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.

固体药物纳米颗粒（SDN）的使用已成为改善药物输送，支持增强口服吸收和长效给药策略的公认方法。广泛的SDN已被成功地用于多种产品，并且目前正在不同治疗领域中进行一些开发计划。通过某些方法，可以提供大范围的物质空间，这些物质空间在物理特性，赋形剂选择和药理学行为上都各不相同。SDN潜在候选人的选择是一个复杂的过程，包括广泛的体内和体外数据，并需要更好地了解物理特性与性能之间的关系。基于生理的药代动力学（PBPK）建模基于将实验数据全面集成到药物分布的数学描述中，从而可以模拟在人类预测之前可以在体内鉴定的SDN药代动力学。本文旨在描述PBPK如何在SDN的开发中找到应用。将预测性PBPK模型集成到SDN开发中可以更好地理解SDN剂量反应关系，支持合理优化的框架，同时减少开发安全有效的纳米药物失败的风险。