas Chromatography-Mass Spectrometry Analysis of Synthetic Opioids Belonging to the Fentanyl Class: A Review

The rising number of deaths caused by fentanyl overdosing in the US due to the overwhelming illicit use of this synthetic opioid has started a global campaign to develop efficient ways to control its production and distribution as well as discovering efficient antidotes to mitigate its lethal effects. Another important vein of focused research established by various agencies lies in the development of efficient and practical protocols for the detection of this opioid and analogs thereof in various matrices, whether environmental or biological in nature, particularly in the field of gas chromatography-mass spectrometry (GC-MS). The following review will cover the literature dealing with the detection and identification of synthetic opioids belonging to the fentanyl class by GC-MS means and hyphenated versions of the technique. Detailed descriptions will be given for the GC-MS methods employed for the analysis of the opioid, starting with the nature of the extraction protocol employed prior to analysis to the actual findings presented by the cited reports. Great effort has gone into describing the methods involved in each paper in a detailed manner and these have been compiled by year in tables at the end of each section for the reader's convenience. Lastly, the review will end with concluding remarks about the state of GC-MS analysis with regards to these powerful opioids and what lies ahead for this analytical field.

DART-MS Spectral Similarity of Infrared Thermally Desorbed Solid Particulate and Solution Cast Propellant Samples

Security and forensic applications employ test and reference materials to develop, calibrate, and validate analytical instrumentation such as mass spectrometry for the trace detection and chemical analysis of target analytes. An emerging class of target analytes includes homemade fuel oxidizer explosives based on pyrotechnics, propellants, and powder mixtures. Test materials for these compounds must appropriately and accurately embody the physical and chemical nature of the threat. Precision liquid deposition methods have long been employed for creation of trace level test materials. Mass spectral similarity and chemical signature differences between solid particulate and solution cast (i.e., liquid deposited) propellant samples were investigated by infrared thermal desorption direct analysis in real time mass spectrometry (IRTD-DART-MS). Differences in the mass spectra and ion distributions of solid and liquid deposited black powders and black powder substitutes were observed. These differences were attributed to chemical processes (e.g., degradation) and physical differences in the crystal formation, spatial distribution, morphology, and size. The production and deposition of test and reference materials remain critical to developing new technologies and detecting evolving threats.

Spectral trends in GC-EI-MS data obtained from the SWGDRUG mass spectral library and literature: A resource for the identification of unknown compounds

Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography-electron ionization-mass spectrometry (GC-EI-MS). Though there is a large body of research focused on the analysis of illicit substances with GC-EI-MS, there is little high-level discussion of mass spectral trends for different classes of drugs. This manuscript compiles literature information and performs simple exploratory analyses on evaluated GC-EI-MS data to investigate mass spectral trends for illicit substance classes. Additionally, this work offers other important aspects: brief discussions of how each class of drugs is used; illustrations of EI mass spectra with proposed structures of commonly observed ions; and summaries of mass spectral trends that can help an analyst classify new illicit compounds.