对多元组件混合物的Styl'One进化校正因子的建模扩展到滚筒紧缩
Fabia Arpago1, Agostino Dall'Ara1
1F. Hoffmann-La Roche AG, Dept. PTDC-F Pharmaceutical R&D, Grenzacherstrasse 124, 4070 Basel, Switzerland.
Journal of pharmaceutical sciences
|April 12, 2024
概括
预测卷紧缩 (RC) 性能对于高效的制药配方至关重要. 本研究确定了一种通用校正因子,可以从机械模拟器准确估计带固体分数 (SF),从而改善扩展和早期开发.
科学领域:
- 制药科学 制药科学
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
背景情况:
- 卷压缩 (RC) 是制药制造中的一个重要的干颗粒技术.
- 准确预测RC参数,如特定压缩力 (SCF) 和间隙宽度,对于实现目标带固体分数 (SF) 是必不可少的,特别是在早期开发中材料可用性有限的情况下.
- 当前的RC模拟器往往高估SF与实际的滚筒压缩机结果相比,需要改进的预测模型.
研究的目的:
- 开发和验证可靠的校正因子,用于预测卷压缩 (RC) 中的带固体分数 (SF).
- 通过使RC模拟器能够准确预测,提高制药配方开发的效率.
- 为了建立一个普遍适用于多组分混合物的校正系数,独立于材料特性.
主要方法:
- 使用机械模拟器 (Styl'One Evolution) 和实际的滚筒压缩机 (Gerteis Mini-Pactor) 在各种特定的压缩力 (SCF) 中压缩了25种不同的配方 (40%的药物负载).
- 应用了简化的约翰逊数学模型,并根据材料特性和压力依赖性计算出了四个不同的校正因数.
- 评估了每个校正因子的预测准确度,并将其与来自Gerteis Mini-Pactor的实验数据进行了对比.
主要成果:
- 实验表明,与工业滚筒压缩机相比,RC模拟器往往会高估固体分数 (SF).
- 计算了四种不同的校正因子,它们在考虑材料特性和压力依赖性方面有所不同.
- 一个特定的校正因子被确定为最佳,在Gerteis迷你电压器上提供了准确的SF预测,并且证明独立于材料特性.
结论:
- 一个单一的,与材料无关的校正因子使得在Gerteis Mini-Pactor上准确模拟多元组合混合物的压缩行为.
- 这一发现显著提高了RC模拟器的预测能力,减少了对广泛实验运行的需求.
- 识别的校正因子对于将工艺扩展到Gerteis迷你包装机以及新药配方的早期开发非常重要.
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