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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Area of Science:

  • Toxicology
  • Computational Biology
  • Data Science

Background:

  • Extraction of toxicological endpoints from primary sources is crucial for systematic reviews and risk assessments.
  • Inconsistent language in primary sources complicates data standardization, increasing manual labor.
  • Standardized endpoint descriptions are essential for optimal data utilization.

Purpose of the Study:

  • To develop automated tools for standardizing extracted toxicological information.
  • To minimize labor efforts in standardizing endpoint descriptions using controlled vocabularies.
  • To create a harmonized controlled vocabulary crosswalk for improved data consistency.

Main Methods:

  • Applied an augmented intelligence approach using automated tools.
  • Created a harmonized controlled vocabulary crosswalk with Unified Medical Language System (UMLS) codes, BfR DevTox terms, and OECD endpoint vocabularies.
  • Processed extractions from National Toxicology Program (NTP) and European Chemicals Agency (ECHA) developmental toxicology studies.

Main Results:

  • Automatically standardized 75% of NTP and 57% of ECHA extracted endpoints.
  • 51% of standardized endpoints required manual review for accuracy.
  • An augmented intelligence approach saved significant manual effort and produced valuable resources like a crosswalk and accessible datasets.

Conclusions:

  • Augmenting manual efforts with automation enhances the efficiency of creating FAIR datasets for regulatory studies.
  • The open-source approach is applicable to other developmental toxicology datasets.
  • The customizable code design supports adaptation for various study types.