Yang Yang, Farid Suzanne S, Thornhill Nina F
Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK.
J Biotechnol. 2014 Jun 10;179:17-25. doi: 10.1016/j.jbiotec.2014.03.004. Epub 2014 Mar 15.
Higher titre processes can pose facility fit challenges in legacy biopharmaceutical purification suites with capacities originally matched to lower titre processes. Bottlenecks caused by mismatches in equipment sizes, combined with process fluctuations upon scale-up, can result in discarding expensive product. This paper describes a data mining decisional tool for rapid prediction of facility fit issues and debottlenecking of biomanufacturing facilities exposed to batch-to-batch variability and higher titres. The predictive tool comprised advanced multivariate analysis techniques to interrogate Monte Carlo stochastic simulation datasets that mimicked batch fluctuations in cell culture titres, step yields and chromatography eluate volumes. A decision tree classification method, CART (classification and regression tree) was introduced to explore the impact of these process fluctuations on product mass loss and reveal the root causes of bottlenecks. The resulting pictorial decision tree determined a series of if-then rules for the critical combinations of factors that lead to different mass loss levels. Three different debottlenecking strategies were investigated involving changes to equipment sizes, using higher capacity chromatography resins and elution buffer optimisation. The analysis compared the impact of each strategy on mass output, direct cost of goods per gram and processing time, as well as consideration of extra capital investment and space requirements.
更高滴度的生产工艺可能会给传统生物制药纯化车间带来设施适配方面的挑战，这些车间的产能最初是与较低滴度的生产工艺相匹配的。设备尺寸不匹配导致的瓶颈，再加上放大过程中的工艺波动，可能会导致昂贵产品的废弃。本文描述了一种数据挖掘决策工具，用于快速预测设施适配问题，并消除暴露于批次间变异性和更高滴度的生物制造设施的瓶颈。该预测工具包括先进的多变量分析技术，用于分析蒙特卡罗随机模拟数据集，这些数据集模拟了细胞培养滴度、步骤产率和色谱洗脱体积中的批次波动。引入了一种决策树分类方法CART（分类与回归树）来探究这些工艺波动对产品质量损失的影响，并揭示瓶颈的根本原因。由此产生的图形化决策树确定了一系列导致不同质量损失水平的因素关键组合的“如果-那么”规则。研究了三种不同的消除瓶颈策略，包括改变设备尺寸、使用更高容量的色谱树脂和优化洗脱缓冲液。该分析比较了每种策略对质量产出、每克产品的直接生产成本和处理时间的影响，同时考虑了额外的资本投资和空间需求。
