In this study, we developed a novel on-line solid phase extraction (SPE)-ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS)-based analytical method for simultaneously quantifying 12 illicit drugs and metabolites (methamphetamine, amphetamine, morphine, codeine, 6-monoacetylmorphine, benzoylecgonine, 3,4-methylenedioxymethamphetamine, 3,4-methylenedioxyamphetamine, cocaine, ketamine, norketamine, and methcathinone) and cotinine (COT) in wastewater samples. The analysis was performed by loading 2 mL of the sample onto an Oasis hydrophilic-lipophilic balance cartridge and using a cleanup step (5% methanol) to eliminate interference with a total run time of 13 min. The isotope-labeled internal standard method was used to quantify the target substances and correct for unavoidable losses and matrix effects during the on-line SPE process. Typical analytical characteristics used for method validation were sensitivity, linearity, precision, repeatability, recovery, and matrix effects. The limit of detection (LOD) and limit of quantification (LOQ) of each target were set at 0.20 ng/L and 0.50 ng/L, respectively. The linearity was between 0.5 ng/L and 250 ng/L, except for that of COT. The intra- and inter-day precisions were <10.45% and 25.64%, respectively, and the relative recovery ranged from 83.74% to 162.26%. The method was used to analyze various wastewater samples from 33 cities in China, and the results were compared with the experimental results of identical samples analyzed using off-line SPE. The difference rate was between 19.91% and −20.44%, and the error range could be considered acceptable. These findings showed that on-line SPE is a suitable alternative to off-line SPE for the analysis of illicit drugs in samples.

A large number of cases showed that fentanyl (FEN) has become the main cause of death from illegal drug overdose owing to its potent effect on respiratory depression, which has emerged as a grave threat to public health and safety. However, traditional analytical methods require cost-prohibitive equipment, complex pretreatment procedures, and technically trained experts, thus highlighting the urgent need to develop a cost-effective, straightforward, and highly sensitive method to detect FEN. This work demonstrated a dual-readout sensor FGGC–AuNCs@Q7 for FEN detection, which is based on the molecular recognition and self-assembly between the macrocycle cucurbit[7]uril (Q7) and FEN, accompanying spontaneous visual Tyndall effect and fluorescence optical responses of the gold nanoclusters within seconds. A detection limit of 1 ng mL⁻¹ and a linear range of 9 to 148 000 ng mL⁻¹ were achieved for fluorescence detection on FEN, with favorable selectivity in the presence of other illicit drugs or common interferents. The proposed method has been proved by its satisfactory application for the analysis of human urine.

Although heroin and morphine are structural analogues and morphine is a metabolite of heroin, it is not known how the effect of each substance on metabolites in vivo differs. Heroin and morphine were administered to C57BL/6J mice in increasing doses from 2 to 25 and 3 to 9 mg kg⁻¹ (twice a day, i.p.), respectively, for 20 days. The animals underwent withdrawal for 5 days and were readministered the drugs after 10 days. Serum and urine analytes were profiled using gas chromatography-mass spectrometry (GC-MS), and metabolic patterns were evaluated based on metabonomics data. Metabonomics data showed that heroin administration changed metabolic pattern, and heroin withdrawal did not quickly restore it to baseline levels. A relapse of heroin exposure changed metabolic pattern again. In contrast, although the administration of morphine changed metabolic pattern, whether from morphine withdrawal or relapse, metabolic pattern was similar to control levels. The analysis of metabolites showed that both heroin and morphine interfered with lipid metabolism, the tricarboxylic acid (TCA) cycle and amino acid metabolism. In addition, both heroin and morphine increased the levels of 3-hydroxybutyric acid and citric acid but decreased the serum levels of 2-ketoglutaric acid and tryptophan. Moreover, heroin and morphine reduced the levels of aconitic acid, cysteine, glycine, and oxalic acid in urine. The results show 3-Hydroxybutyric acid, tryptophan, citric acid and 2-ketoglutaric acid can be used as potential markers of opiate abuse in serum, while oxalic acid, aconitic acid, cysteine, and glycine can be used as potential markers in urine.

Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg⁻¹ (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography–mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.