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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Understanding quantitative structure-property relationships uncertainty in environmental fate modeling
M Sarfraz Iqbal1, Laura Golsteijn, Tomas Öberg
1School of Natural Sciences, Linnaeus University, Kalmar, Sweden. muhammad.sarfraziqbal@lnu.se
Environmental Toxicology and Chemistry
|February 26, 2013
Summary
Uncertainty in chemical fate assessments from in silico predictions is key. Developing better quantitative structure-property relationships (QSPRs) for degradation properties can improve the reliability of persistence and transport potential rankings.
Area of Science:
- Environmental Chemistry
- Computational Chemistry
- Risk Assessment
Background:
- Chemical regulations permit alternative testing strategies like in silico predictions when experimental data is scarce.
- Quantitative structure-property relationships (QSPRs) provide estimates but their uncertainty contribution to fate assessments is not well understood.
Purpose of the Study:
- To investigate the uncertainty introduced by QSPR predictions in environmental fate assessments.
- To quantify QSPR-induced uncertainty in overall persistence (POV) and long-range transport potential (LRTP) for polybrominated diphenyl ethers (PBDEs).
Main Methods:
- Utilized the multimedia fate model Simplebox for probabilistic assessments.
- Integrated QSPRs for key fate input parameters including physical-chemical properties and degradation rates.
- Conducted uncertainty and sensitivity analyses on five PBDEs.
Main Results:
- Uncertainty in POV and LRTP was primarily driven by direct photolysis and biodegradation half-life in water.
- QSPRs specifically developed for PBDEs contributed relatively little to the overall uncertainty.
- The reliability of PBDE rankings for POV and LRTP can be enhanced by improving QSPRs for degradation properties.
Conclusions:
- Uncertainty and sensitivity analyses are valuable tools for non-testing strategies in chemical risk assessment.
- Improved QSPRs for degradation parameters are crucial for enhancing the reliability of fate assessments.
- Guidance is needed for handling QSPR predictions when compounds fall outside their applicability domain.
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