Search

Multiple analytical techniques for the screening of fentanyl‐related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time‐consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface‐enhanced Raman scattering (EC‐SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low‐concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl‐related compounds and other drugs of abuse using EC‐SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In‐house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine‐learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10‐fold cross‐validation. The correct identification for the in‐house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components. [ABSTRACT FROM AUTHOR]

Valerylfentanyl, a novel synthetic opioid less potent than fentanyl, has been reported in biological samples, but there are limited studies on its pharmacokinetic properties. The goal of this study was to elucidate the metabolism of valerylfentanyl using an in vitro human liver microsome (HLM) model compared with an in vivo zebrafish model. Nineteen metabolites were detected with N‐dealkylation—valeryl norfentanyl and hydroxylation as the major metabolic pathways. The major metabolites in HLMs were also detected in 30 day postfertilization zebrafish. An authentic liver specimen that tested positive for valerylfentanyl, among other opioids and stimulants, revealed the presence of a metabolite that shared transitions and retention time as the hydroxylated metabolite of valerylfentanyl but could not be confirmed without an authentic standard. 4‐Anilino‐N‐phenethylpiperidine (4‐ANPP), a common metabolite to other fentanyl analogs, was also detected. In this study, we elucidated the metabolic pathway of valerylfentanyl, confirmed two metabolites using standards, and demonstrated that the zebrafish model produced similar metabolites to the HLM model for opioids. [ABSTRACT FROM AUTHOR]

The increased abuse of novel drugs has created a critical need for cheap and rapid in vivo models to understand whole organism drug‐induced toxicity and metabolic impacts. One such model is zebrafish, which share many similarities to human. Assays have been developed for behavioral, toxicity, and metabolism elucidation following chemical exposure. The zebrafish model provides the advantage of assessing these parameters within a single study. Previous zebrafish studies have evaluated the behavioral effects of fentanyl, but not developmental toxicity and its relation to metabolism. In this study, we evaluate the effects of fentanyl on the development of wild‐type (TL strain) zebrafish and its metabolism over 4 days. Fertilized eggs were exposed to six concentrations of fentanyl (0.01, 0.1, 1, 10, 50, and 100 μM) through embryo media incubated at 28–29°C. Observations included egg coagulation, somite formation, heartbeat, tail and yolk morphology, pericardial formation, and swim bladder inflation. The incubation media was analyzed for the presence of metabolites using a targeted metabolomics approach. Fentanyl concentration caused significant effects on survival and development, with notable defects to the tail, yolk, and pericardium at 50 and 100 μM. Despropionyl fentanyl (4‐ANPP), β‐hydroxy fentanyl, and norfentanyl were detected in zebrafish larvae. We present a single in vivo model to assess toxicity and metabolism of fentanyl exposure in a vertebrate model system. Our findings provide a foundation for further investigations into fentanyl's mechanism of action and translation to human drug exposure. The increased abuse of novel drugs has created a need for cheap and rapid in vivo models to understand whole organism drug‐induced toxicity and metabolic impacts. In this study, we evaluate the effects of fentanyl on the development of zebrafish and its metabolism. We report metabolites and significant developmental defects in zebrafish larvae upon exposure to fentanyl. Our findings provide a foundation for further investigations into fentanyl's mechanism of action and translation to human drug exposure. [ABSTRACT FROM AUTHOR]

Rationale: N‐(1‐Amino‐1‐oxo‐3‐phenylpropan‐2‐yl)‐1‐(5‐fluoropentyl)‐1H‐indole‐3‐carboxamine (PX‐1), N‐(1‐amino‐1‐oxo‐3‐phenylpropan‐2‐yl)‐1‐(5‐fluoropentyl)‐1H‐indazole‐3‐carboxamide (PX‐2), and N‐(1‐amino‐1‐oxo‐3‐phenylpropan‐2‐yl)‐1‐(cyclohexylmethyl)‐1H‐indazole‐3‐carboxamide (PX‐3) are scheduled synthetic cannabinoids (SCs). Due to the lack of metabolism data and the extensive metabolism of SCs, consumption of these illicit substances is challenging to prove. The aim of this study was to investigate the metabolism of PX‐1, PX‐2, and PX‐3 and propose marker metabolites to confirm their use. Methods: PX‐1, PX‐2, and PX‐3 were incubated in pooled human liver microsomes (HLM) to evaluate the phase I metabolites which were identified using a Q‐Exactive hybrid quadrupole‐Orbitrap mass spectrometer. Metabolic stability studies were also performed to investigate the half‐life and clearance rates using an Agilent 6470A triple quadrupole mass spectrometer. Results: The calculated half‐lives were 15.1 ± 1.02, 3.4 ± 0.27, and 5.2 ± 0.89 min for PX‐1, PX‐2, and PX‐3, respectively, in HLM. The calculated intrinsic clearance values were 0.046, 0.202, and 0.133 mL/min/mg for PX‐1, PX‐2, and PX‐3, respectively. Four metabolites of PX‐1, six metabolites of PX‐2, and five phase I metabolites of PX‐3 were detected. Oxidative deamination was the common biotransformation between the three compounds and there were no common metabolites. Conclusions: The metabolic profiles of PX‐1, PX‐2, and PX‐3 provide valuable information for the detection of their metabolites in forensic samples. [ABSTRACT FROM AUTHOR]

Image Credit: Luis Arroyo. The cover image is based on the Research Article I Evaluation of fentanyl toxicity and metabolism using a zebrafish model i by Travon Cooman et al., https://doi.org/10.1002/jat.4253. [Extracted from the article]