Application of a novel particle coating technology for paediatric formulation development

新型颗粒包衣技术在儿科制剂开发中的应用

• 批准号: 2431160
• 金额: --
• 依托单位: Aston University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2431160
• 关键词: Application; novel; particle; coating; technology

"Background: The lack of suitable medicines for the paediatric population means that unlicensed formulations are often prepared and administered by healthcare professionals, which entails exposing this vulnerable population to risks in terms of safety and clinical efficacy. The multitude of challenges restricting progress in paediatric medicine development include lack of understanding of dosage forms that offer dose flexibility, safety and commercial feasibility. The translation of paediatric formulations has been limited, due to the limited availability of technologies to formulate commercially viable products that would be applicable for a wide range of high dose drugs[1],[2],[3]. In addition, a limited choice of excipients for safe inclusion in paediatric medicines, together with taste acceptability issues, presents an additional layer of formulation difficulty.Work leading to this proposal: Dry particle coating developed at Aston is a novel particle engineering technology that, for the first time, will permit layering of fine particles over coarse particles through fluidisation and meticulous precision control of processing parameters. This method allows high drug loading, minimal excipient load and acceptable taste and can be tailored to handle small molecules and biologicals. In data analytics, we have established new approaches for data visualisation, data abstractions, probabilistic modelling and prediction techniques, and analysis and control of plant dynamics[4],[5]. Explicitly, our approach for using machine learning methods, such as radial basis function networks[6] for automated response surface methodology, has never been developed before in formulation development. It is superior to existing machine learning approaches of response surface methodology and swarm optimisation in terms of speed of convergence and accuracy of solution, and has the advantage of not being limited to locally quadratic surfaces. The overarching hypothesis of the proposal is that application of machine learning will produce predictive tools for the manufacture of high dose, fragile dosage forms including biologicals. This project will involve understanding the impact of processing parameters during dry coating on the stability and performance of small drug molecules and biologics such as peptides and proteins. Following the optimisation of formulations, work in this project will involve cell culture based transport and toxicity studies to develop a mechanistic understanding of formulation performance in vivo. Additionally, characterisation studies including HPLC, protein/peptide stability indicating assays, laser diffraction studies to study particle size and charge will be carried out to characterise powder blends. "

“背景：缺乏适合儿科人群的药物意味着未经许可的制剂通常由医疗保健专业人员配制和施用，这使得这一弱势群体面临安全性和临床疗效方面的风险。限制儿科药物开发进展的众多挑战包括缺乏对提供剂量灵活性、安全性和商业可行性的剂型的理解。儿科制剂的转化受到限制，因为配制适用于各种高剂量药物的商业可行产品的技术有限[1]，[2]，[3]。此外，有限的选择的赋形剂安全纳入儿科药品，加上口味的可接受性问题，提出了一个额外的层的配方difficulty.Work导致这一建议：干颗粒涂层开发在阿斯顿是一种新颖的颗粒工程技术，首次将允许分层的细颗粒粗颗粒通过流化和细致的精度控制的工艺参数。这种方法允许高载药量、最小赋形剂载量和可接受的味道，并且可以定制以处理小分子和生物制剂。在数据分析方面，我们已经建立了数据可视化，数据抽象，概率建模和预测技术以及工厂动态分析和控制的新方法[4]，[5]。可以解释的是，我们使用机器学习方法的方法，例如用于自动响应曲面方法的径向基函数网络[6]，以前从未在配方开发中开发过。它是上级现有的机器学习方法的响应面方法和群体优化的收敛速度和精度方面的解决方案，并具有不局限于局部二次曲面的优点。该提案的首要假设是，机器学习的应用将为包括生物制剂在内的高剂量、脆弱剂型的制造提供预测工具。该项目将涉及了解干法包衣过程中工艺参数对小药物分子和生物制剂（如肽和蛋白质）的稳定性和性能的影响。在配方优化之后，该项目的工作将涉及基于细胞培养的运输和毒性研究，以建立对体内配方性能的机械理解。此外，还将进行表征研究，包括HPLC、蛋白质/肽稳定性指示试验、激光衍射研究，以研究粒度和电荷，从而确定粉末混合物。"