Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK.
Department of Chemistry, University of Liverpool, Crown Street, Liverpool, UK.
Adv Drug Deliv Rev. 2018 Jun;131:116-121. doi: 10.1016/j.addr.2018.06.016. Epub 2018 Jun 27.
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.
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。