This work investigates the bioaccumulation patterns of 168 organic chemicals in fish, by comparing their bioconcentration factor (BCF), biomagnification factor (BMF) and octanol-water partitioning coefficient (KOW). It aims to gain insights on the relationships between dietary and non-dietary bioaccumulation in aquatic environment, on the effectiveness of KOW and BCF to detect compounds that bioaccumulate through diet, as well as to detect the presence of structure-related bioaccumulation patterns. A linear relationship between logBMF and logKOW was observed (logBMF = 1.14·logBCF – 6.20) up to logKOW ≈ 4, as well as between logBMF and logBCF (logBMF = 0.96·logBCF – 4.06) up to a logBCF ≈ 5. 10% of compounds do not satisfy the linear BCF-BMF relationship. The deviations from such linear relationships were further investigated with the aid of a self-organizing map and canonical correlation analysis, which allowed us to shed light on some structure-related patterns. Finally, the usage of KOW- and BCF-based thresholds to detect compounds that accumulate through diet led to many false positives (47%–91% for KOW), and a moderate number of false negatives (up to 5% for BCF). These results corroborate the need of using the experimental BMF for hazard assessment practices, as well as of developing computational tools for BMF prediction.

Grisoni, F., Consonni, V., Vighi, M. (2018). Detecting the bioaccumulation patterns of chemicals through data-driven approaches. CHEMOSPHERE, 208, 273-284 [10.1016/j.chemosphere.2018.05.157].

Detecting the bioaccumulation patterns of chemicals through data-driven approaches

Grisoni, F
;
Consonni, V;
2018

Abstract

This work investigates the bioaccumulation patterns of 168 organic chemicals in fish, by comparing their bioconcentration factor (BCF), biomagnification factor (BMF) and octanol-water partitioning coefficient (KOW). It aims to gain insights on the relationships between dietary and non-dietary bioaccumulation in aquatic environment, on the effectiveness of KOW and BCF to detect compounds that bioaccumulate through diet, as well as to detect the presence of structure-related bioaccumulation patterns. A linear relationship between logBMF and logKOW was observed (logBMF = 1.14·logBCF – 6.20) up to logKOW ≈ 4, as well as between logBMF and logBCF (logBMF = 0.96·logBCF – 4.06) up to a logBCF ≈ 5. 10% of compounds do not satisfy the linear BCF-BMF relationship. The deviations from such linear relationships were further investigated with the aid of a self-organizing map and canonical correlation analysis, which allowed us to shed light on some structure-related patterns. Finally, the usage of KOW- and BCF-based thresholds to detect compounds that accumulate through diet led to many false positives (47%–91% for KOW), and a moderate number of false negatives (up to 5% for BCF). These results corroborate the need of using the experimental BMF for hazard assessment practices, as well as of developing computational tools for BMF prediction.
Articolo in rivista - Review Essay
Bioaccumulation; Bioconcentration; Canonical correlation analysis; Machine-learning; Self-organizing map;
Bioaccumulation; Bioconcentration; Canonical correlation analysis; Machine-learning; Self-organizing map
English
2018
208
273
284
reserved
Grisoni, F., Consonni, V., Vighi, M. (2018). Detecting the bioaccumulation patterns of chemicals through data-driven approaches. CHEMOSPHERE, 208, 273-284 [10.1016/j.chemosphere.2018.05.157].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/199822
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