Rational Method Development of In Vitro Release Methodologies for Nanomedicines

纳米药物在过去的几十年里得到了研究和发展，因为它们的小尺寸为药物的输送提供了诱人的特性，比如能够通过某些生物屏障或绕过体内的某些消除过程。纳米药物的总称包括一系列不同的药物配方方法，如聚合纳米颗粒、脂质囊泡和树状大分子。尽管在纳米药物制剂的制造和物理化学性质方面做了大量的工作，但仍然缺乏标准化和监管批准的程序来表征纳米药物的药物释放。与口服制剂等其他制剂途径相比，缺乏既定的药物释放程序是纳米药物开发和监管批准的主要差距。我们认为，这种差距部分源于缺乏对影响纳米药物释放的关键参数的基本和（如果可能的话）普遍理解。这可能与不同纳米药物配方的复杂性和所使用的药物释放方法的多样性有关。因此，在本项目中，我们提出了一种合理而系统的方法来整理和获取三种纳米药物配方的药物释放数据；聚合纳米颗粒、脂质囊泡和树状大分子。我们将首先建立合适的分析管道来表征这些制剂的药物释放，然后通过计算方法来确定这些制剂的关键物理化学性质，这些性质可用于对这些制剂的药物释放概况进行分类。机器学习方法将用于建立一个预测工具，将纳米药物特性与药物释放概况联系起来。该项目的最终结果将是推荐一种通用的工作流程，用于纳米颗粒基制剂的分类和药物释放表征。

Nanomedicines have been researched and developed over the last decades because their small size provides attractive properties for the delivery of drugs, such as the ability to pass certain biological barriers or bypass certain elimination processes within the body. The umbrella term nanomedicines includes a range of different drug formulation methodologies such as polymeric nanoparticles, lipid vesicles and dendrimers. Despite the considerable body of work on the manufacture and physicochemical properties of nanomedicine formulations, standardised and regulatory approved procedures to characterise drug release from nanomedicines are still lacking. Compared to other formulation routes such as oral formulations, the lack of established drug release procedures is a main gap for the development and regulatory approval of nanomedicines. We believe that this gap originates in parts from a lack of fundamental and, if possible, universal understanding of key parameters that affect drug release from nanomedicines. This is likely linked to the complexity of the different nanomedicine formulations and the diversity of drug release methodologies that have been used. In this project, we therefore propose a rational and systematic approach to curate and acquire drug release data from three classes of nanomedicine formulations; polymeric nanoparticles, lipid vesicles and dendrimers. We will first establish suitable analytical pipelines for characterising drug release from these formulations, followed by a computational approach to identifying key physicochemical properties of these formulations that can be used to categorise the drug release profile from these formulations. A machine learning approach will be used to establish a predictive tool to connect nanomedicine properties with drug release profiles. The final outcome of the project will be a recommendation for a universal workflow for the classification and drug release characterisation from nanoparticle-based formulations.