Biologics Process Research and Development, Merck & Co., Inc., Kenilworth, New Jersey, USA.
Modeling & Informatics, Discovery Chemistry, Merck & Co., Inc., Rahway, New Jersey, USA.
Biotechnol Bioeng. 2024 Sep;121(9):2924-2935. doi: 10.1002/bit.28765. Epub 2024 Jun 4.
Advances in upstream production of biologics-particularly intensified fed-batch processes beyond 10% cell solids-have severely strained harvest operations, especially depth filtration. Bioreactors containing high amounts of cell debris (more than 40% particles <10 µm in diameter) are increasingly common and drive the need for more robust depth filtration processes, while accelerated timelines emphasize the need for predictive tools to accelerate development. Both needs are constrained by the current limited mechanistic understanding of the harvest filter-feedstream system. Historically, process development relied on screening scale-down depth filter devices and conditions to define throughput before fouling, indicated by increasing differential pressure and/or particle breakthrough (measured via turbidity). This approach is straightforward, but resource-intensive, and its results are inherently limited by the variability of the feedstream. Semi-empirical models have been developed from first principles to describe various mechanisms of filter fouling, that is, pore constriction, pore blocking, and/or surface deposit. Fitting these models to experimental data can assist in identifying the dominant fouling mechanism. Still, this approach sees limited application to guide process development, as it is descriptive, not predictive. To address this gap, we developed a hybrid modeling approach. Leveraging historical bench scale filtration process data, we built a partial least squares regression model to predict particle breakthrough from filter and feedstream attributes, and leveraged the model to demonstrate prediction of filter performance a priori. The fouling models are used to interpret and provide physical meaning to these computational models. This hybrid approach-combining the mechanistic insights of fouling models and the predictive capability of computational models-was used to establish a robust platform strategy for depth filtration of Chinese hamster ovary cell cultures. As new data continues to teach the computational models, in silico tools will become an essential part of harvest process development by enabling prospective experimental design, reducing total experimental load, and accelerating development under strict timelines.
生物制品上游生产的进展——尤其是细胞固含量超过 10%的强化补料分批工艺——对收获操作造成了严重压力，尤其是深层过滤。含有大量细胞碎片（超过 40%粒径小于 10μm 的颗粒）的生物反应器越来越常见，这推动了对更强大的深层过滤工艺的需求，而加速的时间线则强调了需要预测工具来加速开发。这两个需求都受到当前对收获过滤器进料系统的有限机械理解的限制。从历史上看，工艺开发依赖于筛选缩小规模的深层过滤设备和条件，以在堵塞（通过压差和/或颗粒突破增加来指示）之前定义吞吐量。这种方法简单直接，但资源密集，并且其结果固有地受到进料流的可变性的限制。已经从第一原理开发了半经验模型来描述各种过滤堵塞机制，即孔收缩、孔堵塞和/或表面沉积。将这些模型拟合到实验数据可以帮助确定主要的堵塞机制。尽管如此，这种方法在指导工艺开发方面的应用有限，因为它是描述性的，而不是预测性的。为了解决这一差距，我们开发了一种混合建模方法。利用历史上的实验室规模过滤工艺数据，我们构建了一个偏最小二乘回归模型，用于根据过滤器和进料流属性预测颗粒突破，并且利用该模型来预先演示过滤器性能的预测。该污垢模型用于解释和为这些计算模型提供物理意义。这种混合方法——结合了污垢模型的机械洞察力和计算模型的预测能力——用于为中国仓鼠卵巢细胞培养物的深层过滤建立稳健的平台策略。随着新数据不断教授计算模型，计算工具将通过支持前瞻性实验设计、减少总实验负荷以及在严格的时间线内加速开发，成为收获工艺开发的重要组成部分。
