Suppr超能文献

生理相关药代动力学模型在固体药物纳米颗粒传递中的新兴作用。

The emerging role of physiologically based pharmacokinetic modelling in solid drug nanoparticle translation.

机构信息

Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK.

Department of Chemistry, University of Liverpool, Crown Street, Liverpool, UK.

出版信息

Adv Drug Deliv Rev. 2018 Jun;131:116-121. doi: 10.1016/j.addr.2018.06.016. Epub 2018 Jun 27.

Abstract

The use of solid drug nanoparticles (SDN) has become an established approach to improve drug delivery, supporting enhancement of oral absorption and long-acting administration strategies. A broad range of SDNs have been successfully utilised for multiple products and several development programmes are currently underway across different therapeutic areas. With some approaches, a large range of material space is available with diversity in physical characteristics, excipient choice and pharmacological behaviour. The selection of SDN lead candidates is a complex process including a broad range of in vitro and in vivo data, and a better understanding of how physical characteristics relate to performance is required. Physiologically-based pharmacokinetic (PBPK) modelling is based upon a comprehensive integration of experimental data into a mathematical description of drug distribution, allowing simulation of SDN pharmacokinetics that can be qualified in vivo prior to human prediction. This review aims to provide a description of how PBPK can find application into the development of SDN. Integration of predictive PBPK modelling into SDN development allows a better understanding of the SDN dose-response relationship, supporting a framework for rational optimisation while reducing the risk of failure in developing safe and effective nanomedicines.

摘要

固体药物纳米粒(SDN)的应用已成为改善药物递送的一种既定方法,支持增强口服吸收和长效给药策略。已经成功地将广泛的 SDN 用于多种产品,并且目前正在不同治疗领域开展多个开发项目。对于一些方法,材料空间范围很广,具有物理特性、赋形剂选择和药理学行为的多样性。SDN 先导候选物的选择是一个复杂的过程,包括广泛的体外和体内数据,并且需要更好地了解物理特性如何与性能相关。基于生理的药代动力学(PBPK)模型是基于将实验数据综合整合到药物分布的数学描述中,允许模拟 SDN 药代动力学,然后在体内进行合格预测,以预测人体。本综述旨在描述 PBPK 如何应用于 SDN 的开发。将预测性 PBPK 建模整合到 SDN 开发中,可以更好地理解 SDN 的剂量反应关系,支持合理优化的框架,同时降低开发安全有效的纳米药物失败的风险。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验