Thomas Kraemer

Phosphatidylethanols (PEth) are a group of abnormal phospholipids that are only formed in the presence of ethanol. Particularly, PEth 16:0/18:1 is a popular marker for ethanol abstinence monitoring in the 2–4 weeks preceding blood collection. While a number of studies previously investigated the long‐term stability of PEth 16:0/18:1 in whole blood, they came to conflicting results. An analytical liquid chromatography‐tandem‐mass spectrometry‐based method for the quantification of PEth 16:0/18:1 and 16:0/18:2 was fully validated and used to study the long‐term stability (up to 5 months) at different storage conditions (room temperature [RT], 4°C, −20°C, and −80°C) as well as stability during postal transport. PEth 16:0/18:1 was found to be stable until the end of the study period when stored at −20°C and −80°C but only showed acceptable stability for 1 month at 4°C and 7 days at RT. In accordance with previous studies, PEth 16:0/18:2 degraded rapidly during transport and storage at RT. For the first time, PEth de novo formation was investigated using whole blood samples from a controlled drinking experiment with and without the in vivo presence of ethanol (instead of spiked samples or animal experiments). Significant PEth 16:0/18:1 de novo formation when stored at −20°C and RT as early as 14 h after sample collection was found, which crucially highlights the importance of proper sample storage. Storage at −20°C should be prevented due to the potential for rapid de novo formation. Ideally, storage (short‐ and long‐term) at −80°C is preferred for reliable results.

Gamma‐hydroxybutyrate (GHB) detection in forensic toxicology is hampered by its rapid elimination and endogenous occurrence. GHB‐pentose has recently emerged as a promising biomarker capable of extending the detection window of GHB exposure but requires further evaluation in authentic forensic samples. We systematically investigated GHB‐pentose using two independent LC–MS/MS methods based on hydrophilic interaction liquid chromatography (HILIC) and reversed‐phase (RP) chromatography. Positive findings were evaluated against measured GHB concentrations, case information, matrix type, and concordance between analytical methods. Among 2414 urine cases, 97.6% were negative for GHB and GHB‐pentose. GHB‐pentose could be detected in all 21 GHB‐positive urine samples from living individuals and in four GHB‐positive postmortem cases, whereas 20 other postmortem urine samples with elevated GHB concentrations (10–26 mg/L) were negative for GHB‐pentose. Based on toxicological expert assessment performed independently prior to this study, it was assumed that the GHB detected in these cases was more likely the result of postmortem formation than of prior GHB intake. Fourteen GHB‐negative urine samples were positive for GHB‐pentose; eight of these contained case information supporting previous GHB exposure, including three suspected drug‐facilitated crimes sampled 13–16 h after the incident. GHB‐pentose appears to be a highly specific biomarker of exogenous GHB exposure, capable of extending the detection window beyond that of GHB itself and potentially serving as a discriminant between exogenous GHB administration and endogenous, particularly postmortem, GHB formation. Routine LC–MS/MS screening is feasible, even in the absence of a reference standard, supporting its immediate implementation in forensic toxicology workflows.

Sleep loss is a major contributor to traffic accidents, yet the differential effects of two common real-world patterns of sleep loss, acute total sleep deprivation and cumulative sleep restriction producing an equivalent 8-h sleep loss, on vehicle control remain poorly quantified. Leveraging tightly controlled sleep manipulation and highly immersive driving simulation, we directly compared the effects of different patterns of sleep loss under naturalistic driving conditions. Twenty healthy male participants completed a randomized within-subject crossover sleep design, comprising normal sleep, cumulative sleep restriction (four nights 2 h reduced sleep per night), and acute total sleep deprivation (one night without sleep), each followed by a simulated nighttime-to–early-morning commute. Acute total sleep deprivation produced pronounced impairments in vehicle control. Most prominently, lateral control deteriorated within 4–6 min under acute deprivation, with impairments comparable to those reported at high blood alcohol concentrations (1.2–1.4 g/L). Steering instability and integrated driving performance showed convergent deterioration. In contrast, cumulative sleep restriction resulted in smaller and less consistent changes, while vigilance task performance and speed variability were comparatively preserved across all conditions. Despite graded increases in subjective sleepiness across conditions, cumulative sleep restriction produced only limited changes in driving performance, whereas acute sleep deprivation produced a disproportionate risk to driving safety by selectively and rapidly degrading operational vehicle control. These findings, obtained under ecologically relevant sleep and driving conditions, underscore functional and potentially physiological differences between acute and cumulative sleep loss and, practically, highlight the importance of distinguishing between them when considering sleep-loss–related driving impairment and safety risk. Clinical Trials–name, URL, and registration: Clinical Trial name: Study of Identification of Metabolomics-based Sleepiness Markers for Risk Prevention and Traffic Safety—ClinicalTrials.gov Identifier NCT05585515, released on 18.10.2022, https://clinicaltrials.gov/study/NCT05585515; Swiss National Clinical Trial Portal SNCTP000005089, registered on 12.08.2022, https://www.humanforschung-schweiz.ch/de/studiensuche/studien-detail/58930. IRB Statement: This study was approved by the local ethics committee (Kantonale Ethikkommission Zürich, reference number 2022–01273). No identifying images or other personal or clinical details of participants are presented here or will be presented in reports of the trial results. Informed consent materials are available from the corresponding author on request. We confirm all methods were performed in accordance with the Declaration of Helsinki and its later amendments.

