Integration of a perfusion reactor and continuous precipitation in an entirely membrane-based process for antibody capture.

Continuous precipitation coupled with continuous tangential flow filtration is a cost-effective alternative for the capture of recombinant antibodies from crude cell culture supernatant. The removal of surge tanks between unit operations, by the adoption of tubular reactors, maintains a continuous harvest and mass flow of product with the advantage of a narrow residence time distribution (RTD). We developed a continuous process implementing two orthogonal precipitation methods, CaCl2 precipitation for removal of host-cell DNA and polyethylene glycol (PEG) for capturing the recombinant antibody, with no influence on the glycosylation profile. Our lab-scale prototype consisting of two tubular reactors and two stages of tangential flow microfiltration was continuously operated for up to 8 days in a truly continuous fashion and without any product flow interruption, both as a stand-alone capture and as an integrated perfusion-capture. Furthermore, we explored the use of a negatively charged membrane adsorber for flow-through anion exchange as first polishing step. We obtained a product recovery of approximately 80% and constant product quality, with more than two logarithmic reduction values (LRVs) for both host-cell proteins and host-cell DNA by the combination of the precipitation-based capture and the first polishing step.

Model predictive control for steady-state performance in integrated continuous bioprocesses.

Perfusion bioreactors are commonly used for the continuous production of monoclonal antibodies (mAb). One potential benefit of continuous bioprocessing is the ability to operate under steady-state conditions for an extended process time. However, the process performance is often limited by the feedback control of feed, harvest, and bleed flow rates. If the future behavior of a bioprocess can be adequately described, predictive control can reduce set point deviations and thereby maximize process stability. In this study, we investigated the predictive control of biomass in a perfusion bioreactor integrated to a non-chromatographic capture step, in a series of Monte-Carlo simulations. A simple algorithm was developed to estimate the current and predict the future viable cell concentrations (VCC) of the bioprocess. This feature enabled the single prediction controller (SPC) to compensate for process variations that would normally be transported to adjacent units in integrated continuous bioprocesses (ICB). Use of this SPC strategy significantly reduced biomass, product concentration, and harvest flow variability and stabilized the operation over long periods of time compared to simulations using feedback control strategies. Additionally, we demonstrated the possibility of maximizing product yields simply by adjusting perfusion control strategies. This method could be used to prevent savings in total product losses of 4.5-10% over 30 days of protein production.

Antibody glycation during a Chinese hamster ovary fed-batch process is following a constrained second order reaction.

Glycation on lysine side chains of recombinant monoclonal antibodies (mAb) is a well-known phenomenon in manufacturing processes of biopharmaceuticals that potentially alter the efficacy of the therapeutic protein. In the present study, we report kinetic studies of glycation formation of the model protein Adalimumab, relative to glucose and non-glycated protein in six Chinese hamster ovary (CHO) fed batch cultivations. We developed an in vivo model from glycation kinetic studies that is capable of estimating the reaction rate constant in static and dynamic bioprocesses, respectively. As anticipated, pseudo first order reactions with respect to present glucose concentration or non-glycated mAb were not sufficient to describe the glycation formation during the bioprocesses. However, second order reactions did not reveal linear relationship of glycated mAb to the product of glucose and non-glycated mAb either, suggesting that a reconsideration of the kinetic equation was necessary. With the introduction of a constraint using only the newly formed product (mAbΔt ), the second-order reaction was successfully implemented. In addition, it is shown that the process knowledge derived from dynamic can be transferred to static experiments and vice versa. Hence, intensified design of experiments (iDoE) can be an applicable and useful tool in product quality studies in cell culture processes.

Oxygen Uptake Rate Soft-Sensing via Dynamic k L a Computation: Cell Volume and Metabolic Transition Prediction in Mammalian Bioprocesses.

In aerobic cell cultivation processes, dissolved oxygen is a key process parameter, and an optimal oxygen supply has to be ensured for proper process performance. To achieve optimal growth and/or product formation, the rate of oxygen transfer has to be in right balance with the consumption by cells. In this study, a 15 L mammalian cell culture bioreactor was characterized with respect to k L a under varying process conditions. The resulting dynamic k L a description combined with functions for the calculation of oxygen concentrations under prevailing process conditions led to an easy-to-apply model, that allows real-time calculation of the oxygen uptake rate (OUR) throughout the bioprocess without off-gas analyzers. Subsequently, the established OUR soft-sensor was applied in a series of 13 CHO fed-batch cultivations. The OUR was found to be directly associated with the amount of viable biomass in the system, and deploying of cell volumes instead of cell counts led to higher correlations. A two-segment linear model predicted the viable biomass in the system sufficiently. The segmented model was necessary due to a metabolic transition in which the specific consumption of oxygen changed. The aspartate to glutamate ratio was identified as an indicator of this metabolic shift. The detection of such transitions is enabled by a combination of the presented dynamic OUR method with another state-of-the-art viable biomass soft-sensor. In conclusion, this hyphenated technique is a robust and powerful tool for advanced bioprocess monitoring and control based exclusively on bioreactor characteristics.