Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples

A Review of Recent Progress in Drug Doping and Gene Doping Control Analysis

The illicit utilization of performance-enhancing substances, commonly referred to as doping, not only infringes upon the principles of fair competition within athletic pursuits but also poses significant health hazards to athletes. Doping control analysis has emerged as a conventional approach to ensuring equity and integrity in sports. Over the past few decades, extensive advancements have been made in doping control analysis methods, catering to the escalating need for qualitative and quantitative analysis of numerous banned substances exhibiting diverse chemical and biological characteristics. Progress in science, technology, and instrumentation has facilitated the proliferation of varied techniques for detecting doping. In this comprehensive review, we present a succinct overview of recent research developments within the last ten years pertaining to these doping detection methodologies. We undertake a comparative analysis, evaluating the merits and limitations of each technique, and offer insights into the prospective future advancements in doping detection methods. It is noteworthy that the continual design and synthesis of novel synthetic doping agents have compelled researchers to constantly refine and innovate doping detection methods in order to address the ever-expanding range of covertly employed doping agents. Overall, we remain in a passive position for doping detection and are always on the road to doping control.

Factors affecting untargeted detection of doping agents in biological samples

Doping control is essential for sports, and untargeted detection of doping agents (UDDA) is the holy grail for anti-doping strategies. The present study examined major factors impacting UDDA with metabolomic data processing, including the use of blank samples, signal-to-noise ratio thresholds, and the minimum chromatographic peak intensity. Contrary to data processing in metabolomics studies, both blank sample use (either blank solvent or plasma) and marking of background compounds were found to be unnecessary for UDDA in biological samples, the first such report to the authors' knowledge. The minimum peak intensity required to detect chromatographic peaks affected the limit of detection (LOD) and data processing time for untargeted detection of 57 drugs spiked into equine plasma. The ratio of the mean (ROM) of the extracted ion chromatographic peak area of a compound in the sample group (SG) to that in the control group (CG) impacted its LOD, and a small ROM value such as 2 is recommended for UDDA. Mathematical modeling of the required signal-to-noise ratio (S/N) for UDDA provided insights into the effect of the number of samples in the SG, the number of positive samples, and the ROM on the required S/N, highlighting the power of mathematics in addressing issues in analytical chemistry. The UDDA method was validated by its successful identification of untargeted doping agents in real-world post-competition equine plasma samples. This advancement in UDDA methodology will be a useful addition to the arsenal of approaches used to combat doping in sports.

Machine Learning-Assisted Rapid Screening of Four Types of New Psychoactive Substances in Drug Seizures

Over the past few years, new psychoactive substances (NPS) have become a global health and social problem because of their wide variety, constant structural renewal, vague legal definitions, and rapid adaptation to legal restrictions. The rapid structural modifications of NPS have posed significant challenges for the screening and identification of these new substances using traditional mass spectrometric techniques based on reference substances or a mass spectral database. Here, we propose supervised machine learning (ML) classification models such as k-nearest neighbors, support vector machine, random forest, and multigrained cascade forest for the rapid screening of NPS using mass spectrometric data. This approach utilizes ML methods to learn the statistical probability distributions of mass spectral data for NPS and non-NPS. Four classification ML models were generated and evaluated using a data set comprising 567 LC-MS and 732 GC-MS spectra. Through cross validation, we achieved an F1 score of 0.35-0.97. These algorithms were applied in conjunction with mass spectrometry techniques for the detection of six seizures including electronic cigarette oil and suspected powdered substances netted in drug trafficking cases. The models provided warning signals for synthetic cannabinoids, synthetic cathinones, and fentanyl. Thus, an early warning system was successfully established, which provided a useful method for reliable and effective identifications of unknown NPS.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

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.