Designing a Strategy for pH Control to Improve CHO Cell Productivity in Bioreactor

Abstract

Background: Drastic pH drop is a common consequence of scaling up a mammalian cell culture process, where it may affect the final performance of cell culture. Although CO₂ sparging and base addition are used as common approaches for pH control, these strategies are not necessarily successful in large scale bioreactors due to their effect on osmolality and cell viability. Accordingly, a series of experiments were conducted using an IgG1 producing Chinese Hamster Ovary (CHO-S) cell culture in 30 L bioreactor to assess the efficiency of an alternative strategy in controlling culture pH.

Methods: Factors inducing partial pressure of CO₂ and lactate accumulation (as the main factors altering culture pH) were assessed by Plackett-Burman design to identify the significant ones. As culture pH directly influences process productivity, protein titer was measured as the response variable. Subsequently, Central Composite Design (CCD) was employed to obtain a model for product titer prediction as a function of individual and interaction effects of significant variables.

Results: The results indicated that the major factor affecting pH is non-efficient CO₂ removal. CO₂ accumulation was found to be affected by an interaction between agitation speed and overlay air flow rate. Accordingly, after increasing the agitation speed and headspace aeration, the culture pH was successfully maintained in the range of 6.95-7.1, resulting in 51% increase in final product titer. Similar results were obtained during 250 L scale bioreactor culture, indicating the scalability of the approach.

Conclusion: The obtained results showed that pH fluctuations could be effectively controlled by optimizing CO₂ stripping.

Keywords: Carbon dioxide; Cell survival; Hydrogen-ion concentration (pH); Immunoglobulin G; Lactic acid.

Figure 1.

Pareto chart of standardized effect. The chart demonstrates agitation speed and overlay flow rate are the variables with significant effects on the response variable (mAb titer). The length of each bar corresponds to the standardized effect or interaction and the vertical line indicates the significant effect at a confidence interval of 95%.

Figure 2.

A) Three-dimensional and B) Contour graphs showing the effect of agitation speed (RPM) and overlay flow rate (LPM) on mAb production (mg/l). The plots showed that increasing the agitation speed to ∼150 RPM and overlay flow rate to ∼10 LPM resulted in optimal level of response (Protein titer).

Figure 3.

Influence of selected parameters on lactate and CO₂ production in CCD experimental design. Lactate and CO₂ concentrations during 13 runs of CCD experiments indicated that lactate accumulation is not different between cultures. Therefore, agitation speed and overlay flow rate control pH by changing pCO2 in the culture.

Figure 4.

Prediction values of variables and final mAb titer using response optimizer module of Minitab. Agitation speed of 144.78 RPM and overlay flow rate of 10.5 LPM are predicted as optimal values for reaching the maximum mAb titer of 1857.5 mg/l.

Figure 5.

Culture performance in 30 L and 250 L bioreactors. A) MVCC (10⁶ cells/ml), B) viability (%), (c) titer (mg/L), D) offline pH, E) pCO₂ (mmHg), and F) lactate specific production (qLac) (pg/(cell×h)) during 15 days of culture. Bars represent standard deviation. The scalability of the predicted values are shown in the similar patterns between 30 L and 250 L scale bioreactors (n=3; mean±SD).