In morphine intoxication cases, forensic toxicologists are frequently confronted with the question of if the individual was opioid-tolerant or opioid-naïve, which can be investigated by hair analysis. However, interpretation of results can be challenging. Here, we report on hair testing for morphine and its metabolite hydromorphone following morphine intoxication without tolerance and upon chronic use. Two consecutive hair samples were collected after a non-fatal intoxication. Analysis comprised short hair segments and their initial wash water solutions. In the intoxications, morphine and hydromorphone levels were 3.3 to 56 pg/mg and at maximum 9.8 pg/mg, respectively. Both levels and hydromorphone to morphine ratios were significantly lower compared to chronic morphine use. In the non-fatal intoxication, the highest hydromorphone to morphine ratio was obtained in the segment corresponding to the time of intoxication. Morphine ratios of wash to hair were significantly higher in the intoxications compared to chronic use, being indicative of sweat/sebum contamination. We recommend including the analysis of hydromorphone and the initial wash solution in cases of morphine intoxications. Our study demonstrates that hydromorphone to morphine ratios can help in distinguishing single from chronic morphine use and in estimating the period of exposure when a consecutive hair sample can be collected in survived intoxications.
Background 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. Methods 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. Results 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. Conclusions 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.
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