Accurate and meaningful dose metrics are a basic requirement for in vitro screening to assess potential health risks of engineered nanomaterials (ENMs). Correctly and consistently quantifying what cells “see,” during an in vitro exposure requires standardized preparation of stable ENM suspensions, accurate characterizatoin of agglomerate sizes and effective densities, and predictive modeling of mass transport. Earlier transport models provided a marked improvement over administered concentration or total mass, but included assumptions that could produce sizable inaccuracies, most notably that all particles at the bottom of the well are adsorbed or taken up by cells, which would drive transport downward, resulting in overestimation of deposition. Here we present development, validation and results of two robust computational transport models. Both three-dimensional computational fluid dynamics (CFD) and a newly-developed one-dimensional Distorted Grid (DG) model were used to estimate delivered dose metrics for industry-relevant metal oxide ENMs suspended in culture media. Both models allow simultaneous modeling of full size distributions for polydisperse ENM suspensions, and provide deposition metrics as well as concentration metrics over the extent of the well. The DG model also emulates the biokinetics at the particle-cell interface using a Langmuir isotherm, governed by a user-defined dissociation constant, K D, and allows modeling of ENM dissolution over time. Dose metrics predicted by the two models were in remarkably close agreement. The DG model was also validated by quantitative analysis of flash-frozen, cryosectioned columns of ENM suspensions. Results of simulations based on agglomerate size distributions differed substantially from those obtained using mean sizes. The effect of cellular adsorption on delivered dose was negligible for K D values consistent with non-specific binding (> 1 nM), whereas smaller values (≤ 1 nM) typical of specific high-affinity binding resulted in faster and eventual complete deposition of material. The advanced models presented provide practical and robust tools for obtaining accurate dose metrics and concentration profiles across the well, for high-throughput screening of ENMs. The DG model allows rapid modeling that accommodates polydispersity, dissolution, and adsorption. Result of adsorption studies suggest that a reflective lower boundary condition is appropriate for modeling most in vitro ENM exposures.

中文翻译：

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体外纳米材料剂量学的高级计算模型。

准确而有意义的剂量指标是体外筛查评估工程纳米材料（ENM）潜在健康风险的基本要求。正确，一致地量化体外暴露过程中“看到”的细胞，需要稳定的ENM悬浮液的标准化制备，附聚物大小和有效密度的准确表征以及质量传递的预测模型。较早的运输模型对施用的浓度或总质量提供了显着的改善，但包括可能产生相当大误差的假设，最值得注意的是孔底部的所有颗粒均被细胞吸收或吸收，这将推动运输向下，从而导致高估沉积物。在这里，我们介绍两个健壮的计算传输模型的开发，验证和结果。三维计算流体动力学（CFD）和新开发的一维扭曲网格（DG）模型均用于估算悬浮在培养基中的与行业相关的金属氧化物ENM的输送剂量指标。两种模型都可以对多分散ENM悬浮液的全尺寸分布进行同时建模，并提供整个井范围内的沉积指标以及浓度指标。DG模型还使用Langmuir等温线模拟颗粒-细胞界面的生物动力学，该等温线受用户定义的解离常数K D控制，并允许对ENM随时间的溶解进行建模。这两个模型预测的剂量指标非常接近。DG模型也通过定量分析ENM悬浮液的速冻，冷冻切片柱进行了验证。基于附聚物尺寸分布的模拟结果与使用平均尺寸获得的结果大不相同。对于与非特异性结合（> 1 nM）一致的K D值，细胞吸附对递送剂量的影响可忽略不计，而特异性高亲和力结合的较小的K D值（≤1 nM）则导致更快，最终的材料完全沉积。提出的高级模型提供了实用而强大的工具，可用于获得整个孔的准确剂量指标和浓度曲线，以进行高通量的ENM筛选。DG模型允许进行快速建模，以适应多分散性，溶解性和吸附性。吸附研究的结果表明，反射下边界条件适合于模拟大多数体外ENM暴露。