新型混合模型用于预测滚筒紧缩过程中的带固体分数
Kiran Iyer1, Senthil Kumarasamy2, Pankaj Doshi3
1Pfizer Research and Development, Pfizer Healthcare India Pvt Ltd, Thousand Lights, 237 Anna Salai, Chennai, Tamil Nadu, 600006, India. Kiran.Iyer@pfizer.com.
AAPS PharmSciTech
|December 1, 2025
概括
一个新的校准模型预测了使用最小数据的滚筒紧缩扩展. 这种方法有助于开发用于高效制造药品的数字工具.
科学领域:
- 制药工程 制药工程
- 过程化学 过程化学
- 材料科学 材料科学 材料科学
背景情况:
- 滚筒压缩是制药制造中生产颗粒的关键单元操作.
- 升级滚筒压缩通常需要大量的实验数据,这是耗时和昂贵的.
- 预测建模可以优化滚筒压缩过程,减少实验负担.
研究的目的:
- 开发和验证一个校准模型,用于扩大滚筒紧缩工艺的规模.
- 使用最小的实验数据来准确预测带状固体分数.
- 实现数字工具的应用,以提高药物产品开发过程的效率.
主要方法:
- 实验室规模的滚筒压缩模拟,以评估混合物的压缩特性.
- 开发一个校准模型,结合雷诺斯滚筒压缩模型和带固体分数测量.
- 对不同尺度和设备的实验数据进行模型的验证.
- 将模型扩展为混合方法,将部分特征混合物的API和配方属性纳入其中.
主要成果:
- 校准模型准确地预测了不同尺度和药物负载的带固体分数.
- 混合模型显示了对部分特征混合物的实验测量有合理的一致性.
- 通过使用混合模型来定义操作范围,成功实施了数字实验设计 (DOE).
- 开发的方法显著减少了用于滚筒紧缩扩展所需的实验数据.
结论:
- 一个经过验证,校准的模型能够用最小的数据准确预测滚筒紧缩行为.
- 混合模型为制药产品的扩展和流程优化提供了一个强大的工具.
- 滚筒压缩规模的数字化促进了快速移动药物产品的高效开发.
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