Data-Driven Prediction of Nanoparticle Biodistribution from Physicochemical Descriptors.

IF 15.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
ACS Nano Pub Date : 2025-07-16 DOI:10.1021/acsnano.5c03040
Jimeng Wu,Peter Wick,Bernd Nowack
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Abstract

Nanoparticles have gained significant attention in biomedicine, electronics, and environmental science due to their unique physicochemical properties, which critically influence their absorption, distribution, metabolism, and excretion behavior in biological systems. However, predicting nanoparticle biodistribution and pharmacokinetics remains challenging due to the complexity of biological systems and the reliance on animal-derived data for physiologically based pharmacokinetic (PBPK) modeling. To address these limitations, this study integrates PBPK modeling with quantitative structure-activity (QSAR) relationship principles and multivariate linear regression (MLR) to develop a predictive framework for nanoparticle biodistribution based solely on physicochemical properties, using biodistribution data from healthy mice. Focusing exclusively on nondissolvable nanoparticles, we employed Bayesian analysis with Markov chain Monte Carlo simulations to fit PBPK models and generate kinetic parameters. The MLR-PBPK framework demonstrated strong predictive accuracy for kinetic indicators (adjusted R2 up to 0.9) and successfully simulated nanoparticle biodistribution across 18 experiments. Key physicochemical properties such as zeta potential, size, and coating were identified as the most influential predictors, while the core material and shape had lesser impacts. Despite its success, the model faced limitations in predicting concentration-time curves for certain nanoparticles, highlighting the need for expanded data sets and nonlinear modeling approaches. This study provides a robust, nonanimal alternative for nanoparticle risk assessment, advancing safe and sustainable by design (SSbD) frameworks and offering a valuable tool for early-stage nanoparticle evaluation and design.
基于物理化学描述符的纳米粒子生物分布数据驱动预测。
纳米粒子由于其独特的物理化学性质在生物系统中对其吸收、分布、代谢和排泄行为具有重要影响,因此在生物医学、电子和环境科学领域受到了极大的关注。然而,由于生物系统的复杂性以及基于生理的药代动力学(PBPK)建模依赖于动物来源的数据,预测纳米颗粒的生物分布和药代动力学仍然具有挑战性。为了解决这些局限性,本研究将PBPK模型与定量结构-活性(QSAR)关系原理和多元线性回归(MLR)相结合,利用健康小鼠的生物分布数据,仅基于物理化学性质,开发了纳米颗粒生物分布的预测框架。研究人员利用贝叶斯分析和马尔可夫链蒙特卡罗模拟来拟合PBPK模型并生成动力学参数。MLR-PBPK框架对动力学指标具有很强的预测准确性(将R2调整至0.9),并在18个实验中成功模拟了纳米颗粒的生物分布。关键的物理化学性质,如zeta电位、尺寸和涂层被认为是最具影响力的预测因素,而核心材料和形状的影响较小。尽管取得了成功,但该模型在预测某些纳米颗粒的浓度-时间曲线方面存在局限性,这突出表明需要扩展数据集和非线性建模方法。这项研究为纳米颗粒风险评估提供了一个可靠的、非动物的替代方法,推进了设计安全和可持续(SSbD)框架,并为早期纳米颗粒评估和设计提供了一个有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Nano
ACS Nano 工程技术-材料科学:综合
CiteScore
26.00
自引率
4.10%
发文量
1627
审稿时长
1.7 months
期刊介绍: ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.
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