Evidence map›Paper›PMID 40025270›Full record

ArticleAnalytical sciences : the international journal of the Japan Society for Analytical Chemistry2025

Quantitative analysis of related substances in acetaminophen tablets using pre-column derivatization coupled with HPLC-ICP-MS.

Yalan Liu, Yaru Peng, Qiping Wu, Zeneng Cheng, Feifan Xie, Sanwang Li

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Article in Analytical sciences : the international journal of the Japan Society for Analytical Chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
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

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yalan Liu *Division of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China.
Yaru Peng *Office of Clinical Trial Institution, Peking University Shenzhen Hospital, Shenzhen, China.
Qiping WuDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China.
Zeneng ChengDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China.
Feifan XieDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China.
Sanwang LiDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410011, China. sanwangli@csu.edu.cn.

Funding

Hunan Provincial Innovation Foundation For Postgraduate CX20240321National Natural Science Foundation of China 82003712Natural Science Foundation of Hunan Province 2022JJ40694
6 · The paper itself

Abstract

High-performance liquid chromatography coupled with inductively coupled plasma-mass spectrometry (HPLC-ICP-MS) is a promising structure-independent technique for accurate quantification of drug-related substances in pharmaceuticals. In this study, we aimed to develop a sensitive HPLC-ICP-MS method, combined with pre-column derivatization, to quantify related substances in acetaminophen tablets. Through this method, we enhanced the sensitivity and accuracy of detecting substances that do not directly respond to ICP-MS. The key advancements include the systematic optimization of derivatization conditions and the establishment of a calibration curve with correlation coefficients greater than 0.999. The method achieved impressive limits of quantification: 0.089 µM for o-acetaminophenol, 0.097 µM for p-nitrophenol, and 0.161 µM for p-aminophenol. It demonstrated suitable accuracy and precision for the following substances: o-acetaminophenol with accuracy between 95.7-104.4% and precision ≤ 4.9%, p-nitrophenol with accuracy between 97.6-101.8% and precision ≤ 2.7%, and p-aminophenol with accuracy between 95.6-102.5% and precision ≤ 3.2%. Comparative analysis with standard HPLC-UV method revealed less than 20% difference in quantification, underscoring the effectiveness of this combined technique. Our study demonstrated the feasibility of combining chemical derivatization with HPLC-ICP-MS as a robust tool for the accurate quantification of drug-related substances, particularly those lacking elements detectable by ICP-MS alone.

Indexed as

AcetaminophenMass SpectrometryAminophenolsChromatography, High Pressure LiquidNitrophenolsTablets4-aminophenol4-nitrophenolAcetaminophenAminophenolsNitrophenolsTabletsAcetaminophen-related substancesChemical derivatizationHPLC–ICP-MSQuantitative analysis

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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.