Leogrande Patrizia, Botrè Francesco, Torre Xavier de la, Jardines Daniel, Parr Maria Kristina, Marini Federico
Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy.
Laboratorio Antidoping, Federazione Medico Sportiva Italiana, Largo Giulio Onesti 1, 00197, Rome, Italy; Center of Research and Expertise in Anti-Doping Sciences - REDs, ISSUL - Institute of sport sciences, University of Lausanne, Synathlon - Quartier Centre, 1015, Lausanne, Switzerland.
Talanta. 2021 May 15;227:122173. doi: 10.1016/j.talanta.2021.122173. Epub 2021 Feb 4.
Predictive models have been developed for the early identification of novel anabolic androgenic steroids and to obtain information on their molecular structure. To this purpose, gas-chromatographic and mass spectrometric characteristic parameters of 136 anabolic androgenic steroids have been specifically considered. Starting from Principal Component Analysis, different chemometric methods were applied, such as classification and clustering techniques, outlining a spectral and structural characterization for each steroid subclass, and considering the contribution of more than 30 variables. Mass spectrometric data on the TMS-derivatives of the target steroids were obtained by gas chromatography coupled to quadrupole-time of flight mass spectrometry using electron ionization. Steroids included in the training set were grouped in 5 subclasses according to their structural similarity, and the experimental data, processed by the chemometric models, allowed the identification of class-specific common fragments and spectral trends. The results of this study, validated on a test set of 21 steroids, have confirmed that the proposed approach allows tracing novel "designer anabolic steroids", including those previously unknown new structures that may have been designed and illicitly synthesized to be invisible to the current anti-doping tests.
已经开发出预测模型,用于早期识别新型合成代谢雄激素类固醇,并获取其分子结构信息。为此,专门考虑了136种合成代谢雄激素类固醇的气相色谱和质谱特征参数。从主成分分析开始,应用了不同的化学计量学方法,如分类和聚类技术,勾勒出每个类固醇亚类的光谱和结构特征,并考虑了30多个变量的贡献。目标类固醇的TMS衍生物的质谱数据通过气相色谱与四极杆飞行时间质谱联用并采用电子电离获得。训练集中的类固醇根据其结构相似性分为5个亚类,化学计量学模型处理的实验数据能够识别特定类别的共同碎片和光谱趋势。这项研究的结果在21种类固醇的测试集上得到验证,证实了所提出的方法能够追踪新型“设计合成代谢类固醇”,包括那些可能经过设计和非法合成从而在当前反兴奋剂检测中不可见的前所未知的新结构。