Rational Method Development of In Vitro Release Methodologies for Nanomedicines

在过去的几十年里，纳米医学已经得到了研究和开发，因为它们的小尺寸为药物的递送提供了有吸引力的特性，例如能够通过某些生物屏障或绕过体内的某些消除过程。纳米医学包括一系列不同的药物制剂方法，如聚合物纳米颗粒，脂质囊泡和树枝状聚合物。尽管在纳米药物制剂的制造和物理化学性质方面有相当多的工作，但仍然缺乏标准化和监管批准的程序来阻止药物从纳米药物中释放。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，这种差距部分源于对影响纳米药物释放的关键参数缺乏基本的，如果可能的话，普遍的理解。这可能与不同纳米药物制剂的复杂性和所使用的药物释放方法的多样性有关。因此，在这个项目中，我们提出了一个合理的和系统的方法来策划和获取药物释放数据从三类纳米药物制剂;聚合物纳米颗粒，脂质囊泡和树枝状聚合物。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理化性质，这些理化性质可用于对这些制剂的药物释放曲线进行分类。机器学习方法将用于建立一种预测工具，将纳米医学特性与药物释放曲线联系起来。该项目的最终结果将是对基于纳米颗粒的制剂的分类和药物释放表征的通用工作流程的建议。

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.