Software-assisted automated detection and identification of "unknown" analogues of benzodiazepines in liquid chromatography mass spectrometry analysis.

RATIONALE: Benzodiazepines (BZDs) construct a large group of psychoactive drugs acting as depressants of the central nervous system (CNS) and used in medicine as sedatives and anxiolytic and antiepileptic agents. The illicit use of these materials is a worldwide problem, and for many years, part of the benzodiazepines have been abused as rape drugs. For example, flunitrazepam (Rohypnol) is most commonly linked by media reports to drug-facilitated sexual assaults, more commonly referred to as "date rape." Furthermore, there are growing concerns for other misuses of these drugs. Over the last few years, there was an increase in the number, type, and availability of new psychoactive substances (NPS) belonging to the benzodiazepine group, challenging standard forensic labs to fully identify the chemical structure of new, unknown benzodiazepines. METHODS: This work demonstrates a new application of the automated tool for the detection and identification of benzodiazepine analogues using high-resolution-accurate-mass LC-MS analysis, followed by "Compound Discoverer" (CD) software data processing, to automatically detect various benzodiazepine analogues by picking peaks and compare them to in silico calculated modifications made on a predefined basic backbone. Subsequently, a complete structural elucidation for the proposed molecular formula is obtained by MS/MS data analysis of the suspected component. RESULTS: This method was found to be useful for the automated detection and putative identification of a series of nine "unknown" benzodiazepine analogues, at concentrations in the low ng/mL range. CONCLUSIONS: We hereby present a general demonstration of this powerful tool for the forensic community in the detection and identification of hazardous unknown compounds.

Software-assisted automated detection and identification of "unknown" fentanyl analogues.

Fentanyl and its non-pharmaceutical analogues (NPFs) are potent synthetic opioids, traditionally used for pain management, with ever-increasing illicit uses. Tightening the regulation for known fentanyls leads to new synthetic analogues in the opioid market. Furthermore, the Organization for the Prohibition of Chemical Weapons (OPCW) has recently issued a decision regarding aerosolized use of central nervous system (CNS)-acting agents, such as fentanyl and its analogues, under the concern that these materials could be misused for terror or war purposes. The ever-increasing development of new fentanyl analogues makes the task of detection and identification of these new, unknown analogues crucial. In this work, we introduce an automated tool for the detection and putative identification of "unknown" fentanyl analogues, using liquid chromatography-mass spectrometry (LC-MS) (high-resolution mass spectrometry [HRMS]) analysis, subsequently followed by data processing using the "Compound Discoverer" software. This software, in our modified use, enabled the automatic detection of various fentanyl analogues, by "digging" out components and comparing them to pre-calculated theoretical molecular ions of possible modifications or transformations on the fentanyl backbone structure (no library or database used). Subsequently, structural elucidation for the proposed component of interest is carried out by automated MS/MS data interpretation, as performed by the software. This method was explored on 12 fentanyl-based "unknown" analogues used as model examples, including chemical modifications such as fluorination and methylation. In all tested compounds, automatic detection and identification were achieved, even at concentrations as low as 1 ng/mL in an environmental soil matrix extract.

Evaluation of Matrix Complexity in Nontargeted Analysis of Small-Molecule Toxicants by Liquid Chromatography-High-Resolution Mass Spectrometry.

Complex mixtures, characterized by high density of compounds, challenge trace detection and identification. This is further exacerbated in nontargeted analysis, where a compound of interest may be well hidden under thousands of matrix compounds. We studied the effect of matrix complexity on nontargeted detection (peak picking) by LC-MS/MS (Orbitrap) analysis. A series of ∼20 drugs, V-type chemical warfare agents and pesticides, simulating toxic unknowns, were spiked at various concentrations in several complex matrices including urine, rosemary leaves, and soil extracts. Orbitrap "TraceFinder" software was used to explore their peak intensities in relation to the matrix (peak location in an intensity-sorted list). Average practical detection limits of nontargets were determined. While detection among the first 10,000 peaks was achieved at 0.3-1 ng/mL levels in the extract, for the more realistic "top 1000" list, much higher concentrations were required, approaching 10-30 ng/mL. A negative power law functional dependence between the peak location in an intensity-sorted suspect list and the nontarget concentration is proposed. Controlled complexity was explored with a series of urine dilutions, resulting in an excellent correlation between the power law coefficient and dilution factor. The intensity distribution of matrix peaks was found to spread (unevenly) on a broad range, fitting well the Weibull distribution function with all matrices and extracts. The quantitative approach demonstrated here gives a measure of the actual capabilities and limitations of LC-MS in the analysis of nontargets in complex matrices. It may be used to estimate and compare the complexity of matrices and predict the typical detection limits of unknowns.

Aroma Characterization of Roasted Meat and Meat Substitutes Using Gas Chromatography-Mass Spectrometry with Simultaneous Selective Detection and a Dedicated Software Tool, AromaMS.

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples.

pyAIR-A New Software Tool for Breathomics Applications-Searching for Markers in TD-GC-HRMS Analysis.

Volatile metabolites in exhaled air have promising potential as diagnostic biomarkers. However, the combination of low mass, similar chemical composition, and low concentrations introduces the challenge of sorting the data to identify markers of value. In this paper, we report the development of pyAIR, a software tool for searching for volatile organic compounds (VOCs) markers in multi-group datasets, tailored for Thermal-Desorption Gas-Chromatography High Resolution Mass-Spectrometry (TD-GC-HRMS) output. pyAIR aligns the compounds between samples by spectral similarity coupled with retention times (RT), and statistically compares the groups for compounds that differ by intensity. This workflow was successfully tested and evaluated on gaseous samples spiked with 27 model VOCs at six concentrations, divided into three groups, down to 0.3 nL/L. All analytes were correctly detected and aligned. More than 80% were found to be significant markers with a p-value < 0.05; several were classified as possibly significant markers (p-value < 0.1), while a few were removed due to background level. In all group comparisons, low rates of false markers were found. These results showed the potential of pyAIR in the field of trace-level breathomics, with the capability to differentially examine several groups, such as stages of illness.

