Towards a New Qualitative Screening Assay for Synthetic Cannabinoids Using Metabolomics and Machine Learning.

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.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.

OBJECTIVES: Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. METHODS: Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. RESULTS: Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. CONCLUSIONS: With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Human Metabolome Changes after a Single Dose of 3,4-Methylenedioxymethamphetamine (MDMA) with Special Focus on Steroid Metabolism and Inflammation Processes.

The intake of 3,4-methylenedioxymethamphetamine (MDMA) is known to increase several endogenous substances involved in steroid and inflammation pathways. Untargeted metabolomics screening approaches can determine biochemical changes after drug exposure and can reveal new pathways, which might be involved in the pharmacology and toxicology of a drug of abuse. We analyzed plasma samples from a placebo-controlled crossover study of a single intake of MDMA. Plasma samples from a time point before and three time points after the intake of a single dose of 125 mg MDMA were screened for changes of endogenous metabolites. An untargeted metabolomics approach on a high-resolution quadrupole time-of-flight mass spectrometer coupled to liquid chromatography with two different chromatographic systems (reversed-phase and hydrophobic interaction liquid chromatography) was applied. Over 10 000 features of the human metabolome were detected. Hence, 28 metabolites were identified, which showed significant changes after administration of MDMA compared with placebo. The analysis revealed an upregulation of cortisol and pregnenolone sulfate 4 h after MDMA intake, suggesting increased stress and serotonergic activity. Furthermore, calcitriol levels were decreased after the intake of MDMA. Calcitriol is involved in the upregulation of trophic factors, which have protective effects on brain dopamine neurons. The inflammation mediators hydroxyeicosatetraenoic acid, dihydroxyeicosatetraenoic acid, and octadecadienoic acid were found to be upregulated after the intake of MDMA compared with placebo, which suggested a stimulation of inflammation pathways.

A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - Proof of concept study using an artificial neural network for sample classification.

Liquid chromatography coupled to high-resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However, to avoid the handling of the resulting large dataset, most laboratories in that field still use targeted screening methods, which offer good sensitivity and specificity but are limited to known compounds. The promising field of machine learning offers new possibilities such as artificial neural networks that can be trained to classify large amounts of data. In this proof of concept study, we exemplify such a machine learning approach for raw HRMS-DIA data files. We evaluated a machine learning model using training, validation, and test sets of solvent and whole blood samples containing drugs (of abuse) common in forensic toxicology. For that purpose, different platforms were used. With a feedforward neural network model architecture, a category prediction (blank sample vs. drug containing sample) was aimed for. With the applied machine learning approaches, the sensitivity and specificity, of the validation and test set, for the prediction of sample classes were in a suitable range for an actual use in a (routine) laboratory (e.g. workplace drug testing). In conclusion, this proof of concept study clearly demonstrated the huge potential of machine learning in the analysis of HRMS-DIA data.