Evidence map›Paper›PMID 40629991›Full record

ReviewBioanalysis2025

Liquid chromatography-tandem mass spectrometry methods for clinical quantitation of antibiotics.

Xingrui He, Wenqian Zhang, Feifan Xie

Abstract readReview
In one paragraph

Review in Bioanalysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xingrui HeDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
Wenqian ZhangDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
Feifan XieDivision of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate quantification of antibiotics in biological matrices is pivotal for supporting individualized medication, optimizing antibiotics therapy and addressing the global crisis of antimicrobial resistance (AMR). LC-MS/MS has emerged as the gold standard for clinical antibiotic quantification, particularly valued for its accuracy, multiplexing efficiency, and compatibility with complex sample types. This review provides a comprehensive overview of the principles, methodologies, and clinical applications of LC-MS/MS in antibiotic quantification, with a focus on its role in therapeutic drug monitoring (TDM), pharmacokinetics (PK), and epidemiological studies. Key challenges, including sample preparation and matrix effects, are critically discussed. Tailored strategies for analyzing diverse antibiotic classes (e.g. β-lactams, aminoglycosides, colistins, macrolides, and glycopeptides) are highlighted by considering their physicochemical properties, including polarity, acid-base characteristics, and structural complexity. Furthermore, emerging trends in automation, high-resolution mass spectrometry, and quality control frameworks are outlined to guide future advancements. By bridging technological innovation with clinical needs, LC-MS/MS continues to be a cornerstone of precision medicine and a vital tool in global AMR mitigation efforts.

Indexed as

Anti-Bacterial AgentsTandem Mass SpectrometryChromatography, LiquidDrug MonitoringHumansAnti-Bacterial Agentsantibioticsantimicrobial resistanceLC-MS/MSpharmacokineticstherapeutic drug monitoring

Identifiers

PMID40629991
PMCPMC12367091

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.