Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles.

IF 2.6 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Beilstein Journal of Nanotechnology Pub Date : 2024-09-10 eCollection Date: 2024-01-01 DOI:10.3762/bjnano.15.93
Supratik Kar, Siyun Yang
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Abstract

Metal oxide nanoparticles (MONPs) are widely used in medicine and environmental remediation because of their unique properties. However, their size, surface area, and reactivity can cause toxicity, potentially leading to oxidative stress, inflammation, and cellular or DNA damage. In this study, a nano-quantitative structure-toxicity relationship (nano-QSTR) model was initially developed to assess zebrafish toxicity for 24 MONPs. Previously established 23 first- and second-generation periodic table descriptors, along with five newly proposed third-generation descriptors derived from the periodic table, were employed. Subsequently, to enhance the quality and predictive capability of the nano-QSTR model, a nano-quantitative read across structure-toxicity relationship (nano-qRASTR) model was created. This model integrated read-across descriptors with modeled descriptors from the nano-QSTR approach. The nano-qRASTR model, featuring three attributes, outperformed the previously reported simple QSTR model, despite having one less MONP. This study highlights the effective utilization of the nano-qRASTR algorithm in situations with limited data for modeling, demonstrating superior goodness-of-fit, robustness, and predictability (R 2 = 0.81, Q 2 LOO = 0.70, Q 2 F1/R 2 PRED = 0.76) compared to simple QSTR models. Finally, the developed nano-qRASTR model was applied to predict toxicity data for an external dataset comprising 35 MONPs, addressing gaps in zebrafish toxicity assessment.

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引入第三代元素周期表描述符,对金属氧化物纳米颗粒的斑马鱼毒性进行纳米-qRASTR建模。
金属氧化物纳米粒子(MONPs)因其独特的性能而被广泛应用于医学和环境修复领域。然而,它们的尺寸、表面积和反应性会导致毒性,可能导致氧化应激、炎症、细胞或 DNA 损伤。在本研究中,初步建立了一个纳米定量结构-毒性关系(nano-QSTR)模型,以评估 24 种 MONPs 的斑马鱼毒性。采用了之前建立的 23 个第一代和第二代元素周期表描述符,以及从元素周期表中新提出的 5 个第三代描述符。随后,为了提高纳米 QSTR 模型的质量和预测能力,又创建了一个纳米定量跨结构毒性关系(nano-qRASTR)模型。该模型整合了纳米 QSTR 方法中的跨读描述符和建模描述符。纳米 QRASTR 模型具有三个属性,尽管少了一个 MONP,但其性能优于之前报道的简单 QSTR 模型。这项研究强调了在建模数据有限的情况下对纳米 QRASTR 算法的有效利用,与简单 QSTR 模型相比,纳米 QRASTR 算法具有更高的拟合度、稳健性和可预测性(R 2 = 0.81,Q 2 LOO = 0.70,Q 2 F1/R 2 PRED = 0.76)。最后,所开发的纳米 QRASTR 模型被用于预测由 35 种 MONPs 组成的外部数据集的毒性数据,以填补斑马鱼毒性评估方面的空白。
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来源期刊
Beilstein Journal of Nanotechnology
Beilstein Journal of Nanotechnology NANOSCIENCE & NANOTECHNOLOGY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.70
自引率
3.20%
发文量
109
审稿时长
2 months
期刊介绍: The Beilstein Journal of Nanotechnology is an international, peer-reviewed, Open Access journal. It provides a unique platform for rapid publication without any charges (free for author and reader) – Platinum Open Access. The content is freely accessible 365 days a year to any user worldwide. Articles are available online immediately upon publication and are publicly archived in all major repositories. In addition, it provides a platform for publishing thematic issues (theme-based collections of articles) on topical issues in nanoscience and nanotechnology. The journal is published and completely funded by the Beilstein-Institut, a non-profit foundation located in Frankfurt am Main, Germany. The editor-in-chief is Professor Thomas Schimmel – Karlsruhe Institute of Technology. He is supported by more than 20 associate editors who are responsible for a particular subject area within the scope of the journal.
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