Pandey Shubham Kumar, Roy Kunal
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Aquat Toxicol. 2025 Sep;286:107441. doi: 10.1016/j.aquatox.2025.107441. Epub 2025 Jun 4.
The persistence, bioaccumulation and toxicity (PBT) potential of organic chemicals is used to assess the hazards associated with them. Predicting the biomagnification potential of chemicals in fish reflects their persistence or accumulation in the organisms of the upper trophic levels. The quantitative read-across structure-property relationship (q-RASPR) methodology has evolved in recent years and showcased its effective predictive potential through combining the advantages of both quantitative structure-property relationship (QSPR) and read-across (RA). In this study, we have employed the q-RASPR approach to develop a predictive model for the estimation of the lipid-normalized dietary biomagnification factor (i.e. BMF). We utilized the BMF data experimentally collected as per the Organization of Economic Co-operation and Development (OECD) guideline 305 for model development. The final univariate q-RASPR model was statistically fit, robust, showing a superior predictive performance [Q = 0.90, Q = 0.89, MAE = 0.27, RMSE = 0.37, CCC=0.94] compared to the previously developed models. The developed model is capable of predicting the BMF of organic chemicals in fish. Therefore, this model can serve as an effective tool to enlighten the path for the assessment of environmental risk associated with the chemicals.
有机化学品的持久性、生物累积性和毒性（PBT）潜力用于评估与其相关的危害。预测化学品在鱼类中的生物放大潜力反映了它们在较高营养级生物体内的持久性或累积性。近年来，定量跨域结构-性质关系（q-RASPR）方法不断发展，并通过结合定量结构-性质关系（QSPR）和跨域（RA）的优势展示了其有效的预测潜力。在本研究中，我们采用q-RASPR方法开发了一个预测模型，用于估算脂质标准化膳食生物放大因子（即BMF）。我们利用根据经济合作与发展组织（OECD）指南305实验收集的BMF数据进行模型开发。最终的单变量q-RASPR模型在统计上拟合良好、稳健，与先前开发的模型相比，显示出卓越的预测性能[Q = 0.90，Q = 0.89，平均绝对误差（MAE）= 0.27，均方根误差（RMSE）= 0.37，一致性相关系数（CCC）= 0.94]。所开发的模型能够预测鱼类中有机化学品的BMF。因此，该模型可作为一种有效工具，为评估与化学品相关的环境风险指明道路。
