Discrimination of seized drug positional isomers based on statistical comparison of electron-ionization mass spectra

Improved Discrimination of Mass Spectral Isomers Using the High-Dimensional Consensus Mass Spectral Similarity Algorithm

This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C10H16 and C10H14) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data.

Identification of synthetic cathinone positional isomers using electron activated dissociation mass spectrometry

BACKGROUND

Synthetic cathinones (SCs) are a large category of new psychoactive substances (NPS), which pose a serious threat to public health due to limited information about their toxicology and pharmacology. Many SCs are closely related in their chemical structures, with some substances being positional isomers. In this study, we propose a new workflow for the identification of SC isomers using liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS2) combined with electron activated dissociation (EAD) and chemometrics. Differentiation between isomeric SCs is essential for both legislative and public safety reasons, since minor differences in their molecular structures may change their legal status and pharmacological profiles.

RESULTS

The workflow was optimized using ring-substituted isomers of methylmethcathinones, methylethcathinones, and chloromethcathinones. The kinetic energy in the EAD cell was investigated at three levels (i.e., 15, 18, and 20 eV) for each group. Two data analysis methods (i.e., t-distributed stochastic neighbor embedding [t-SNE] and a Random Forest [RF] algorithm) were applied using the obtained EAD mass spectral data. The three sets of ring-substituted SCs were clearly distinguished using t-SNE and an RF algorithm. Moreover, the RF approach resulted in a 97 % classification accuracy for isomer identification using various combinations of compounds, isomers, and electron kinetic energies. This workflow was subsequentially applied to the analysis of 26 blind street samples, resulting in a 92 % classification accuracy for isomer identification. However, the accuracy varied based on the kinetic electron energy. A subset of the original data set, focusing on 15-eV data only, was used, resulting in a classification accuracy of 100 %.

SIGNIFICANCE

This study presents the first LC-HRMS2 workflow based on EAD and chemometrics, which resulted in a classification accuracy of 100 % of authentic street samples. The developed LC-HRMS2 workflow demonstrates that EAD product ions and their characteristic ion ratios can be successfully used to identify ring-substituted positional isomers of SCs.

On the challenge of unambiguous identification of fentanyl analogs: Exploring measurement diversity using standard reference mass spectral libraries

Fentanyl analogs are a class of designer drugs that are particularly challenging to unambiguously identify due to the mass spectral and retention time similarities of unique compounds. In this paper, we use agglomerative hierarchical clustering to explore the measurement diversity of fentanyl analogs and better understand the challenge of unambiguous identifications using analytical techniques traditionally available to drug chemists. We consider four measurements in particular: gas chromatography retention indices, electron ionization mass spectra, electrospray ionization tandem mass spectra, and direct analysis in real time mass spectra. Our analysis demonstrates how simultaneously considering data from multiple measurement techniques increases the observable measurement diversity of fentanyl analogs, which can reduce identification ambiguity. This paper further supports the use of multiple analytical techniques to identify fentanyl analogs (among other substances), as is recommended by the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG).

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.

Instrument-independent chemometric models for rapid, calibration-free NPS isomer differentiation from mass spectral GC-MS data

Chemometric analysis of mass spectral data for the purpose of differentiating positional isomers of novel psychoactive substances has seen a substantial increase in popularity in recent years. However, the process of generating a large and robust dataset for chemometric isomer identification is time consuming and impractical for forensic laboratories. To begin to address this problem, three sets of ortho/meta/para positional ring isomers (fluoroamphetamine (FA), fluoromethamphetamine (FMA), and methylmethcathinone (MMC)) were analyzed using multiple GC-MS instruments at three distinct laboratories. A diverse assortment of instrument manufacturers, model types, and parameters was utilized in order to incorporate substantial instrumental variation. The dataset was randomly split into 70% training and 30% validation sets, stratified by instrument. Following an approach based on Design of Experiments, the validation set was used to optimize the preprocessing steps performed prior to Linear Discriminant Analysis. Using the optimized model, a minimum m/z fragment threshold was determined to allow analysts to assess whether an unknown spectrum is of sufficient abundance and quality to be compared to the model. To assess the robustness of the models, a test set was developed utilizing two instruments from a fourth laboratory that was not involved in the generation of the primary dataset in addition to spectra from widely used mass spectral libraries. Of the spectra that reached the threshold, the classification accuracy was 100% for all three isomer types. Only two of the test and validation spectra that did not reach the threshold were misclassified. The results indicate that forensic illicit drug experts world-wide can use these models for robust NPS isomer identification on the basis of preprocessed mass spectral data without the need for acquiring reference drug standards and creating instrument specific GC-MS reference datasets. The continued robustness of the models could be ensured through international collaboration to collect data that captures all potential GC-MS instrumental variation encountered in forensic illicit drug analysis laboratories. This would allow every forensic institute to confidently assign isomeric structures without the need for additional chemical analysis.

Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry

The differentiation of positional isomers is a well established analytical challenge for forensic laboratories. As more novel psychoactive substances (NPSs) are introduced to the illicit drug market, robust yet efficient methods of isomer identification are needed. Although current literature suggests that Direct Analysis in Real Time-Time-of-Flight mass spectrometry (DART-ToF) with in-source collision induced dissociation (is-CID) can be used to differentiate positional isomers, it is currently unclear whether this capability extends to positional isomers whose only structural difference is the precise location of a single substitution on an aromatic ring. The aim of this work was to determine whether chemometric analysis of DART-ToF data could offer forensic laboratories an alternative rapid and robust method of differentiating NPS positional ring isomers. To test the feasibility of this technique, three positional isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone) were analyzed. Using a linear rail for consistent sample introduction, the three isomers of each type were analyzed 96 times over an eight-week timespan. The classification methods investigated included a univariate approach, the Welch t test at each included ion; a multivariate approach, linear discriminant analysis; and a machine learning approach, the Random Forest classifier. For each method, multiple validation techniques were used including restricting the classifier to data that was only generated on one day. Of these classification methods, the Random Forest algorithm was ultimately the most accurate and robust, consistently achieving out-of-bag error rates below 5%. At an inconclusive rate of approximately 5%, a success rate of 100% was obtained for isomer identification when applied to a randomly selected test set. The model was further tested with data acquired as a part of a different batch. The highest classification success rate was 93.9%, and error rates under 5% were consistently achieved.

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

To address challenges associated with the increased prevalence of novel psychoactive substances (NPSs), laboratories often adopt new techniques or new methods with the goal of obtaining more detailed chemical information with a higher level of confidence. To demonstrate how new methods applied to existing techniques can be a viable approach, a targeted gas chromatography mass spectrometry (GC-MS) method for synthetic cathinones was developed. To create the method, a range of GC-MS parameters were first investigated using a seven-component test solution with the goal of minimizing compounds with overlapping acceptance windows by maximizing retention time differences within a reasonable runtime. Once developed, the targeted method was evaluated through several studies and was compared to a general GC-MS confirmatory method. The method produced a twofold increase in retention time differences of the test solution compounds with a 3.83-min shorter runtime than the general method. Limitations of the method were also studied by analyzing an additional forty-eight cathinones to identify instances where definitive compound identification may not be possible due to overlapping acceptance windows and mass spectra. Thirty-eight pairs of compounds had retention times differences of less than 2% and, of those thirty-eight, one pair had indistinguishable mass spectra. A set of case samples were also analyzed using the method to evaluate suitability for casework. An increase in split ratio was required to obtain acceptable sensitivity. The development of this method is part of a larger project to measure benefits and drawbacks of different drug chemistry workflows.

A framework for the development of targeted gas chromatography mass spectrometry (GC-MS) methods: Synthetic cannabinoids

With the increased presence of novel psychoactive substances (NPSs) in casework, drug analysis has become more challenging. To address these challenges, new screening technologies with improved specificity are being implemented, allowing for the creation and adoption of targeted confirmatory analyses that produce more conclusive results. This paper outlines a six-step, data-driven, framework to develop and evaluate gas chromatography mass spectrometry (GC-MS) methods for targeted classes of drugs. The process emphasizes maximizing retention time differences (to minimize the potential for retention time acceptance windows to overlap) and understanding the trade-offs between sensitivity and reproducibility using a test solution containing pairs of compounds that are difficult to distinguish. The method is then evaluated by expanding the panel of compounds analyzed, identifying limitations in compound discrimination, comparing to current methods, and analyzing representative casework to establish usability. To demonstrate this framework, a method for synthetic cannabinoids was created. The developed method utilizes a DB-200 column and an isothermal temperature program. It was found that sensitivity could be adjusted, without compromising reproducibility, by altering the split ratio and injection volume. The targeted method successfully differentiated 50 cannabinoids based on either retention time differences or mass spectral dissimilarity - determined using a newly developed spectral comparison test. Compared to a general method used for casework, the targeted method was an order of magnitude more sensitive, a minute shorter, and provided major increases in retention time differences. This framework can be implemented and adapted to develop targeted methods for other applications or compound classes.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.