Abstract

Background:

Research suggests environmental contaminants can impact metabolic health; however, high costs prohibit in vivo screening of putative metabolic disruptors. High-throughput screening programs, such as ToxCast, hold promise to reduce testing gaps and prioritize higher-order (in vivo) testing.

Objectives:

We sought to a) examine the concordance of in vitro testing in 3T3-L1 cells to a targeted literature review for 38 semivolatile environmental chemicals, and b) assess the predictive utility of various expert models using ToxCast data against the set of 38 reference chemicals.

Methods:

Using a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models: a) the 8-Slice model published by Janesick et al. (2016) and b) the 5-Slice model published by Auerbach et al. (2016). We examined the performance of the two models against the Janesick in vitro results and our own 38-chemical reference set. We further evaluated the predictive performance of various modifications to these models using cytotoxicity filtering approaches and validated our best-performing model with new chemical testing in 3T3-L1 cells.

Results:

The literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous in vitro results to the literature-derived calls. ToxPi models provided balanced accuracies ranging from 0.55 to 0.88, depending on the model specifications and reference set. Validation chemical testing correctly predicted 29 of 30 chemicals as per 3T3-L1 testing, suggesting good adipogenic prediction performance for our best adapted model.

Discussion:

Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779

中文翻译：

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基于 ToxCast 的预测模型在评估环境化学品潜在代谢干扰方面的实用性

摘要

背景：

研究表明环境污染物会影响代谢健康；然而，高昂的成本阻碍了对假定的代谢干扰物的体内筛选。高通量筛选程序（例如 ToxCast）有望减少测试差距并优先考虑高阶（体内）测试。

目标：

我们试图a ) 检查 38 种半挥发性环境化学物质的 3T3-L1 细胞体外测试与有针对性的文献综述的一致性，以及b ) 使用 ToxCast 数据针对 38 种参考化学物质集评估各种专家模型的预测效用。

方法：

我们使用一组 38 种化学物质以及之前在 3T3-L1 细胞中发表的结果，进行了代谢靶向文献综述，以确定一致的活性测定。为了评估 ToxCast 预测效用，我们使用了两个已发布的 ToxPi 模型：a ) Janesick 等人发布的 8-Slice 模型。(2016) 和b ) Auerbach 等人发表的 5-Slice 模型。（2016）。我们根据 Janesick体外结果和我们自己的 38 种化学参考集检查了两个模型的性能。我们使用细胞毒性过滤方法进一步评估了对这些模型进行各种修改的预测性能，并通过 3T3-L1 细胞中的新化学测试验证了我们表现最佳的模型。

结果：

文献综述揭示了 38 种化学品中 30 种的相关出版物（其余 8 种化学品仅在我们之前的 3T3-L1 测试中进行了检查）。将我们之前的体外结果与文献得出的结果进行比较，我们观察到平衡准确度（敏感性和特异性的平均值）为 0.86 。ToxPi 模型提供的平衡精度范围为 0.55 至 0.88，具体取决于模型规格和参考集。根据 3T3-L1 测试，验证化学测试正确预测了 30 种化学物质中的 29 种，这表明我们的最佳适应模型具有良好的脂肪形成预测性能。

讨论：

使用最新的 ToxCast 数据和更新的 ToxPi 模型，我们发现 ToxCast 在预测共识调用方面的表现与我们自己的 3T3-L1 测试类似。此外，我们还提供了大部分未经测试的化学物质的完整排名列表，其 ToxPi 评分可预测脂肪形成活性并需要进一步研究。https://doi.org/10.1289/EHP6779