Purpose

In pharmaceutical manufacturing, understanding and quantifying how process conditions impact product quality is pivotal to guaranteeing process profitability with sustained product yield. We describe an integrated system model for a commercial-scale continuous manufacturing process of a high-value active pharmaceutical ingredient (API) and its use to optimize process conditions to maximize yield with assured product quality.

Methodology

Global sensitivity analysis (GSA) was used to assess different process parameters’ impacts on API yield in order to guide the selection of decision variables for yield optimization. We then formulated different scenarios for optimization within approved process parameter ranges to propose optimal process conditions to increase API yield.

Results

Within the considered parameter space, varying only key initial starting material and feed reagent mass fractions within their allowed parameter ranges showed potential for + 0.2% yield improvement while varying all process parameters could allow + 0.4% yield improvement.

Conclusions

The general modelling framework to guide control strategies, highlight process improvements in silico, and reduce experimental burden can be applied to multiple pharmaceutical products across different manufacturing modalities and operating modes.

中文翻译：

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工业环境中活性药物成分连续制造系统模型的应用

目的

在药品制造中，了解和量化工艺条件如何影响产品质量对于保证工艺盈利能力和持续的产品产量至关重要。我们描述了一种用于高价值活性药物成分 (API) 的商业规模连续制造过程的集成系统模型，以及它用于优化工艺条件以在保证产品质量的情况下最大限度地提高产量。

方法

全局敏感性分析 (GSA) 用于评估不同工艺参数对 API 产量的影响，以指导选择用于产量优化的决策变量。然后，我们在批准的工艺参数范围内制定了不同的优化方案，以提出最佳工艺条件以提高 API 产量。

结果

在所考虑的参数空间内，仅在其允许的参数范围内改变关键的初始起始材料和进料试剂质量分数显示了 + 0.2% 的产量提高的潜力，而改变所有工艺参数可以允许 + 0.4% 的产量提高。

结论

用于指导控制策略、突出计算机中的工艺改进和减少实验负担的通用建模框架可以应用于不同制造模式和操作模式的多种药品。