Jaworska Joanna, Dancik Yuri, Kern Petra, Gerberick Frank, Natsch Andreas
Procter & Gamble NV, 100 Temselaan, 1853, Strombeek - Bever, Belgium.
J Appl Toxicol. 2013 Nov;33(11):1353-64. doi: 10.1002/jat.2869. Epub 2013 May 14.
Frameworks to predict in vivo effects by integration of in vitro, in silico and in chemico information using mechanistic insight are needed to meet the challenges of 21(st) century toxicology. Expert-based approaches that qualitatively integrate multifaceted data are practiced under the term 'weight of evidence', whereas quantitative approaches remain rare. To address this gap we previously developed a methodology to design an Integrated Testing Strategy (ITS) in the form of a Bayesian Network (BN). This study follows up on our proof of concept work and presents an updated ITS to assess skin sensitization potency expressed as local lymph node assay (LLNA) potency classes. Modifications to the ITS structure were introduced to include better mechanistic information. The parameters of the updated ITS were calculated from an extended data set of 124 chemicals. A detailed validation analysis and a case study were carried out to demonstrate the utility of the ITS for practical application. The improved BN ITS predicted correctly 95% and 86% of chemicals in a test set (n = 21) for hazard and LLNA potency classes, respectively. The practical value of using the BN ITS is far more than a prediction framework when all data are available. The BN ITS can develop a hypothesis using subsets of data as small as one data point and can be queried on the value of adding additional tests before testing is commenced. The ITS represents key steps of the skin sensitization process and a mechanistically interpretable testing strategy can be developed. These features are illustrated in the manuscript via practical examples.
为应对21世纪毒理学的挑战，需要利用机理洞察整合体外、计算机模拟和化学信息来预测体内效应的框架。基于专家的方法定性地整合多方面数据，这种方法被称为“证据权重”，而定量方法仍然很少见。为了弥补这一差距，我们之前开发了一种以贝叶斯网络（BN）形式设计综合测试策略（ITS）的方法。本研究是在我们的概念验证工作基础上进行的，提出了一种更新的ITS，以评估以局部淋巴结试验（LLNA）效力类别表示的皮肤致敏潜力。对ITS结构进行了修改，以纳入更好的机理信息。更新后的ITS参数是根据124种化学品的扩展数据集计算得出的。进行了详细的验证分析和案例研究，以证明ITS在实际应用中的效用。改进后的BN ITS在一个测试集（n = 21）中分别正确预测了95%和86%的化学品的危害和LLNA效力类别。当所有数据都可用时，使用BN ITS的实际价值远不止是一个预测框架。BN ITS可以使用小至一个数据点的数据集来提出假设，并且可以在测试开始前就添加额外测试的价值进行查询。ITS代表了皮肤致敏过程的关键步骤，并且可以开发出一种具有机理可解释性的测试策略。这些特点在本文中通过实际例子进行了说明。
