Guan Fuyu, You Youwen, Fay Savannah, Adreance Matthew A, McGoldrick Leif K, Robinson Mary A
Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA.
Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA.
Talanta. 2023 Jun 1;258:124446. doi: 10.1016/j.talanta.2023.124446. Epub 2023 Mar 11.
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.
兴奋剂检测对体育赛事至关重要，而对兴奋剂的非靶向检测（UDDA）是反兴奋剂策略的圣杯。本研究利用代谢组学数据处理方法，研究了影响UDDA的主要因素，包括空白样品的使用、信噪比阈值和最小色谱峰强度。与代谢组学研究中的数据处理不同，本研究发现，对于生物样品中的UDDA，使用空白样品（空白溶剂或血浆）和标记背景化合物都是不必要的，据作者所知，这是首份此类报告。检测色谱峰所需的最小峰强度影响了对添加到马血浆中的57种药物进行非靶向检测的检测限（LOD）和数据处理时间。样品组（SG）中化合物的提取离子色谱峰面积与对照组（CG）中该化合物的提取离子色谱峰面积的平均值之比（ROM）影响其LOD，建议UDDA采用2这样的小ROM值。对UDDA所需信噪比（S/N）进行数学建模，深入了解了SG中的样品数量、阳性样品数量和ROM对所需S/N的影响，凸显了数学在解决分析化学问题方面的作用。通过成功鉴定实际比赛后马血浆样品中的非靶向兴奋剂，验证了UDDA方法。UDDA方法的这一进展将成为打击体育赛事中兴奋剂使用的一系列方法中的一项有益补充。
