针对药物固体制剂连续生产过程中的滑剂和润滑剂效应,开发机械还原阶次模型 (ROM)。

Sunidhi Bachawala, Marcial Gonzalez
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引用次数: 0

摘要

随着制药业从批量生产向连续生产过渡,实时监控和基于机械模型的控制对于符合美国食品及药物管理局的质量标准至关重要。众所周知,滑润剂和润滑剂会影响片剂的关键质量属性(CQAs),如拉伸强度、片剂孔隙率和溶出曲线(Razavi 等人,2018 年;Apeji 和 Olowosulu,2020 年)。预测这些影响的定量模型对于在直接压片生产线中实现润滑剂和滑润剂喂料与混合的集中控制策略至关重要。这项工作介绍了减阶机械模型的开发情况,以捕捉润滑剂(硬脂酸镁)和滑胶剂(二氧化硅)对 CQAs 和关键工艺参数 (CPP) 的影响。使用 Natoli NP400 压片机和 SOTAX AT4 片剂测试仪,对白炭黑与 MCC(Avicel PH200)和 APAP(对乙酰氨基酚)进行了 30 种不同混合条件的拉丁超立方实验。实验表明,白炭黑的混合条件对拉伸强度和混合体积密度有很大影响。同样,添加硬脂酸镁(MgSt)会改变混合物的体积密度、形成片剂所需的压实力以及片剂的抗张强度,具体取决于润滑条件(Mehrotra 等人,2007 年;Razavi 等人,2018 年)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Development of mechanistic reduced order models (ROMs)for glidant and lubricant effects in continuous manufacturing of pharmaceutical solid-dosage forms.

As the pharmaceutical industry transitions from batch to continuous manufacturing, real-time monitoring, and mechanistic model-based control are essential to conform to FDA quality standards. Glidants and lubricants are known to affect the Critical Quality Attributes (CQAs) of a tablet such as tensile strength, tablet porosity, and dissolution profile (Razavi et al., 2018; Apeji and Olowosulu, 2020). Quantitative models for predicting these effects are essential for enabling centralized control strategies of lubricant and glidant feeding and blending in direct compression tableting lines. This work presents the development of mechanistic reduced order models to capture the effects of lubricant (magnesium stearate) and glidant (silica) on CQAs and Critical Process Parameters (CPPs). A Latin Hypercube experimental campaign with thirty different mixing conditions of silica with MCC (Avicel PH200) and APAP (Acetaminophen) was carried out using a Natoli NP400 tablet press and a SOTAX AT4 tablet tester. Experiments show that the tensile strength and blend bulk density are significantly affected by the mixing conditions of silica. Similarly, adding magnesium stearate (MgSt) changes the bulk density of the blend, compaction force required to form a tablet, and tensile strength of the tablet, depending on the lubrication conditions (Mehrotra et al., 2007; Razavi et al., 2018).

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Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks. Continuous-Time Surrogate Models for Data-Driven Dynamic Optimization. Development of mechanistic reduced order models (ROMs)for glidant and lubricant effects in continuous manufacturing of pharmaceutical solid-dosage forms. Development of a Virtual Sensor for Real-Time Prediction of Granule Flow Properties. A Hierarchical Approach to Monitoring Control Performance and Plant-Model Mismatch.
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