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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification

Iker Alvarez-Mora1,2, Katarzyna Arturi3, Frederic Béen4,5

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High-throughput effect-directed analysis (HT-EDA) accelerates the identification of toxic chemicals in environmental samples. This review explores novel methods and computational tools to enhance HT-EDA for broader monitoring applications.

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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Analytical Chemistry

Background:

  • Increasing chemical production necessitates robust methods for assessing environmental and human health impacts.
  • High-resolution mass spectrometry (HRMS) detects numerous compounds but identifying toxicity drivers in complex mixtures remains challenging.
  • Effect-directed analysis (EDA) combines bioassays, fractionation, and chemical analysis to identify toxicity drivers.

Purpose of the Study:

  • To provide an updated review of high-throughput effect-directed analysis (HT-EDA) methodologies.
  • To discuss novel methods, tools, and computational approaches for accelerating EDA workflows.
  • To identify current limitations in HT-EDA and propose solutions for improved environmental monitoring.

Main Methods:

  • Review of existing literature on HT-EDA, including microfractionation, downscaled bioassays, and automation.
  • Discussion of high-performance thin-layer chromatography (HPTLC) as an alternative to HPLC in HT-EDA.
  • Exploration of computational prioritization tools and data processing workflows for HT-EDA.

Main Results:

  • HT-EDA significantly accelerates the identification of toxicity drivers in complex environmental mixtures.
  • Integration of automation, microplate-based fractionation, and advanced computational tools enhances HT-EDA efficiency.
  • HPTLC presents a viable alternative for fractionation in HT-EDA, complementing microplate methods.

Conclusions:

  • HT-EDA is crucial for overcoming the limitations of traditional EDA, enabling large-scale environmental monitoring.
  • Further development of computational tools and novel methods like HPTLC integration will advance HT-EDA capabilities.
  • This review highlights strategies to bridge the gap between current HT-EDA and its application in routine monitoring.