Department of Chemistry, Faculty of Science , University of Calgary , Calgary , Alberta T2N 1N4 , Canada.
College of Pharmacy, Rady Faculty of Health Sciences , University of Manitoba , Winnipeg , Manitoba R3E 0T5 , Canada.
ACS Nano. 2019 Feb 26;13(2):1583-1594. doi: 10.1021/acsnano.8b07562. Epub 2019 Jan 31.
Developing predictive modeling frameworks of potential cytotoxicity of engineered nanoparticles is critical for environmental and health risk analysis. The complexity and the heterogeneity of available data on potential risks of nanoparticles, in addition to interdependency of relevant influential attributes, makes it challenging to develop a generalization of nanoparticle toxicity behavior. Lack of systematic approaches to investigate these risks further adds uncertainties and variability to the body of literature and limits generalizability of existing studies. Here, we developed a rigorous approach for assembling published evidence on cytotoxicity of several organic and inorganic nanoparticles and unraveled hidden relationships that were not targeted in the original publications. We used a machine learning approach that employs decision trees together with feature selection algorithms ( e.g., Gain ratio) to analyze a set of published nanoparticle cytotoxicity sample data (2896 samples). The specific studies were selected because they specified nanoparticle-, cell-, and screening method-related attributes. The resultant decision-tree classifiers are sufficiently simple, accurate, and with high prediction power and should be widely applicable to a spectrum of nanoparticle cytotoxicity settings. Among several influential attributes, we show that the cytotoxicity of nanoparticles is primarily predicted from the nanoparticle material chemistry, followed by nanoparticle concentration and size, cell type, and cytotoxicity screening indicator. Overall, our study indicates that following rigorous and transparent methodological experimental approaches, in parallel to continuous addition to this data set developed using our approach, will offer higher predictive power and accuracy and uncover hidden relationships. Results obtained in this study help focus future studies to develop nanoparticles that are safe by design.
开发工程纳米粒子潜在细胞毒性的预测建模框架对于环境和健康风险分析至关重要。由于纳米粒子潜在风险的可用数据的复杂性和异质性，以及相关影响属性的相互依存性，因此难以开发纳米粒子毒性行为的概括。缺乏系统的方法来进一步研究这些风险，进一步增加了文献中的不确定性和可变性，并限制了现有研究的通用性。在这里，我们开发了一种严格的方法，用于汇集关于几种有机和无机纳米粒子细胞毒性的已发表证据，并揭示了原始出版物中未针对的隐藏关系。我们使用了一种机器学习方法，该方法使用决策树和特征选择算法（例如增益比）来分析一组已发表的纳米粒子细胞毒性样本数据（2896 个样本）。选择特定的研究是因为它们指定了纳米粒子、细胞和筛选方法相关的属性。所得决策树分类器足够简单、准确且具有较高的预测能力，并且应该广泛适用于各种纳米粒子细胞毒性设置。在几个有影响力的属性中，我们表明，纳米粒子的细胞毒性主要可以根据纳米粒子材料化学、纳米粒子浓度和大小、细胞类型和细胞毒性筛选指标来预测。总体而言，我们的研究表明，在使用我们的方法不断补充数据的同时，遵循严格透明的方法学实验方法将提供更高的预测能力和准确性，并揭示隐藏的关系。本研究的结果有助于将未来的研究重点放在通过设计开发安全的纳米粒子上。
