Application of multivariate analysis and mass transfer principles for refinement of a 3-L bioreactor scale-down model-when shake flasks mimic 15,000-L bioreactors better

Abstract

Characterization of manufacturing processes is key to understanding the effects of process parameters on process performance and product quality. These studies are generally conducted using small-scale model systems. Because of the importance of the results derived from these studies, the small-scale model should be predictive of large scale. Typically, small-scale bioreactors, which are considered superior to shake flasks in simulating large-scale bioreactors, are used as the scale-down models for characterizing mammalian cell culture processes. In this article, we describe a case study where a cell culture unit operation in bioreactors using one-sided pH control and their satellites (small-scale runs conducted using the same post-inoculation cultures and nutrient feeds) in 3-L bioreactors and shake flasks indicated that shake flasks mimicked the large-scale performance better than 3-L bioreactors. We detail here how multivariate analysis was used to make the pertinent assessment and to generate the hypothesis for refining the existing 3-L scale-down model. Relevant statistical techniques such as principal component analysis, partial least square, orthogonal partial least square, and discriminant analysis were used to identify the outliers and to determine the discriminatory variables responsible for performance differences at different scales. The resulting analysis, in combination with mass transfer principles, led to the hypothesis that observed similarities between 15,000-L and shake flask runs, and differences between 15,000-L and 3-L runs, were due to pCO2 and pH values. This hypothesis was confirmed by changing the aeration strategy at 3-L scale. By reducing the initial sparge rate in 3-L bioreactor, process performance and product quality data moved closer to that of large scale.

Reduced scale model qualification of 5‐L and 250‐ml bioreactors using multivariant visualization and Bayesian inferential methods

“…This elucidation should be combined with a fundamental understanding of cell metabolism and typical degradation pathways. For example, is pCO 2 correlated to any changes in the oligosaccharide profiles (Ahuja et al, 2015)? If not, it may not matter to the product quality of the molecule.…”

“…Using PLS‐DA, it is possible to see graphically which of the input parameters and PPAs are most responsible for the differences in QAs noticed among scales. Ahuja, Jain, and Ram (2015) compared PCA, PCA‐DA, PLS, PLS‐DA, and orthogonal partial least squares (OPLS or OPLS‐DA) analyses to identify outliers (PCA) and differentiate between runs of bioreactors at different scales. They used the PLS or OPLS models to identify variables which seemed to differentiate performance across scales.…”

Microfluidic tools toward industrial biotechnology

“…Microbioreactors as scale down models could preview not only the cultivation condition, but also indicate points which should be adjusted for the scale up process. In addition, statistics tools as multivariate analysis (MVA) have been employed to study the difference between scales, and could also be applied in microbioreactors for optimization of industrial biotechnolgy. The works of Tsang et al (2013) and Tescione et al (2015) show the interesting approach of this statistical method to identify gaps between scales since the difference of geometries also leads to a variation on processes performance.…”

Real‐time dissolved carbon dioxide monitoring I: Application of a novel in situ sensor for CO2 monitoring and control

“…Hence, dCO 2 concentrations must be analyzed carefully and thoroughly during scale‐up and technology transfer activities (Matsunaga, Kano, Maki, & Dobashi, ; Mitchell‐Logean & Murhammer, ; Mostafa & Gu, ). The real‐time dCO 2 values will be useful not only for optimizing but also for reproducing culture conditions independent of scale (Ahuja, Shilpa Jain, & Ram, ). Thus, the careful monitoring and control of DO and dCO 2 are needed in assessing the culture conditions (Ge, Kostov, & Rao, ; Gupta & Rao, ).…”