Rational Method Development of In Vitro Release Methodologies for Nanomedicines

纳米药物体外释放方法的合理方法开发

• 批准号: 2739115
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2739115
• 关键词: Rational; Method; Development; Vitro; Release

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.

在过去的几十年里，人们一直在研究和开发纳米药物，因为它们的小尺寸为药物输送提供了诱人的特性，例如能够通过某些生物屏障或绕过体内的某些消除过程。总括术语纳米医学包括一系列不同的药物配方方法，如聚合物纳米颗粒、脂泡和树枝状大分子。尽管在纳米药物制剂的制造和物理化学性质方面进行了大量的工作，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路线相比，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，这一差距部分源于对影响纳米药物药物释放的关键参数缺乏基本了解，如果可能的话，还缺乏普遍了解。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，在这个项目中，我们提出了一种合理和系统的方法来管理和获取三类纳米药物制剂的药物释放数据：聚合物纳米粒、脂泡和树枝状大分子。我们首先将建立适当的分析管道来表征这些制剂的药物释放，然后使用计算方法来确定这些制剂的关键物理化学性质，这些特性可用于对这些制剂的药物释放情况进行分类。将使用机器学习方法来建立一个预测工具，将纳米医学特性与药物释放曲线联系起来。该项目的最终成果将是一项关于纳米颗粒制剂的分类和药物释放特性的通用工作流程的建议。