As sleep loss leads to accidents and impaired safety, a direct metabolic marker would be beneficial for forensic interpretation. In a sufficiently powered, randomized, controlled, crossover trial under realistic conditions, we examined the salivary metabolome of 20 young men (habitual sleep duration 7–9 h) following three interventions: one night of total sleep deprivation, four consecutive nights of sleep restriction to 6 h, and control (8 h of sleep). Oral fluid specimens were repeatedly collected and analyzed using liquid chromatography coupled to mass spectrometry. Logistic regression models were trained to classify unseen samples without reference samples from the same individual. Acute sleep deprivation exhibited a unique metabolic fingerprint that could be detected precisely (F_0.5 = 0.90) when using only 12 molecular features. This fingerprint was more pronounced in samples collected in the morning/midday hours. Nevertheless, at all time points, the overall correct predictions by far outweighed the incorrect ones. Four nights of sleep restriction did not lead to exploitable metabolic changes. This study presents a metabolic fingerprint of acute sleep deprivation in oral fluid under realistic conditions and explores practical implications and limitations of its machine learning-aided classification. Metabolomics-based, reference-free sleep loss detection holds potential for applications in forensic, clinical, and occupational contexts.

Sleep loss is a major contributor to traffic accidents, yet the differential effects of acute versus cumulative 8-hour sleep loss on vehicle control remain poorly quantified. This study leveraged tightly controlled sleep manipulation together with highly immersive driving simulation to directly compare sleep-loss modes under naturalistic driving conditions. To evaluate subjective sleepiness, driving performance and vigilance, healthy male participants completed a randomized within-subject crossover sleep design, comprising normal sleep, cumulative sleep restriction (four nights with 2 h reduced sleep per night), and acute total sleep deprivation (one night without sleep), each followed by a simulated nighttime-to-early-morning driving protocol segmented into modules. Linear mixed-effects models evaluated condition effects and time-on-task dynamics. Acute total sleep deprivation produced pronounced impairments in vehicle control. Most prominently, lateral control deteriorated rapidly under acute deprivation, with effect magnitudes comparable to those reported at high blood alcohol concentrations. Steering instability and integrated driving performance showed convergent deterioration. In contrast, cumulative sleep restriction resulted in smaller and less consistent changes, while vigilance task performance and speed variability were comparatively preserved across all conditions. Despite increased subjective sleepiness, cumulative sleep loss provoked relatively small changes in driving performance, whereas acute sleep deprivation produced a disproportionate risk to driving safety by selectively and rapidly degrading operational vehicle control. These findings, obtained under ecologically relevant sleep and driving conditions, underscore functional and potentially underlying physiological differences between acute and cumulative sleep loss and, practically, highlight the importance of distinguishing between them when considering sleep-loss-related driving impairment and safety risk.

Background/Objectives: Cytochrome P450 (CYP) enzymes are crucial for drug metabolism, yet inter-individual variability in their activity remains a significant clinical challenge. Current phenotyping methods are often impractical or even impossible, particularly in forensic toxicology and vulnerable populations. This proof-of-concept study investigated the feasibility of using in vitro assays with human liver microsomes (HLM) and recombinant CYP enzymes (isoenzymes), combined with untargeted metabolomics, to identify potential endogenous biomarker candidates indicative of CYP phenotype. Methods: This study uses in vitro incubations of HLM and isoenzymes in tandem with targeted and untargeted LC-(HR)MS and metabolomics techniques as well as statistical processing. Results: We demonstrate that HLM and isoenzymes maintain activity in the presence of complex biological matrices (blood/plasma), enabling metabolomic profiling. Untargeted analysis of assays in plasma revealed numerous potential biomarkers, with several showing significant correlations to enzyme activity. Conclusions: While identification remains the major challenge, this approach offers a promising avenue for developing accessible and efficient methods for indirect CYP phenotyping, potentially facilitating investigations in scenarios where traditional approaches are limited. This work provides a foundation for future studies focused on further developing in vitro assays and validating the proposed biomarkers, as well as establishing their utility in clinical and forensic settings.

