Evidence map›Paper›PMID 39613988›Full record

ArticleAnalytical and bioanalytical chemistry2025

An OPRM1-SNAP-tag/CMC method to directly identify drug components in sewage.

Chenjia Li, Qi Liao, Rui Wang, Xinping Zhang, Mengyang Ma, Yonghong Liu, Lei Xiao, Ying Jiao, Nan Wang

Abstract read
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In one paragraph

Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chenjia Li *School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Qi Liao *National Narcotics Laboratory ShaanXi Regional Center, Xi'an, Shaanxi, China.
Rui WangNational Narcotics Laboratory ShaanXi Regional Center, Xi'an, Shaanxi, China.
Xinping ZhangSchool of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Mengyang MaSchool of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Yonghong LiuNational Narcotics Laboratory ShaanXi Regional Center, Xi'an, Shaanxi, China.
Lei XiaoNational Narcotics Laboratory ShaanXi Regional Center, Xi'an, Shaanxi, China.
Ying JiaoNational Narcotics Laboratory ShaanXi Regional Center, Xi'an, Shaanxi, China. jiaoyingedith@163.com.
Nan WangSchool of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Funding

National Narcotics Laboratory ShaanXi Regional Center GDS-ZZKJ-202302Natural science Basic research project of Shaanxi Province 2024JC-YBMS-123
6 · The paper itself

Abstract

The scourge of drug addiction and abuse poses a significant challenge to society. Opioid drugs acting on μ-opioid receptor (OPRM1) make it one of the pivotal targets for drug addiction. In the past decade, sewage analysis has become a prevalent method of drug monitoring. However, traditional methods of detecting drugs in sewage are cumbersome, and rapid detection methods are relatively lacking. To address this, an innovative OPRM1-SNAP-tag/CMC method to directly identify drug components in sewage was established. Cell membrane chromatography (CMC) is an affinity chromatography technique which effectively detects receptor affinity substances. Cells constructed with high expression of specific receptor could be used to screen for compounds acting on the receptor. CMC based on OPRM1 provides a potentially convenient and effective tool for the detection of targeted drug components in sewage. In this study, the selectivity, reproducibility, column lifetime, and carryover of the CMC column had been assessed. Initially, we eluted the collected domestic sewage with methanol and acetonitrile, and the retention peaks were observed on the CMC system. Subsequently, without any preliminary sample preparation, we directly injected filtered samples of suspicious sewage into the OPRM1-SNAP-tag/CMC system, where we observed retention peaks as well. The retained components were further identified as morphine by using UPLC-MS/MS. In conclusion, the OPRM1-SNAP-tag/CMC method stands out as a reliable and robust model for the detection of drug components in sewage. It provides a valuable analytical tool for frontline drug control efforts, enhancing our capacity to monitor and mitigate the impact of drug abuse on society.

Indexed as

Receptors, Opioid, muSewageAnalgesics, OpioidCell MembraneChromatography, AffinityHumansMorphineReproducibility of ResultsSubstance Abuse DetectionTandem Mass SpectrometryAnalgesics, OpioidMorphineOPRM1 protein, humanReceptors, Opioid, muSewageCMCDrug monitoringSewageUPLC-MS/MSμ-Opioid receptor

Identifiers

PMID39613988

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.