基于生理学的纳米材料药物代谢动力学 (PBPK) 建模进展

Ozlem Ozbek, Destina Ekingen Genc, Kutlu O. Ulgen
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引用次数: 0

摘要

纳米颗粒(NPs)已被广泛用于改善小分子药物的药代动力学特性和组织分布,如靶向特定组织、增强其全身循环和扩大其治疗特性。与小分子药物相比,NPs 因其复杂的多功能性而具有独特而复杂的体内处置特性。基于生理学的药代动力学(PBPK)建模是模拟材料吸收、分布、代谢和消除(ADME)特性的有力工具,可用于表征和预测各类纳米粒子的体内处置、毒性、药效和靶向暴露。本综述介绍了 PBPK 模型应用的最新进展,涉及具有独特性质的纳米颗粒、在生物系统中的处置特征、ADME 特征、PBPK 模型中对纳米颗粒转运过程的描述,以及纳米颗粒 PBPK 模型开发中遇到的挑战,并将其与小分子模型中遇到的挑战并列。综述了 PBPK 模型中应用的纳米颗粒相关、非纳米颗粒相关和种间缩放方法。体外到体内外推法(IVIVE)是一种很有前途的计算工具,可根据体外和硅学研究的结果提供体内预测。最后,介绍了基于 ML/AI 的 PBPK 建模方法的最新进展以及在估计 ADME 参数和药代动力学(PK)分析结果方面所面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials

Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials
Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting to a specific tissue of interest, enhancing their systemic circulation, and enlarging their therapeutic properties. NPs have unique and complicated in vivo disposition properties compared to small molecule drugs due to their complex multifunctionality. Physiologically based pharmacokinetic (PBPK) modeling has been a powerful tool in the simulation of the absorption, distribution, metabolism, and elimination (ADME) characteristics of the materials, and it can be used in the characterization and prediction of the systemic disposition, toxicity, efficacy, and target exposure of various types of nanoparticles. In this review, recent advances in PBPK model applications related to the nanoparticles with unique properties, and dispositional features in the biological systems, ADME characteristics, the description of transport processes of nanoparticles in the PBPK model, and the challenges in PBPK model development of nanoparticles are delineated and juxtaposed with those encountered in small molecule models. Nanoparticle related, non-nanoparticle-related, and interspecies-scaling methods applied in PBPK modeling are reviewed. In vitro to in vivo extrapolation (IVIVE) methods being a promising computational tool to provide in vivo predictions from the results of in vitro and in silico studies are discussed. Finally, as a recent advancement ML/AI-based approaches and challenges in PBPK modeling in the estimation of ADME parameters and pharmacokinetic (PK) analysis results are introduced.
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