Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Department of Forensic Imaging/Virtopsy, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Clin Chem. 2022 Jun 1;68(6):848-855. doi: 10.1093/clinchem/hvac045.
Synthetic cannabinoids (SCs) are steadily emerging on the drug market. To remain competitive in clinical or forensic toxicology, new screening strategies including high-resolution mass spectrometry (HRMS) are required. Machine learning algorithms can detect and learn chemical signatures in complex datasets and use them as a proxy to predict new samples. We propose a new screening tool based on a SC-specific change of the metabolome and a machine learning algorithm.
Authentic human urine samples (n = 474), positive or negative for SCs, were used. These samples were measured with an untargeted metabolomics liquid chromatography (LC)-quadrupole time-of-flight-HRMS method. Progenesis QI software was used to preprocess the raw data. Following feature engineering, a random forest (RF) model was optimized in R using a 10-fold cross-validation method and a training set (n = 369). The performance of the model was assessed with a test (n = 50) and a verification (n = 55) set.
During RF optimization, 49 features, 200 trees, and 7 variables at each branching node were determined as most predictive. The optimized model accuracy, clinical sensitivity, clinical specificity, positive predictive value, and negative predictive value were 88.1%, 83.0%, 92.7%, 91.3%, and 85.6%, respectively. The test set was predicted with an accuracy of 88.0%, and the verification set provided evidence that the model was able to detect cannabinoid-specific changes in the metabolome.
An RF approach combined with metabolomics enables a novel screening strategy for responding effectively to the challenge of new SCs. Biomarkers identified by this approach may also be integrated in routine screening methods.
合成大麻素 (SCs) 在毒品市场上不断涌现。为了在临床或法医毒理学中保持竞争力，需要包括高分辨率质谱 (HRMS) 在内的新筛选策略。机器学习算法可以检测和学习复杂数据集中的化学特征，并将其用作预测新样本的代理。我们提出了一种基于 SC 代谢组特异性变化和机器学习算法的新筛选工具。
使用了来自真实人体尿液样本（n = 474），这些样本被检测为含有或不含有 SC。这些样本使用非靶向代谢组学液相色谱 (LC)-四极杆飞行时间-HRMS 方法进行测量。Progenesis QI 软件用于预处理原始数据。在特征工程之后，使用 10 折交叉验证方法和训练集（n = 369）在 R 中优化随机森林 (RF) 模型。使用测试集（n = 50）和验证集（n = 55）评估模型性能。
在 RF 优化过程中，确定了 49 个特征、200 棵树和每个分支节点的 7 个变量作为最具预测性的特征。优化模型的准确性、临床灵敏度、临床特异性、阳性预测值和阴性预测值分别为 88.1%、83.0%、92.7%、91.3%和 85.6%。测试集的预测准确性为 88.0%，验证集证明该模型能够检测到代谢组中与大麻素特异性相关的变化。
RF 方法与代谢组学相结合，为有效应对新 SC 带来的挑战提供了一种新的筛选策略。该方法鉴定的生物标志物也可以整合到常规筛选方法中。
