Abstract

Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm; iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.

摘要

纳米粒子 (NP) 是目前用于多种工业和生物医学应用的一类广泛材料。由于它们的小尺寸（1-100 nm），NPs 很容易进入人体，引起组织损伤。NP 毒性取决于物理和化学 NP 特性（例如，大小、电荷和表面积），其方式和大小仍然未知。我们评估了 NP 原子描述符的平均值和个体重要性，以及化学性质和实验条件，以确定几种纳米材料的细胞毒性终点。我们采用多中心细胞毒性纳米材料数据库（12 种不同的材料，其第一维和第二维分别介于 2.70 和 81.2 nm 和 4.10 和 4048 nm 之间）。我们开发了一个基于极端梯度提升和超参数优化的回归模型。我们采用 Shapley 附加解释来获得良好的细胞毒性预测性能。模型性能被量化为真实值和预测值之间具有统计学意义的 Spearman 相关性，范围从 0.5 到 0.7。我们的结果表明，i) 原位尺寸和表面积分别大于 200 nm 和 50 m2/g，ii) 初级颗粒小于 20 nm；iii) 不规则（即非球形）形状和 iv) 正 Z 电位对 NP 细胞毒性的预测贡献最大，特别是如果乳酸脱氢酶 (LDH) 测定用于短实验时间。这些结果在毒性终点方面是适度稳定的，尽管在剂量量化方法中出现了一定程度的可变性，证实了纳米生物相互作用的复杂性和对大型、