A multiple-method comparative study using GC-MS, AMDIS and in-house-built software for the detection and identification of "unknown" volatile organic compounds in breath.

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath-exhaled VOCs are often "unknowns" and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof-of-concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid-phase microextraction and thermal desorption (TD) tubes with two GC-mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02-20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in-house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use "raw" AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid-19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.

Explosive vapour/particles detection using SERS substrates and a hand-held Raman detector.

We developed and optimized surface-enhanced Raman spectrometry (SERS) methods for trace analysis of explosive vapour and particles using a hand-held Raman spectrometer in the field. At first, limits of detection (LODs) using SERS methods based on a colloidal suspension of gold nanoparticles were measured under alkaline conditions and are as follows: pentaerythritol tetranitrate (PETN) (1.5 × 10-6 M, 6.9 ng), 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX), 8.1 × 10-6 M, 35 ng; urea nitrate (UN), 9.2 × 10-4 M, 165 ng; 2,4,6-trinitrotoluene (TNT), 1.1 × 10-7 M, 0.35 ng. We developed SERS substrates that demonstrate the wide applicability of this technique for use in the field for explosive vapour and particles adsorbed on a surface based on Au nanoparticles that were optimal for the detection of the target materials in solution. Au nanoparticles were modified onto quartz fibres or a polyurethane sponge for vapour/particles detection. SERS detection of vapours of 2,4-dinitrotoluene (2,4-DNT) and 1,3-dinitrobenzene (1,3-DNB) was shown by sampling vapours onto Au-modified quartz fibres followed by hand-held Raman analysis with estimated minimum detection levels of 3.6 ng and 54 ng, respectively. The detection of 2,4-DNT using sponge-based SERS decorated with Au nanoparticles was also demonstrated; however, the sensitivity was lower than that observed using quartz fibres. The detection of TNT on a surface was performed by utilizing quartz-fibres precoated with alumina and modified with Au nanoparticles, and the detection of 10 µg (0.53 µg cm-2) of TNT was demonstrated.

Highly sensitive retrospective determination of organophosphorous nerve agent biomarkers in human urine implemented in vivo in rabbit.

Highly toxic organophosphorous nerve agents (OPAs) have been used in several armed conflicts and terror attacks in the last few decades. A new method for retrospective determination of alkyl methylphosphonic acid (AMPA) metabolites in urine after exposure to VX, GB and GF nerve agents was developed. This method enables a rapid, sensitive and selective determination of trace levels of the nerve agent biomarkers ethyl methylphosphonic acid (EMPA), isopropyl methylphosphonic acid (IMPA) and cyclohexyl methylphosphonic acid (CMPA) in urine. The new technique involves a unique combination of two solid phase extraction (SPE) cartridges: a Ba/Ag/H cartridge for urine interference removal, and a ZrO2 cartridge for selective reconstitution and enrichment of the AMPAs. Extraction of AMPAs from the ZrO2 cartridge was accomplished with a 1% ammonium hydroxide (NH4OH) solution and was followed by analysis via liquid chromatography-mass spectrometry (LC-MS). The limits of quantitation (LOQs) were in the range of 10-100 pg/mL with recoveries of 64-71% (± 5-19%) after fast sample preparation and a total LC-MS analysis cycle time of 15 min and 13 min, respectively. This method was successfully applied in vivo in a rabbit that was exposed to 0.5 LD50 (7.5 µg/kg, i.v.) sarin for retrospective monitoring of the IMPA metabolite in urine. For the first time, IMPA was determined in rabbit urine samples for 15 days post-exposure, which is longer than any reported post-exposure method for AMPAs. To the best of our knowledge, this new method is the most sensitive and rapid for AMPA determination in urine by LC-MS/MS analysis.

Instantaneous monitoring of free sarin in whole blood by dry blood spot-thermal desorption-GC-FPD/MS analysis.

Dry blood spot (DBS), a micro whole-blood sampling technique, enables rapid and self-blood collection; it is stable and economical. Currently, DBS filters require various sample preparation procedures specifically tailored for the target compounds, which are followed by GC-MS or LC-MS analysis. However, the small amounts of blood make the approach analytically challenging, mostly in terms of sensitivity and quantification. Herein, we introduce a new DBS concept for GC-compatible volatile to semi-volatile compounds in which DBS is directly coupled with thermal desorption analysis, thus eliminating time consuming treatments. Furthermore, to stabilize the target compound over the sampling DBS substrate, a commercial filter based on an extremely efficient trapping adsorption phase, styrene-divinylbenzene (SDVB), is first used. The performance of the new SDVB-DBS concept was demonstrated herein for monitoring the most volatile chemical warfare agent, sarin, which might be present in blood and the detection of which is usually challenging due to its rapid metabolism. This study encompasses adequate sampling and analysis method parametrization and validation, leading to a detection sensitivity of 100 pg sarin per 30 µL whole blood in 5-day-old samples, with a linear dynamic range of two orders of magnitude, adequate precision, and acceptable accuracy. Applying the method to an in-vivo mouse intranasal exposure experiment (3LD50 GB) enabled the successful detection of 25-90 ng mL-1 free sarin in blood samples drawn 2 min after exposure. The method's performance clearly emphasizes the potential of the new concept in "freezing the clock" for reactive whole blood media in pharmacokinetics and pharmacodynamics studies, as well as in applications in which informative and reliable monitoring of unstable target compounds and biomarkers is desired.