基于有害结局路径的化学物质计算毒理学研究
Computational Toxicity Prediction of Chemicals by Adverse Outcome Pathway (AOP)
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摘要: 计算毒理学利用分子致毒机制信息和数学模型预测化学物质对人体健康和环境的危害。有害结局路径(adverse outcome pathway,AOP)可将化学物质在个体水平的危害或有害结局(adverse outcome,AO)与其在分子水平上的启动事件(molecular initiating event,MIE)建立关联，为将表征分子致毒机制的体外生物测试数据应用到高通量的化学物质毒性预测中提供了可能。然而，目前缺乏基于有害结局路径的高通量预测化学物质毒性的研究。本研究基于AOP框架，联合ToxCast体外测试数据，选取101种典型环境化学物质进行毒性预测，并通过与PubChem内已报道的化学物质毒性比较，对预测结果进行评价。结果表明，基于AOP预测到101种化学物质共可潜在诱导58个AOs，覆盖了生殖毒性等在内的11个毒性类型。不同毒性类型的真阳性预测率(true positive rate,TPR)不同，其中致癌/遗传毒性、生殖毒性与消化系统毒性的TPR均超过了70%，而神经毒性与呼吸系统毒性的TPR均低于30%。不同毒性类型的TPR与AOP知识库中该毒性类型的AOP (P<0.02,r=0.685)、MIE (P<0.01,r=0.734)、体外生物测试的数量(P<0.01,r=0.752)和化学物质体外测试数量(P<0.01,r=0.293)呈显著正相关。综上，本研究的结果表明，增加高通量体外测试数据和丰富AOP知识库，将进一步提高对化学物质的潜在毒性预测的准确性，为未来化学物质的高通量筛查和风险评估提供支撑。Abstract: The aim of computational toxicology is to predict adverse effects of chemicals on human health and ecological species based on mechanistic information via mathematical models. The development of adverse outcome pathway (AOP) framework allows toxicity prediction of chemical using molecular mechanism data, where the molecular initiating event (MIE) can be connected to the adverse outcome (AO) at individual level. However, there lacks systematic evaluation on high-throughput prediction of chemical toxicity based on AOP. Here, we developed a method of toxicity prediction by integrating in vitro bioassays data in ToxCast and AOPs deposited in the AOP knowledge base (AOP-KB). A wide spectrum of toxicity endpoints of 101 chemical were predicted and the prediction results were validated by comparing with the toxicity reported in PubChem. The results demonstrated that the AOP framework can effectively predict 11 types of toxicity (e.g. reproductive toxicity) for 101 chemicals. The true positive prediction rates (TPRs) were different for different toxicity types, among which the TPRs of carcinogenic/genetic toxicity, reproductive toxicity and digestive toxicity were all more than 70%, while the TPRs of neurotoxicity and respiratory toxicity were less than 30%.The TPR with different toxicity types was positively correlated with the counts of AOPs (P<0.02, r=0.685), MIEs (P<0.01, r=0.734), in vitro bioassays (positive) (P<0.01, r=0.752) in the AOP knowledge database, and in vitro bioassays (positive) for the 101 chemicals (P<0.01, r=0.293). Overall, this study demonstrated that the efficiency of chemical toxicity prediction can be significantly improved by enlargement of the in vitro bioassays data and enrichment of AOP knowledgebase, which is valuable for future risk assessment and management of chemicals.
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Key words:
- chemical /
- high-throughput /
- predictive toxicology /
- adverse outcome pathway /
- in vitro bioassay
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