A mathematical model of competition between fiber and mucin degraders in the gut provides a possible explanation for mucus thinning.

The human gut microbiota relies on complex carbohydrates (glycans) for energy and growth, primarily dietary fiber and host-derived mucins. We introduce a mathematical model of a glycan generalist and a mucin specialist in a two-compartment chemostat model of the human colon. Our objective is to characterize the influence of dietary fiber and mucin supply on the abundance of mucin-degrading species within the gut ecosystem. Current mathematical gut reactor models that include the enzymatic degradation of glycans do not differentiate between glycan types and their degraders. The model we present distinguishes between a generalist that can degrade both dietary fiber and mucin, and a specialist species that can only degrade mucin. The integrity of the colonic mucus barrier is essential for overall human health and well-being, with the mucin specialist Akkermanisa muciniphila being associated with a healthy mucus layer. Competition, particularly between the specialist and generalists like Bacteroides thetaiotaomicron, may lead to mucus layer erosion, especially during periods of dietary fiber deprivation. Our model treats the colon as a gut reactor system, dividing it into two compartments that represent the lumen and the mucus of the gut, resulting in a complex system of ordinary differential equations with a large and uncertain parameter space. To understand the influence of model parameters on long-term behavior, we employ a random forest classifier, a supervised machine learning method. Additionally, a variance-based sensitivity analysis is utilized to determine the sensitivity of steady-state values to changes in model parameter inputs. By constructing this model, we can investigate the underlying mechanisms that control gut microbiota composition and function, free from confounding factors.

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%).

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).

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.

Incorporating measurement variability when comparing sets of high-resolution mass spectra.

Mass spectra are an important signature by which compounds can be identified. We recently formulated a mathematical approach for incorporating measurement variability when comparing sets of high-resolution mass spectra. Leveraging replicate mass spectra, we construct high-dimensional consensus mass spectra-representing each of the compared analytes-and compute the similarity between these data structures. In this paper, we present this approach and discuss its applications and limitations when trying to discriminate methamphetamine and phentermine using in-source collision induced dissociation mass spectra collected with direct analysis in real time mass spectrometry.

Qualitative Analysis of Real Drug Evidence Using DART-MS and the Inverted Library Search Algorithm.

Chromatographic-less mass spectrometry techniques like direct analysis in real-time mass spectrometry (DART-MS) are steadily being employed as seized drug screening tools. However, these newer analytical platforms require new computational methods to best make use of the collected data. The inverted library search algorithm (ILSA) is a recently developed method designed specifically for working with mass spectra of mixtures collected with DART-MS and has been implemented as a function in the NIST/NIJ DART-MS data interpretation tool (DIT). This paper demonstrates how DART-MS and the ILSA/DIT can be used to analyze seized drug evidence, while discussing insights gathered during the evaluation of 92 adjudicated case samples. The evaluation verified that the combination of DART-MS and the ILSA/DIT can be used as an informative tool to help analysts screen seized drug evidence but also revealed several factors─such as the influence of incorporating multiple in-source fragmentation spectra and the effect of scoring thresholds─an analyst must consider while employing these methods. Use cases demonstrating the benefit of the nonscoring metrics provided by the ILSA/DIT and demonstrating how the ILSA/DIT can be used to identify novel substances are also presented. A summary of considerations for using the ILSA/DIT for drug screening concludes this paper.

Updates to the Inverted Library Search Algorithm for Mixture Analysis.

Identifying mixture components is a well-known challenge in analytical chemistry. The Inverted Library Search Algorithm is a recently proposed method for identifying mixture components using in-source collision induced dissociation (is-CID) mass spectra of a query mixture and a reference library of pure compound is-CID mass spectra ( J. Am. Soc. Mass Spectrom. 2021, 32 (7), 1725-1734). This article presents several subtle but important advances to the algorithm, including updated compound matching strategies that improve result explainability and spectral filtering to better handle noisy mass spectra as is often observed with real-world samples such as seized drug evidence.

