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Benchmark dose (BMD) modeling: current practice, issues, and challenges.

Lynne T Haber1, Michael L Dourson1, Bruce C Allen2

  • 1a Risk Science Center , University of Cincinnati , Cincinnati , OH , USA.

Critical Reviews in Toxicology
|March 9, 2018
PubMed
Summary
This summary is machine-generated.

Benchmark dose (BMD) modeling is the leading method for risk assessment, offering improved data utilization and uncertainty handling. Harmonizing approaches, particularly regarding benchmark response (BMR) and uncertainty factors (UF), is crucial for advancing risk assessment science.

Keywords:
BMDBMDSBenchmark doseNOAELPROASTmodel averagingmodel choicemodel restrictionmodeling issuesrisk assessment

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Area of Science:

  • Toxicology and Risk Assessment
  • Biostatistics and Computational Biology

Background:

  • Benchmark dose (BMD) modeling is the current standard for establishing points of departure in risk assessment.
  • Key benefits include comprehensive data utilization, enhanced consistency, and improved statistical uncertainty accounting.
  • Discrepancies persist between regulatory bodies like the US Environmental Protection Agency (US EPA) and the European Food Safety Authority (EFSA) regarding specific modeling aspects.

Purpose of the Study:

  • To highlight ongoing disagreements in benchmark dose (BMD) modeling methodologies.
  • To identify areas requiring further research for harmonizing risk assessment practices.
  • To emphasize the importance of model confidence over selecting the lowest benchmark dose lower confidence limit (BMDL).

Main Methods:

  • Discussion of differences in benchmark response (BMR) selection for continuous data.
  • Analysis of the implications of using unrestricted models and various mathematical models.
  • Exploration of model averaging as a potential solution to single-model selection challenges.

Main Results:

  • Divergences in regulatory recommendations (US EPA vs. EFSA) lead to variations in benchmark dose lower confidence limit (BMDL) values.
  • Current practices may not adequately account for model confidence or biological relevance in all cases.
  • Model averaging shows promise but requires integration of biological considerations.

Conclusions:

  • Further research is needed to refine benchmark dose (BMD) modeling, particularly concerning benchmark response (BMR) and uncertainty factor (UF) interplay.
  • Incorporating biological data into model averaging weights is essential for robust risk assessment.
  • Addressing these methodological differences will facilitate harmonization and advance the field of risk assessment.