Determining the bodily origin of biological traces is a valuable tool in forensic investigations as it helps corroborate testimonies, reconstruct crime-related activities, and select relevant samples for further analysis. Current body fluid identification (BFI) methods rely on enzymatic, spectroscopic, and chemical tests, which are often limited in sensitivity and specificity. Recent research has explored novel markers for BFI, for instance metabolites, based on their potential body fluid/tissue specificity. Metabolites are small molecules produced by human and microbial cellular processes that can be measured using advanced techniques like gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These methodologies remain underexplored for the identification of forensically relevant body fluids/tissues. In this pilot study, we employed a high-resolution, untargeted LC-quadrupole time-of-flight (QTOF)-MS approach to investigate body fluid/tissue specific markers from nine biological fluids/tissues, including feces, fingerprick blood, menstrual blood, saliva, semen, skin from palms, urine, vaginal fluid and venous blood. We used sparse partial least-squares discriminant analysis (sPLS-DA) to identify key features responsible for body fluid/tissue-specific clustering and generalized local learning (GLL) to select features directly associated with specific body fluids/tissues. Lastly, we present nine predictive features, one for each fluid/tissue, demonstrating that our approach has the potential to be used in forensic casework.

Intoxication cases involving new psychoactive substances (NPS) are known to provide various challenges for forensic toxicological case interpretation, starting with the identification of previously unknown substances. Furthermore, the pharmacological characteristics of these substances, including potency and metabolic processes, remain largely unstudied. In this particular medico-legal case, a 20-year-old man consumed clonazafone and fluoro-etonitazene, which were examined in blood by targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, a urine screening was conducted using LC-high-resolution mass spectrometry (HRMS) to investigate the metabolism of these substances, particularly clonazafone. Clonazafone was (semi-)quantified in urine (39 ng/mL), muscle tissue (3.0 ng/g), and stomach content (76’000 ng/mL), but could not be detected in peripheral blood, heart blood, and vitreous humor (lower limit of quantification: 0.1 ng/mL). Additionally, clonazepam (1.5 ng/mL) and its metabolite 7-amino-clonazepam (140 ng/mL), as well as amphetamine (110 ng/mL) and the designer-opioid fluoro-etonitazene (3.3 ng/mL) were found in blood. Within the HR screening, desglycylclonazafone, the intermediate of clonazafone that can be further converted into clonazepam, was detected in the stomach content and urine. Screening in urine has also revealed several metabolites of clonazafone. The cause of death was assumed to be a mixed drug intoxication with fluoro-etonitazene, clonazepam, and amphetamine.

Determining the bodily origin of biological traces is a valuable tool in forensic investigations as it helps corroborate testimonies, reconstruct crime-related activities, and select relevant samples for further analysis. Current body fluid identification (BFI) methods rely on enzymatic, spectroscopic, and chemical tests, which are often limited in sensitivity and specificity. Recent research has explored novel markers for BFI, for instance metabolites, based on their potential body site specificity. Metabolites are small molecules produced by human and microbial cellular processes that can be measured using advanced techniques like gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These methodologies remain underexplored for the identification of forensically relevant body fluids. In this pilot study, we employed a high-resolution, untargeted LC-quadrupole time of flight (QTOF)-MS approach to investigate body fluid specific markers from nine biological fluids, including feces, fingerprick blood, menstrual blood, saliva, semen, skin from palms, urine, vaginal fluid and venous blood. We used sparse partial least squares discriminant analysis (sPLS-DA) to identify key features responsible for site-specific clustering and Generalised Local Learning (GLL) to select features directly associated with specific body fluids. Lastly, we present nine predictive features, one for each fluid, demonstrating that our approach has the potential to be used in forensic casework.

Urine and hair are among the primary biological matrices used for drug and abstinence testing in clinical, forensic, and antidoping settings. A persistent challenge in such analyses is the adulteration of samples: through dilution, substitution, or chemical modification, aimed at concealing the presence of xenobiotics such as drugs of abuse, ethanol, or doping agents. In this study, we investigated whether chemical adulteration, specifically oxidative treatment with hydrogen peroxide (H₂O₂), induces detectable and characteristic changes in the proteomes of urine and hair samples. Using a bottom-up proteomics approach involving LC-HR-MS/MS with data-dependent acquisition, we compared urine samples before and after treatment (10% H₂O₂) and untreated hair samples with those exposed to increasing concentrations of hydrogen peroxide (3%, 6%, 9%, and 12%). Data are available via ProteomeXchange with the identifier PXD064208. We identified distinct peptides, including oxidatively modified forms, that were exclusively present in either untreated or chemically treated groups. In hair samples, the appearance of some of those peptides was dependent on the peroxide concentration. Peptides detectable only after oxidative exposure were of particular interest, as they appeared to be nonphysiological and specific to the adulteration process. These species serve as candidate biomarkers for indirect detection of sample manipulation or for assessing the integrity of compromised samples. The extraction and characterization of these potential marker peptides constitute the primary outcomes of this study. These findings should lay the groundwork for further validation and the development of proteomic methods aimed at enhancing the reliability of drug testing and sample authenticity assessment in forensic and antidoping contexts.