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Integration of stochastic simulation with advanced multivariate and visualisation analyses for rapid prediction of facility fit issues in biopharmaceutical processes

机译:随机模拟与高级多元分析和可视化分析的集成,可快速预测生物制药过程中设施的适应性问题

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This paper describes a decision-support tool that integrates Monte Carlo simulation data derived using a stochastic discrete-event simulation model to mimic process fluctuations with advanced multivariate statistical techniques to help pinpoint the potential root causes of sub-optimal facility fit issues. Principal component analysis combined with clustering algorithms was used to analyse the complex datasets from complete industrial batch processes for biopharmaceuticals. The challenge of visualising the multidimensional nature of the dataset was addressed using hierarchical and K-means clustering as well as parallel co-ordinate plots to help identify process fingerprints and characteristics of clusters leading to sub-optimal facility fit issues. Industrially-relevant case studies are presented that focus on technology transfer challenges for therapeutic antibodies moving from early phase to late phase clinical trials. The case study details how sub-optimal facility fit can be alleviated by allocating alternative product pool tanks. The impact of this operational change is then assessed by reviewing an updated process fingerprint.
机译:本文介绍了一种决策支持工具,该工具集成了使用随机离散事件模拟模型得出的蒙特卡洛模拟数据,以先进的多元统计技术来模拟过程波动,以帮助查明次优设施拟合问题的潜在根本原因。主成分分析与聚类算法相结合,用于分析完整的生物制药工业批处理过程中的复杂数据集。使用分层聚类和K-均值聚类以及平行坐标图来解决可视化数据集多维性质的难题,以帮助识别过程指纹和聚类特征,从而导致次优设施拟合问题。提出了与行业相关的案例研究,这些案例研究侧重于治疗抗体从早期临床试验过渡到晚期临床试验的技术转移挑战。案例研究详细说明了如何通过分配备用产品池来缓解次优设施的问题。然后,通过检查更新的过程指纹来评估此操作更改的影响。

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