ReviewBioanalysis2025
Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.
Review in Bioanalysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypertension is a multifaceted cardiovascular disease, a significant risk factor for stroke, heart attack, heart failure, and renal damage. An essential phase in the drug development process is the exploration of effective bioanalytical approaches to investigate drug metabolism and pharmacokinetics precisely. The use of LC-MS has increased significantly over the last 10 years; numerous researchers have made contributions to the field and enhanced the technical capabilities of workflows based on LC-MS. This review provides a critical analysis of the method development and validation of bioanalytical methods using Liquid Chromatography-Mass Spectrometry (LC-MS) of a few antihypertensive drugs, focusing on extraction techniques and validation parameters. Furthermore, a fleeting look at the GLP, regulatory guidelines, machine learning and artificial intelligence in bioanalysis. Despite these advancements, the document identifies gaps in current regulatory guidelines and advocates areas for further research, predominantly concerning matrix effects and the impact of co-medications. The integration of AI tools in LC-MS has shown the potential to revolutionize bioanalytical methods, yet there is still an imperative for global harmonization. We assume that this review will offer a foundation for the research of new strategies and assist in the identification of the optimum relevant methodology parameters for known and emerging antihypertensive drugs.
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Registered trials
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.