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.

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.

A New Library-Search Algorithm for Mixture Analysis Using DART-MS.

Forensic analysis of seized drug evidence often involves determining whether the components of an unknown mixture are illicit compounds. One approach to this task is to screen the evidence using direct analysis in real time mass spectrometry (DART-MS) to make presumptive identifications. This manuscript introduces a new library-search algorithm that enhances presumptive identifications of mixture components using a series of in-source collision-induced dissociation mass spectra collected through DART-MS. The multistage search, titled the Inverted Library-Search Algorithm (ILSA), identifies potential components in a mixture by first searching the lowest fragmentation mass spectrum for target peaks, assuming these peaks are protonated molecules, and then scoring each target peak with possible library matches. As a proof of concept, the ILSA is demonstrated through several example searches of model seized drug mixtures of acetyl fentanyl, benzyl fentanyl, amphetamine, and methamphetamine searched against a small library of select compounds and the freely available NIST DART-MS Forensics Database. Discussion of the search results and several open areas of research to further extend the method are provided. This new approach for presumptive identification provides analysts with refined information about mixture components and will be of immediate importance in forensic analysis using DART-MS. A prototype implementation of the ILSA is available at https://github.com/asm3-nist/DART-MS-DST.

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.

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.

Creation and Release of an Updated NIST DART-MS Forensics Database.

Facing increasing caseloads and an everchanging drug landscape, forensic laboratories have been implementing new analytical tools. Direct analysis in real time mass spectrometry (DART-MS) is often one of these tools because it provides a wealth of information from a rapid, simple analysis. The data produced by these systems, while extremely useful, can be difficult to interpret, especially in the case of complex mixtures, and therefore, mass spectral databases are often used to assist in interpretation of data. Development of these databases can be expensive and time-consuming and often relies on manual evaluation of the underlying data. The National Institute of Standards and Technology (NIST) released an initial DART-MS in-source collisional-induced dissociation mass spectral database for seized drugs in the early 2010s but it has not been updated to reflect the increasing prevalence of novel psychoactive substances. Recently, efforts to update the database have been undertaken. To assist in development of the database, an automated data evaluation process was also created. This manuscript describes the new NIST DART-MS Forensics Database and the steps taken to automate the data evaluation process.

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.

Combining Fragment-Ion and Neutral-Loss Matching during Mass Spectral Library Searching: A New General Purpose Algorithm Applicable to Illicit Drug Identification.

A mass spectral library search algorithm that identifies compounds that differ from library compounds by a single "inert" structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compounds, to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of "nearest-neighbor" compounds that differ from library compounds by a single chemical moiety. Additionally, determination of the structural origin of the shifted peaks can aid in the determination of the chemical structure and fragmentation mechanism of the query compound. A variety of examples are presented, including the identification of designer drugs and chemical derivatives not present in the library.

A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon.

A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed.

Assessing the influence of reactor system design criteria on the performance of model colon fermentation units.

Fermentation reactor systems are a key platform in studying intestinal microflora, specifically with respect to questions surrounding the effects of diet. In this study, we develop computational representations of colon fermentation reactor systems as a way to assess the influence of three design elements (number of reactors, emptying mechanism, and inclusion of microbial immobilization) on three performance measures (total biomass density, biomass composition, and fibre digestion efficiency) using a fractional-factorial experimental design. It was determined that the choice of emptying mechanism showed no effect on any of the performance measures. Additionally, it was determined that none of the design criteria had any measurable effect on reactor performance with respect to biomass composition. It is recommended that model fermentation systems used in the experimenting of dietary effects on intestinal biomass composition be streamlined to only include necessary system design complexities, as the measured performance is not benefited by the addition of microbial immobilization mechanisms or semi-continuous emptying scheme. Additionally, the added complexities significantly increase computational time during simulation experiments. It was also noted that the same factorial experiment could be directly adapted using in vitro colon fermentation systems.