Intelligent Automation, Inc., Rockville, MD, USA.
Int J Nanomedicine. 2013;8 Suppl 1(Suppl 1):31-43. doi: 10.2147/IJN.S40742. Epub 2013 Sep 16.
Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials.
We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric) was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials.
We found several important attributes that contribute to the 24 hours post-fertilization (hpf) mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of nanomaterials. Sample prediction models can be found at http://neiminer.i-a-i.com/nei_models.
The EZ Metric-based data mining approach has been shown to have predictive power. The results provide valuable insights into the modeling and understanding of nanomaterial exposure effects.
预测纳米材料的生物效应对于工业界和政策制定者评估工程纳米材料应用所带来的潜在危害至关重要。
我们生成了一个关于纳米材料暴露对胚胎斑马鱼产生毒性影响的实验数据集。研究了几种纳米材料，如金属纳米粒子、树枝状聚合物、金属氧化物和聚合物材料。胚胎斑马鱼指标（EZ 指标）被用作代表不良影响的筛选水平测量。利用该数据集，我们开发了一种数据挖掘方法来模拟纳米材料的毒性终点和整体生物影响。数据挖掘技术，如数值预测，可以帮助分析人员为纳米材料开发风险评估模型。
我们发现了几个对 24 小时后孵化（hpf）死亡率有重要影响的属性，如剂量浓度、壳组成和表面电荷。这些发现与使用胚胎斑马鱼的纳米材料毒性的先前研究一致。我们对纳米材料整体效应/影响以及特定毒性终点（如死亡率、发育延迟和形态畸形）的建模进行了案例研究。结果表明，我们可以实现某些生物效应（如 24 hpf 死亡率、120 hpf 死亡率和 120 hpf 心脏畸形）的高预测准确性。结果还表明，个体生物效应的加权方案对纳米材料整体影响的建模有重大影响。示例预测模型可在 http://neiminer.i-a-i.com/nei_models 上找到。
基于 EZ 指标的数据挖掘方法已被证明具有预测能力。结果为纳米材料暴露效应的建模和理解提供了有价值的见解。
