生物富集因子(BCFs)是生物体内化学物质积累的标志,它们在确定各种化学品的环境风险方面发挥着重要作用。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估计BCF。当前的研究旨在评估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确定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。基于回归的定量结构-性质关系(QSPR)模型用于识别导致急性鱼类生物浓缩的化学特征。采用“智能共识”算法的多个模型用于基于回归的方法,提高了模型的预测能力。为了确保所开发模型的鲁棒性和可解释性,采用各种统计内部和外部验证指标进行严格验证。从开发的模型来看,可以指出,大的亲脂性和电负性部分的存在极大地增强了药物的生物累积潜力,而亲水特性对BCF有负面影响。此外,开发的模型用于筛选DrugBank数据库(https://go。drugbank.com/)用于评估整个数据库的BCF属性。从建模描述符中获得的证据可能会在未来用于水生风险评估,额外的好处是,为了监管的目的,及早警告它们可能对水生生态系统产生的负面影响。
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.