Paul Kroll

Quantification of cell lysis during CHO bioprocesses: Impact on cell count, growth kinetics and productivity.

High cell densities and high viability are critical quality attributes for mammalian bioprocesses. Determination of living and dead cell numbers is nowadays routinely performed by automated image-based cell analyzers or flow cytometry. However, complete lysis of cells is usually neglected by these devices. We present a novel method for robust quantification of lysed cell populations over the course of a CHO bioprocess. The release of lactate dehydrogenase (LDH) and double stranded genomic DNA in culture supernatants were used as markers for cell lysis. We considered the degradation of both markers over cultivation time, which significantly increased the amount of released LDH and DNA. For correct and robust estimation of lysed cell fractions, degradation of both markers over cultivation time was considered, where redundancy of markers allowed data reconciliation. Calculating the number of cells which were subject to complete cell lysis, we could show that this fraction makes up as much as 30% of the total produced biomass and is not described by measurements of image-based analyzers. Finally, we demonstrate that disregarding cell lysis heavily affects the calculation of biomass yields and growth rates and that increasing levels of cell lysis are related to decreased productivity.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes.

Understanding process parameter interactions and their effects on mammalian cell cultivations is an essential requirement for robust process scale-up. Furthermore, knowledge of the relationship between the process parameters and the product critical quality attributes (CQAs) is necessary to satisfy quality by design guidelines. So far, mainly the effect of single parameters on CQAs was investigated. Here, we present a comprehensive study to investigate the interactions of scale-up relevant parameters as pH, pO2 and pCO2 on CHO cell physiology, process performance and CQAs, which was based on design of experiments and extended product quality analytics. The study used a novel control strategy in which process parameters were decoupled from each other, and thus allowed their individual control at defined set points. Besides having identified the impact of single parameters on process performance and product quality, further significant interaction effects of process parameters on specific cell growth, specific productivity and amino acid metabolism could be derived using this method. Concerning single parameter effects, several monoclonal antibody (mAb) charge variants were affected by process pCO2 and pH. N-glycosylation analysis showed positive correlations between mAb sialylation and high pH values as well as a relationship between high mannose variants and process pH. This study additionally revealed several interaction effects as process pH and pCO2 interactions on mAb charge variants and N-glycosylation pattern. Hence, through our process control strategy and multivariate investigation, novel significant process parameter interactions and single effects were identified which have to be taken into account especially for process scale-up.

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Impact of cell lysis on the description of cell growth and death in cell culture

The primary task of process development is a process design that guarantees product quality and maximizes product quantity. One part of the process development is the identification of critical process parameters. Especially in cell culture processes, unwanted cell damage as critical process parameter is still challenging in stirred tank reactors and needs therefore to be considered. Nevertheless, this topic and its effects on process performance are currently not well discussed and not verified in literature until now.

Soft sensor for monitoring biomass subpopulations in mammalian cell culture processes

ObjectivesBiomass subpopulations in mammalian cell culture processes cause impurities and influence productivity, which requires this critical process parameter to be monitored in real-time.ResultsFor this reason, a novel soft sensor concept for estimating viable, dead and lysed cell concentration was developed, based on the robust and cheap in situ measurements of permittivity and turbidity in combination with a simple model. It could be shown that the turbidity measurements contain information about all investigated biomass subpopulations. The novelty of the developed soft sensor is the real-time estimation of lysed cell concentration, which is directly correlated to process-related impurities such as DNA and host cell protein in the supernatant. Based on data generated by two fed-batch processes the developed soft sensor is described and discussed.ConclusionsThe presented soft sensor concept provides a tool for viable, dead and lysed cell concentration estimation in real-time with adequate accuracy and enables further applications with respect to process optimization and control.

Increased poly-β-hydroxybutyrate production from carbon dioxide in randomly mutated cells of cyanobacterial strain Synechocystis sp. PCC 6714: Mutant generation and characterization

Photosynthetic Poly-β-hydroxybutyrate (PHB) productivity in cyanobacteria needs to be increased to make cyanobacterial derived bioplastics economically feasible and competitive with petroleum-based plastics. In this study, high PHB yielding mutants of Synechocystis sp. PCC 6714 have been generated by random mutagenesis, using UV light as a mutagen. The selection of strains was based on PHB content induced by nitrogen and phosphorus starvation. The fast growing mutant MT_a24 exhibited more than 2.5-fold higher PHB productivity than that of the wild-type, attaining values of 37 ± 4% dry cell weight PHB. The MT_a24 was characterized for phenotypes, CO2 uptake rate and gene expression levels using quantitative PCR. Genome sequencing showed that UV mutagenesis treatment resulted in a point mutation in the ABC-transport complex, phosphate-specific transport system integral membrane protein A (PstA). The MT_a24 shows potential for industrial production of PHB and also for carbon capture from the atmosphere or point sources.

Media photo-degradation in pharmaceutical biotechnology - impact of ambient light on media quality, cell physiology, and IgG production in CHO cultures: Impact of media photo-degradation on media quality, cell physiology and IgG production in CHO cultures

Abstract BACKGROUND Many vital components in bioprocess media are prone to photo‐conversion or photo‐degradation upon exposure to ambient light, with severe negative consequences for biomass yield and overall productivity. However, there is only limited awareness of light irradiation as a potential risk factor when working in transparent glass bioreactors, storage vessels or disposable bag systems. The chemical complexity of most media renders a root‐cause analysis difficult. This study investigated in a novel, holistic approach how light‐induced changes in media composition relate to alterations in radical burden, cell physiology, morphology, and product formation in industrial Chinese hamster ovary (CHO) bioprocesses. RESULTS Two media formulations from proprietary and commercial sources were tested in a pre‐hoc light exposure scenario prior to cultivation. Using fluorescence excitation/emission (EEM) matrix spectroscopy, a photo‐sensitization of riboflavin was identified as a likely cause for drastically decreased IgG titers (up to −80%) and specific growth rates (−50% to −90%). Up to three‐fold higher radical levels were observed in photo‐degraded medium. On the biological side, this resulted in significant changes in cell morphology and aberrations in the normal IgG biosynthesis/secretion pathway. CONCLUSION These findings clearly illustrate the underrated impact of room light after only short periods of exposure, occurring accidentally or knowingly during bioprocess development and scale‐ up. The detrimental effects, which may share a common mechanistic cause at the molecular level, correlate well with changes in spectroscopic properties. This offers new perspectives for online monitoring concepts, and improved detectability of such effects in future.

Workflow for target-oriented parametrization of an enhanced mechanistic cell culture model†

The goal of this study is to develop a macroscopic mechanistic model describing growth and production within fed-batch cultivations of CHO cells. The model should be used for process characterization as well as for process monitoring including real-time parameter adaptations. The model proved to be able to describe a data-set of 40 processes differing in clones, scales, and process conditions with a normalized root mean square error of approximately 10%. However, due to limited parameter identifiability and limited knowledge about physiologically meaningful parameter values, a broad range of parameters could describe the data with similar quality. This hampered comparison of the model parameters as well as their real-time estimation. Therefore an iterative workflow combining techniques like sensitivity and identifiability analysis, analysis of the specific rates as well as structural adaptations of the parameter space is developed. By applying it the parameter variability could be reduced by 80% with similar predictive power as the original parameters. Summing up, based on a mechanistic CHO model, a generic and transferrable workflow is created for target-oriented parameter estimation in case of limited parameter identifiability. Finally, we suggest a methodology, which fits ideally into the frame of Process Analytical Technology aiming to increase process understanding.