Applying in silico approaches to nanotoxicology: Current status and future potential

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-05-01 DOI:10.1016/j.comtox.2022.100225
Natalia Lidmar von Ranke , Reinaldo Barros Geraldo , André Lima dos Santos , Victor G.O. Evangelho , Flaminia Flammini , Lucio Mendes Cabral , Helena Carla Castro , Carlos Rangel Rodrigues
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引用次数: 2

Abstract

Nanomaterial development is one of the most significant technological advances of the 21st century, with considerable impact in several fields. However, nanomaterials can pose risks to human health and the environment. Therefore, it is imperative to perform toxicological tests; nonetheless, identification and analysis of all preparations is laborious. In this regard, in silico approaches facilitate nanotoxicity assessment at low cost and without involving animal testing. In this paper we review the use of computational approaches for nanotoxicology prediction. First, we present computational nanotoxicology in a regulatory context. Next, we discuss the primary computational methods used in toxicology, such as (quantitative) structure–activity relationship models, physiologically based pharmacokinetic models, and molecular modeling, and address the singularities of each method for nanomaterial analyses. Lastly, we describe several integrative approaches for computational nanotoxicology. Various database analyses combined with complementary computational approaches can lead to creative solutions for predicting toxicological effects during the design of new nanomaterials. Therefore, data-integration methods promote understanding of complex nanotoxicological events and can be used to develop successful precision models.

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纳米毒理学的计算机应用:现状和未来潜力
纳米材料的发展是21世纪最重要的技术进步之一,在几个领域具有相当大的影响。然而,纳米材料可能对人类健康和环境构成风险。因此,必须进行毒理学试验;尽管如此,所有制剂的鉴定和分析都是费力的。在这方面,硅片方法有助于以低成本和不涉及动物试验的方式进行纳米毒性评估。在本文中,我们回顾了计算方法在纳米毒理学预测中的应用。首先,我们提出了在监管背景下的计算纳米毒理学。接下来,我们讨论了毒理学中使用的主要计算方法,如(定量)结构-活性关系模型、基于生理学的药代动力学模型和分子模型,并讨论了纳米材料分析中每种方法的独特性。最后,我们描述了计算纳米毒理学的几种综合方法。各种数据库分析与互补的计算方法相结合,可以在新纳米材料设计过程中为预测毒理学效应提供创造性的解决方案。因此,数据集成方法促进了对复杂纳米毒理学事件的理解,并可用于开发成功的精确模型。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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