Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
Sci Total Environ. 2020 Sep 15;735:139243. doi: 10.1016/j.scitotenv.2020.139243. Epub 2020 May 17.
Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.
蜜蜂（Apis mellifera）作为授粉媒介，在农业、食物链和生态群落之间架起桥梁，提供了关键的生态系统服务，从而确保了粮食生产和安全。单一植保产品（PPPs）的生态风险评估需要了解暴露和毒性。基于结构的定量构效关系（QSAR）模型等计算工具在预测化学品的结构、物理化学和药代动力学性质以及单一和多种化学品的毒性方面可以发挥重要作用。在这里，使用 EFSA 的 OpenFoodTox、美国环保署的 ECOTOX 和农药特性数据库，开发了第一个用于 PPPs 的综合蜜蜂 QSAR 模型，i）预测急性接触毒性（LD），ii）描述农药活性物质的作用模式（MoA）。开发并测试了三种不同的基于分类和四种基于回归的模型，以确定基于 k-NN 算法提供最可靠预测的两个模型。基于两类别 QSAR 模型（有毒/无毒；n=411）的验证使用敏感性（=0.93）、特异性（=0.85）、平衡准确性（=0.90）和马修斯相关系数（MCC=0.78）作为统计参数。基于回归的模型（n=113）的验证可靠性和稳健性（R=0.74；MAE=0.52）。本研究提出了 113 种农药活性物质的 MoA 分析和蜜蜂急性接触毒性的第一个协调作用模式分类方案，包括来自三个不同数据库的 LD 数据点。该分类允许进一步定义 PPPs 活性物质的作用模式和靶标部位，从而使监管机构和科学家能够细化单一化学品的化学分组和毒性外推，以及基于成分的多种化学品混合物风险评估。简要讨论了相关的未来展望，以整合作用模式、不良结局途径（AOPs）和毒代动力学信息，以细化蜜蜂健康背景下不同水平生物组织的单一化学品/组合毒性预测和风险估计。
