Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides’ pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC50%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs.

随着人口增长，社会对营养物质与新技术的需求持续攀升。基于金属氧化物工程纳米颗粒（metal oxide engineered nanoparticles, MeOx ENPs）的农业技术革命，有望在保障粮食安全的前提下，重塑韧性农业体系。但若大规模应用这类纳米颗粒，可能会对靶标与非靶标物种产生意料之外的毒理学效应。当前关于纳米农药的有害效应的相关公开信息较为匮乏，因此可通过计算机仿真（in silico）建模来探究相关问题。为此，本研究采用计算建模方法，探究金属氧化物纳米颗粒（metal oxide nanoparticles, MeOx NPs）对支气管上皮（BEAS-2B）细胞与鼠源髓系（RAW 264.7）细胞毒性的影响，以弥补金属氧化物纳米颗粒毒性相关的数据缺口。研究初期，研究团队借助小型数据集建模软件（Small Dataset Modeler software，http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/），以半数有效浓度（effective concentration, EC50%）作为终点指标，仅采用周期表描述符，基于4个数据集构建了偏最小二乘（partial least squares, PLS）回归模型。为进一步探究模型预测结果，本研究采用了在定量构效关系（Quantitative Structure-Activity Relationship, QSAR）模型中筛选得到的描述符，开展了交叉参照（read-across）分析。此外，本研究还完成了终点间细胞毒性关系建模，即定量毒性-毒性关系（quantitative toxicity-toxicity relationship, QTTR）。研究发现，纳米交叉参照法所得结果的精度与QSAR模型相当。对两种细胞系的PLS模型分析结果表明，金属阳离子形成能力与化学键形成能力会对细胞毒性产生影响，而金属元素本身则对生态毒理效应具有显著作用。综上，本研究可为设计出比传统同类产品更高效的安全纳米农药提供可行路径。