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  1. Home
  3. A Systematic Analysis Of Read-across Within Reach Registration Dossiers.
  1. Home
  3. A Systematic Analysis Of Read-across Within Reach Registration Dossiers.

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A systematic analysis of read-across within REACH registration dossiers.

G Patlewicz1, P Karamertzanis2, K Paul Friedman1

  • 1Center for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, 109 TW Alexander Dr, Research Triangle Park, USA, NC 27711.

Computational Toxicology (Amsterdam, Netherlands)
|July 12, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study compares expert-driven read-across with data-driven methods like Generalised Read-Across (GenRA) for filling data gaps in chemical safety assessments. It quantifies similarities and predicts toxicity values, offering insights for regulatory submissions.

Keywords:
GenRANew Approach Methods (NAMs)REACHread-acrosssimilarity context

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Area of Science:

  • Chemical toxicology
  • Computational toxicology
  • Regulatory science

Background:

  • Read-across is a key data-gap filling technique in chemical safety, but its acceptance is hindered by uncertainty and expert dependency.
  • Data-driven approaches, such as Generalised Read-Across (GenRA), offer systematic quantification of uncertainties and performance, facilitating the integration of New Approach Method (NAM) data.
  • Establishing scientific confidence in read-across predictions is crucial for regulatory acceptance.

Purpose of the Study:

  • To systematically investigate the differences between expert-driven and data-driven read-across approaches.
  • To assess the scientific confidence in using read-across for regulatory submissions.
  • To evaluate the utility of GenRA in incorporating NAM data and quantifying uncertainties in read-across.

Main Methods:

  • Compiled a dataset of expert-driven read-across assessments from REACH registration data (IUCLID).
  • Mapped ~5000 read-across cases to EPA's DSSTox database, resulting in 389 unique target-source analogue pairs with curated SMILES.
  • Evaluated target-source analogue similarity using structural and metabolic contexts, derived a model to predict analogue pairs, and predicted Point of Departure (POD) values using GenRA.

Main Results:

  • Compared structural and metabolic similarity contexts to quantify their contribution to analogue pair prediction.
  • GenRA was used to predict POD values, which were then compared to those reported in REACH dossiers.
  • The study identified generalizable insights into current read-across applications for regulatory submissions.

Conclusions:

  • Data-driven read-across, particularly GenRA, provides a framework for quantifying uncertainties and systematically incorporating NAM data.
  • Understanding similarity contexts is key to building scientific confidence in read-across predictions.
  • This research offers valuable insights into the practical application and acceptance criteria for read-across in regulatory decision-making.