Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti‐doping and veterinary residues.
The emergence of novel doping agents is a continuous issue for analysts who aim to maintain the integrity of horseracing together with the well-being and safety of the animals and riders involved. Untargeted mass spectrometric analysis presents a potential improvement for antidoping as it enables the detection of compounds being indirectly affected by an administered drug. In this study, liquid chromatographyhigh-resolution mass spectrometry was used to investigate a 12-horse administration study of the synthetic opioid, butorphanol. A mass spectrometric workflow capable of detecting metabolic differences for an extended period of time was successfully developed. This proof-of-concept study demonstrates the potential of untargeted workflows to provide a list of biomarkers of exposure and effect that are indicative of drug administration which may be implemented into routine testing for improved doping control.
The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.
The concept of biomarker measurements in the form of a ratio has not been explored in detail. This is surprising considering the current and future potential for biomarkers incorporating endogenous reference compounds (ERCs) in a range of fields. A selection of these relating to clinical and forensic applications, human antidoping, equine antidoping and veterinary residues are discussed.
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