Quantitative read-across structure-property relationship (q-RASPR): a novel approach to estimate the bioaccumulative potential for diverse classes of industrial chemicals in aquatic organisms.

Abstract

The Bioconcentration Factor (BCF) is used to evaluate the bioaccumulation potential of chemical substances in reference organisms, and it directly correlates with ecotoxicity. Traditional in vivo BCF estimation methods are costly, time-consuming, and involve animal sacrifice. Many in silico technologies are used to avoid the problems associated with in vivo testing. This study aims to develop a quantitative read across structure-property relationship (q-RASPR) model using a structurally diverse dataset consisting of 1303 compounds by combining quantitative structure-property relationship (QSPR) and read-across (RA) algorithms. The model incorporates simple, interpretable, and reproducible 2D molecular descriptors along with RASAR descriptors. The PLS-based q-RASPR model demonstrated robust performance with internal validation metrics (R² = 0.727 and Q²_(LOO) = 0.723) and external validation metrics (Q²_F1 = 0.739, Q²_F2 = 0.739, and CCC = 0.858). These results indicate that the q-RASPR model is statistically superior to the corresponding QSPR model. Furthermore, screening of 1694 compounds from the Pesticide Properties Database (PPDB) was performed using the PLS-based q-RASPR model for assessing the eco-toxicological bioaccumulative potential of various compounds, ensuring the external predictability of the developed model and confirming the real-world application of the developed model. This model offers a reliable tool for predicting the BCF of new or untested compounds, thereby helping to develop safe and environment-friendly chemicals.