Classification-based QSARs for predicting dietary biomagnification in fish

ABSTRACT

The assessment of bioaccumulation is an important step to describe the environmental behaviour and the potential risk due to exposure to potentially hazardous chemicals. In the last two decades, several in silico tools have been made available to predict bioconcentration, which is commonly used to assess bioaccumulation in risk assessment frameworks all over the world. However, only a few QSAR studies address the prediction of the biomagnification factor (BMF), which describes the accumulation of chemicals into organisms due to exposure through the diet. No classification models are currently available to this end. In this work, we developed classification QSARs to predict classes based on dietary biomagnification, using three different classifiers (i.e. LDA, ANN and RF). We started from a recently published dataset that includes more than 300 curated dietary BMF values measured in fish. The new models have high-quality performances (accuracy in fitting: from 94 to 96%; accuracy in prediction from 84 to 86%). The good performances of the here proposed QSARs confirm the quality of the original input data and highlight the importance of data curation and data sharing to support the development of new in silico approaches to assist risk assessment and chemicals screening.

KEYWORDS:

• QSAR
• classification
• biomagnification
• bioaccumulation
• linear discriminant analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2066174.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2066174

Additional information

Funding

Ministero dell’Università e della Ricerca MIUR: PhD fellowship (2019-2022) to Ms. Linda Bertato (PhD Course in Chemical and Environmental Sciences, DISCA - University of Insubria). University of Insubria Post Doc grant (2021-2022): In silico solutions for the assessment of biotransformation related endpoints of organic chemicals in multiple organisms (Dr. Nicola Chirico).