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Evaluation of time extrapolation factors based on the database RepDose.

M Batke1, S Escher, S Hoffmann-Doerr

  • 1Fraunhofer ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany. monika.batke@item.fraunhofer.de

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Extrapolation factors (EF) are crucial for assessing long-term human exposure risks from short-term animal studies. This study derived time-dependent EFs using the RepDose database, improving chemical risk assessment accuracy.

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Area of Science:

  • Toxicology
  • Risk Assessment
  • Pharmacokinetics

Background:

  • Chemical risk assessment often relies on short-term animal studies for long-term human exposure evaluation.
  • Extrapolation factors (EF) are typically used to adjust No Observed Effect Levels (NOELs) or Lowest Observed Effect Levels (LOELs) from short-term to long-term exposure durations.

Purpose of the Study:

  • To derive time-dependent extrapolation factors (EFs) for chemical risk assessment.
  • To improve the accuracy of extrapolating toxicity data from short-term to long-term exposure scenarios.
  • To analyze and reduce variability in EF datasets based on repeated dose toxicity studies.

Main Methods:

  • Utilized large datasets from the RepDose database for oral and inhalation repeated dose toxicity studies.
  • Derived EFs based on NOEL/C or LOEL/C ratios (short-term N(L)OEL / long-term N(L)OEL).
  • Employed a tiered approach to analyze and exclude sources of variability, such as LOEL/C ratios and dose spacing.

Main Results:

  • Developed "final" EF datasets based on compound-specific, time-dependent toxicity differences.
  • For oral studies, distribution functions showed: subacute-to-subchronic (GM 1.3, GSD 2.4, 90th 4.0), subacute-to-chronic (GM 3.4, GSD 3.7, 90th 18.2), and subchronic-to-chronic (GM 1.4, GSD 2.1, 90th 3.6).
  • Limited inhalation data confirmed the oral EF findings for systemic toxicity.

Conclusions:

  • The derived time-dependent EFs provide a more refined approach to chemical risk assessment.
  • These EFs reduce uncertainty in extrapolating short-term toxicity data to predict long-term human exposure effects.
  • The findings support the use of compound-specific, time-dependent factors for more accurate risk evaluations.