Machine learning- and multilayer molecular network-assisted screening hunts fentanyl compounds

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Fentanyl-Hunter: A New Tool to Find Fentanyl and Its Sneaky Metabolites

Researchers developed Fentanyl-Hunter, a new platform combining machine learning and molecular networking to detect fentanyl and its metabolites in various samples like wastewater and urine. The platform successfully identified known and novel fentanyl metabolites, suggesting wider use than previously thought.

Possible Conflicts of Interest

Some authors have a patent application related to the machine learning method used in the study.

Identified Weaknesses

Limited generalizability of in vitro findings

The in vitro study may not fully represent the complex metabolism happening in a living body, so the identified metabolites might not be the only or most important ones in real-life exposure.

Reliance on MS2 spectra

While the method uses high-quality MS2 spectra, it might miss low-abundance compounds and can't perfectly tell apart very similar molecules without reference standards, leading to potential false negatives or misclassifications.

Uncertainty in retrospective screening

The widespread presence detected in different countries relies on public datasets with varying sample types and preparation, making it hard to definitively conclude actual fentanyl prevalence and necessitating further studies.

Rating Explanation

This study presents a novel and potentially valuable tool for fentanyl detection with robust methodology. While limitations regarding in vitro vs. in vivo and dataset limitations exist, the study's contribution to the field warrants a good rating. The disclosed patent application is a potential conflict of interest that slightly lowers the rating.

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