高效节能的药物固体口服剂型智能制造

Ashley Dan, Rohit Ramachandran
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引用次数: 0

摘要

背景:全球制药市场是一个万亿美元的产业,年增长率超过5%。然而,与其他制造业(如炼油、汽车)相比,制药行业在制造创新和自动化方面落后。在药物固体剂型的生产中,利用能量利用率作为生产效率的绩效指标既没有得到广泛实施,也没有得到优化以实现效率最大化。本研究将侧重于智能制造平台的开发和实施,通过考虑不同的制造场景来优化能源生产率,同时保持平板电脑的质量。 方法:本研究将考虑三个主要的单元操作(湿造粒、干燥和碾磨),它们在制药下游加工中相对更耗能,用于生产固体剂型,如片剂。将考虑四个案例研究,它们是1:基线批次,2:基线连续,3:优化批次和4:优化连续。实施智能制造以呈现优化案例3和4:对能源和性能指标的改进进行量化,并与基线案例进行比较。& # x0D;结果与结论:本研究采用的智能制造平台,集成了先进的工艺模型开发、优化、技术经济分析和数据集成。与基线批量情况相比,该框架的使用分别使优化批量和连续情况下的能源利用率提高了70%和80%。在优化的情况下,片剂质量保持在目标规格内,并与基线批次情况相当。这个智能制造框架可以推广到药品制造和其他基于颗粒的行业,如食品、农业和精细化工。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy efficient smart manufacturing of pharmaceutical solid oral dosage forms
Background: The global pharmaceuticals market is a trillion-dollar industry which grows more than 5% annually. However, in comparison to other manufacturing industries (e.g., oil refining, automotive), the pharmaceutical sector lags in manufacturing innovation and automation. In the production of pharmaceutical solid dosage forms, the use of energy utilization as a performance measure of production efficiency has neither been implemented extensively, nor been optimized to maximize efficiency. This study will focus on the development and implementation of a smart manufacturing platform to optimize energy productivity whilst maintaining tablet quality via the consideration of different manufacturing scenarios. Methods: This study will consider three main unit operations (wet granulation, drying and milling) which are relatively more energy intensive in pharmaceutical downstream processing, used to produce solid dosage forms, such as tablets. Four case-studies will be considered, which are 1: baseline batch, 2: baseline continuous, 3: optimized batch and 4: optimized continuous. Smart manufacturing is implemented to present optimized cases 3: and 4: Improvements in the energy and performance metrics are quantified and compared to the baseline cases. Results and conclusions: The smart manufacturing platform used in this study, integrates advanced process model development, optimization, technoeconomic analysis and data integration. The utilization of this framework contributed to a ~70% and ~80% improvement in energy utilization in the optimized batch and continuous cases, respectively, when compared to the baseline batch case. In the optimized cases, tablet quality was maintained within targeted specifications and was comparable to the baseline batch case. This smart manufacturing framework can be generalized for drug product manufacturing and other particulate-based industries such as food, agriculture, and fine chemicals.
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