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bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling.

David J Degnan1, Lisa M Bramer1, Lisa Truong2

  • 1Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, United States of America.

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|July 28, 2025
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Summary
This summary is machine-generated.

A new Python library, benchmark dose response curve (bmdrc), quantifies chemical exposure risks using proportional data from toxicology assays. It follows EPA guidelines for accurate risk assessment of malformations and toxicity.

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Area of Science:

  • Toxicology and Environmental Health
  • Computational Biology
  • Data Science in Chemical Risk Assessment

Background:

  • Chemical exposures pose health risks, including chronic diseases like cancer, but quantifying these risks remains challenging.
  • Assessing chemical risk often involves measuring abnormal responses in organisms at increasing exposure concentrations.
  • Analyzing proportional data from these assays requires specialized methods to estimate toxicity levels accurately.

Purpose of the Study:

  • To develop a standalone Python library for processing proportional toxicology data.
  • To implement the Environmental Protection Agency (EPA) recommended benchmark dose (BMD) estimation methods.
  • To provide a tool that handles both morphological and behavioral proportional data with EPA-compliant filters and models.

Main Methods:

  • Development of the benchmark dose response curve (bmdrc) Python library.
  • Incorporation of EPA-recommended filters, models, and fitting steps for proportional data analysis.
  • Inclusion of visualizations for filters and fitted curves, along with reproducibility reports.

Main Results:

  • The bmdrc Python library offers a comprehensive solution for benchmark dose estimation from proportional toxicology data.
  • The library adheres to EPA guidelines, ensuring accurate and reproducible risk assessment.
  • bmdrc has been integrated as a support package for an existing chemical information web portal.

Conclusions:

  • The bmdrc Python library effectively addresses the need for a specialized tool in chemical risk assessment.
  • It supports toxicology analyses involving proportional responses to chemical concentrations.
  • This open-source tool enhances the reproducibility and accuracy of toxicological studies.