Related Experiment Videos
Can poly-parameter linear-free energy relationships (pp-LFERs) improve modelling bioaccumulation in fish?
Shizhen Zhao1, Kevin C Jones2, Andrew J Sweetman2
1Lancaster Environment Centre, Lancaster University, Lancaster, LA14YQ, UK; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China.
Chemosphere
|October 17, 2017
Summary
Poly-parameter linear free energy relationships (pp-LFERs) improve chemical partitioning predictions in fish models. Incorporating pp-LFERs into physiologically based toxicokinetic (PBTK) models enhances risk assessment accuracy for aquatic organisms.
Area of Science:
- Environmental Chemistry
- Toxicology
- Computational Modeling
Background:
- Biosorbent interactions with chemicals are key to understanding chemical partitioning.
- Poly-parameter linear free energy relationships (pp-LFERs) estimate chemical partitioning into biological phases.
- Fish models are crucial for assessing chemical risks in aquatic food chains and human dietary exposure.
Purpose of the Study:
- To evaluate the impact of implementing pp-LFERs in one-compartment and multi-compartment physiologically based toxicokinetic (PBTK) fish models.
- To assess the implications of using pp-LFERs for chemical risk assessment.
- To compare the predictive performance of pp-LFERs against single-parameter (sp) LFERs using the bioconcentration factor (BCF).
Main Methods:
- Development and application of one-compartment and multi-compartment fish models.
- Implementation of poly-parameter linear free energy relationships (pp-LFERs) and single-parameter (sp) LFERs.
- Utilizing the bioconcentration factor (BCF) as the primary evaluation metric.
- Analysis of model performance based on R-squared values and prediction accuracy (factor of 10).
Main Results:
- Models incorporating pp-LFERs showed a slight improvement over sp-LFERs in the one-compartmental fish model (R²=0.75 vs R²=0.72).
- Significant enhancement in prediction accuracy was observed for compounds with log KOW between 4 and 5 (R² increased from 0.52 to 0.71).
- Multi-compartmental PBTK models with pp-LFERs and metabolism consideration achieved predictions within a factor of 10 of measured data.
Conclusions:
- pp-LFERs offer enhanced accuracy for chemical partitioning predictions in fish models compared to sp-LFERs.
- The KOW-based (sp-LFERs) approach is adequate for initial screening of partitioning characteristics.
- Further research is needed to incorporate ionization and improve biotransformation quantification in biota models for comprehensive risk assessment.