Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India; QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.
Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India.
Environ Int. 2022 Dec;170:107625. doi: 10.1016/j.envint.2022.107625. Epub 2022 Nov 11.
Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research aims to evaluate the ecotoxicity potential of 122 pharmaceuticals and identify possible important structural attributes using BCF as the determining feature against a group of fish species. We have calculated the theoretical 2D descriptors from the OCHEM platform and SiRMS descriptor calculating software. The regression-based quantitative structure-property relationship (QSPR) modeling was used to identify the chemical features responsible for acute fish bioconcentration. Multiple models with the "intelligent consensus" algorithm were employed for the regression-based approach improving the predictive ability of the models. To ensure the robustness and interpretability of the developed models, rigorous validation was performed employing various statistical internal and external validation metrics. From the developed models, it can be specified that the presence of large lipophilic and electronegative moieties greatly enhances the bioaccumulative potential of pharmaceuticals, whereas the hydrophilic characteristics have shown a negative impact on BCF. Furthermore, the developed models were employed to screen the DrugBank database (https://go.drugbank.com/) for assessing the BCF properties of the entire database. The evidence acquired from the modeled descriptors might be used for aquatic risk assessment in the future, with the added benefit of providing an early caution of their probable negative impact on aquatic ecosystems for regulatory purposes.
生物浓缩因子（BCFs）是化学物质在生物体中积累的标志物，它们在确定各种化学物质的环境风险方面起着重要作用。获得 BCF 的实验既昂贵又耗时；因此，最好在化学开发过程的早期就对其进行估算。本研究旨在评估 122 种药物的生态毒性潜力，并使用 BCF 作为针对一组鱼类的决定特征来识别可能的重要结构属性。我们已经从 OCHEM 平台和 SiRMS 描述符计算软件计算了理论 2D 描述符。基于回归的定量构效关系（QSPR）建模用于识别导致鱼类急性生物浓缩的化学特征。采用基于回归的方法的“智能共识”算法的多个模型用于提高模型的预测能力。为了确保开发模型的稳健性和可解释性，采用了各种统计内部和外部验证指标对其进行了严格验证。从开发的模型中可以看出，大的亲脂性和带负电的部分的存在极大地增强了药物的生物累积潜力，而亲水性特征对 BCF 则有负面影响。此外，还利用所开发的模型对 DrugBank 数据库（https://go.drugbank.com/）进行了筛选，以评估整个数据库的 BCF 属性。从建模描述符中获得的证据可用于未来的水生风险评估，并且可以为其对水生生态系统可能产生的负面影响提供早期警告，以达到监管目的。
