Université de Lyon, Université Lyon 1, CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, 69100, Villeurbanne, France.
Sci Data. 2022 Mar 30;9(1):130. doi: 10.1038/s41597-022-01248-y.
Regulatory bodies require bioaccumulation evaluation of chemicals within organisms to better assess toxic risks. Toxicokinetic (TK) data are particularly useful in relating the chemical exposure to the accumulation and depuration processes happening within organisms. TK models are used to predict internal concentrations when experimental data are lacking or difficult to access, such as within target tissues. The bioaccumulative property of chemicals is quantified by metrics calculated from TK model parameters after fitting to data collected via bioaccumulation tests. In bioaccumulation tests, internal concentrations of chemicals are measured within organisms at regular time points during accumulation and depuration phases. The time course is captured by TK model parameters thus providing bioaccumulation metrics. But raw TK data remain difficult to access, most often provided within papers as plots. To increase availability of TK data, we developed an innovative database from data extracted in the scientific literature to support TK modelling. Freely available, our database can dynamically evolve thanks to any researcher interested in sharing data to be findable, accessible, interoperable and reusable.
监管机构要求对生物体中的化学物质进行生物积累评估，以更好地评估毒性风险。毒代动力学 (TK) 数据在将化学暴露与生物体内部的积累和消除过程联系起来方面特别有用。当缺乏或难以获取实验数据时，例如在靶组织内，TK 模型可用于预测内部浓度。通过将从生物积累测试中收集的数据拟合到 TK 模型参数后计算得出的指标来量化化学物质的生物累积特性。在生物积累测试中，在积累和消除阶段的定期时间点测量化学物质在生物体内部的浓度。时间过程由 TK 模型参数捕获，从而提供生物累积指标。但是，原始 TK 数据仍然难以获取，通常以论文中的图表形式提供。为了增加 TK 数据的可用性，我们从科学文献中提取数据开发了一个创新型数据库，以支持 TK 建模。我们的数据库是免费提供的，由于任何有兴趣共享数据的研究人员都可以使其具有可发现性、可访问性、互操作性和可重用性，因此它可以动态发展。
