一种新的混合规则模型,用于预测直接压缩的药物混合物的流动性
Magdalini Aroniada1, Gabriele Bano2, Yuliya Vueva1
1GlaxoSmithKline (GSK), Park Road, Ware SG12 0DP, United Kingdom.
International journal of pharmaceutics
|October 13, 2023
概括
这项研究引入了一项新的混合规则,用于预测制药配方的粉末混合流动性. 该模型通过从单个组件数据中估计流量特性,减少了昂贵的实验测试,有助于直接压缩制造.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 粉末流动性对于制药可制造性至关重要,影响配方选择和制造途径.
- 目前的方法严重依赖实验测试,导致高劳动力和活性药物成分 (API) 消耗.
- 直接压缩 (DC) 是一种具有成本效益的制造方法,需要具有良好的粉末流动特性.
研究的目的:
- 开发一种可预测的混合规则,用于通过直接压缩 (DC) 制造的制药配方中的粉末混合流动性.
- 为了从单个组件的流量特性中预测混合物流动性,减少对广泛实验的依赖.
- 通过各种API和DC相关配方验证模型的有效性.
主要方法:
- 基于控制颗粒固体相互作用的凝聚力来制定混合规则.
- 使用通常在工业环境中可用的数据对模型进行校准.
- 使用各种活性药物成分 (API) 和与DC相关的配方成分验证预测模型.
主要成果:
- 开发的混合规则准确地预测了用于直接压缩 (DC) 配方的粉末混合物流动性.
- 该模型有效地估计了广泛的直流相关配方组合的流量特性.
- 在API粒子属性 (形状,大小) 和粒子间力量之间建立了线性相关性,将模型预测与API基本特征联系起来.
结论:
- 开发的混合规则提供了一种务实且具有成本效益的方法,用于预测制药制造中的粉末混合液流动性.
- 这种预测模型可以显著减少对广泛实验工作的需求,节省时间和资源.
- 了解API属性和粒子间力之间的关系可以提高模型对直接压缩配方的预测能力和适用性.
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