Application of a novel particle coating technology for paediatric formulation development

“背景：缺乏适合儿科人群的药物意味着，未经许可的配方往往由卫生保健专业人员配制和管理，这就需要将这一弱势群体暴露在安全性和临床疗效方面的风险中。限制儿科医学发展进展的诸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了解。儿科配方的翻译受到限制，这是由于现有的技术有限，无法配制适用于大范围高剂量药物[1]、[2]、[3]的商业上可行的产品。此外，用于安全纳入儿科药物的辅料选择有限，再加上口味可接受性问题，给配方带来了额外的困难。导致这一提议的工作：阿斯顿开发的干颗粒涂层是一种新颖的颗粒工程技术，首次允许通过流化和对加工参数的精确控制将细颗粒分层在粗颗粒上。该方法允许高药物负荷，最小的赋形剂负荷和可接受的味道，并且可以定制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植物动态分析和控制的新方法[4]，[5]。明确地说，我们使用机器学习方法的方法，如用于自动响应面方法的径向基函数网络[6]，在配方开发之前从未开发过。它在收敛速度和解的准确性方面优于现有的响应面方法和群优化的机器学习方法，并且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、易碎剂型的生产提供预测工具。该项目将包括了解干燥涂层过程中工艺参数对小药物分子和生物制剂（如多肽和蛋白质）的稳定性和性能的影响。在配方优化之后，本项目的工作将涉及基于细胞培养的转运和毒性研究，以开发对体内配方性能的机制理解。此外，将进行表征研究，包括HPLC，蛋白质/肽稳定性指示分析，激光衍射研究，以研究颗粒大小和电荷，以表征粉末混合物。”

"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. "