Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, Pennsylvania 19348, United States.
Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, Pennsylvania 19382, United States.
Anal Chem. 2021 Jun 1;93(21):7746-7753. doi: 10.1021/acs.analchem.1c01273. Epub 2021 May 21.
To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fifty-seven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The acquired LC-HRMS raw data files were processed using metabolomic software for compound detection and identification. For UDD with the resultant data, a mathematical model was created, and two algorithms were generated to calculate the ratio of the mean (ROM) and outlier index (OLI). Using ROM and OLI, the majority of the 57 drugs were accurately detected by name (52 of 57) or chemical formula (1 of 57). The limit of detection for the drugs was from tens of picograms to nanograms per milliliter. Xenobiotics and endogenous substances relevant to doping control were also identified using this untargeted approach following their extraction from real-world race samples, thus validating the UDD methodology. To the authors' knowledge, this is the first completely UDD methodological approach and represents significant advance toward using artificial intelligence for the detection of both known and emerging doping agents in sports.
为了解决当前靶向分析方法只能检测已知兴奋剂的局限性，开发了一种新的非靶向药物检测（UDD）方法学，以帮助打击体育中的兴奋剂。将 57 种药物掺入空白马血浆中，并将其视为未知物，因为未使用其确切质量和色谱保留时间进行检测。从血浆样品中提取掺入的药物，并使用液相色谱-高分辨率质谱联用仪（LC-HRMS）进行分析。使用代谢组学软件对获得的 LC-HRMS 原始数据文件进行处理，以进行化合物检测和鉴定。对于由此产生的数据进行的 UDD，创建了一个数学模型，并生成了两个算法来计算平均值比（ROM）和异常值指数（OLI）。使用 ROM 和 OLI，57 种药物中的大多数（52 种）都能准确地按名称（57 种中的 52 种）或化学式（57 种中的 1 种）进行检测。药物的检出限从几十皮克到纳克/毫升不等。通过从实际比赛样本中提取，使用这种非靶向方法还鉴定了与兴奋剂控制相关的外源性物质和内源性物质，从而验证了 UDD 方法学。据作者所知，这是第一种完全的 UDD 方法学方法，代表了在体育中使用人工智能检测已知和新兴兴奋剂方面的重大进展。
