Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?
Bioanalytical methods
HT-EDA
Mass spectrometry
NTS
Journal
Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327
Informations de publication
Date de publication:
12 Jul 2024
12 Jul 2024
Historique:
received:
30
04
2024
accepted:
24
06
2024
revised:
21
06
2024
medline:
12
7
2024
pubmed:
12
7
2024
entrez:
11
7
2024
Statut:
aheadofprint
Résumé
The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.
Identifiants
pubmed: 38992177
doi: 10.1007/s00216-024-05424-4
pii: 10.1007/s00216-024-05424-4
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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