Accelerated bioprocess characterization by data enrichment in scale-down models

Scale-down models are often used for the definition of operating ranges in process development and validation and therefore data exploitation is an important task. Usually, the experimental scientists have very tight timelines and consequentially may often only perform the required routine of cleaning and sorting the data without more sophisticated analyses, even though it might improve data quality. This problem is more exacerbated by the rise of fully automated, miniaturized high-throughput equipment in up- and downstream process development where data processing automation is not just optional but a must. However, cleaned, sorted and time-aligned data alone do not guarantee a sufficient representation of the process. Often, further data enrichment to extract relevant process parameters is required but omitted due to time constraints. The benefit of data enrichment is true process understanding [1]: the calculation of scalable rates and yields, minima, maxima, median or derivatives of measured online or offline signals and the categorization into clone, lot, feeding strategy, seed train fitness, feed or media can be easily used to explain variation in the current process. Multivariate regression methods such as partial least squares regression (PLS-R) or hybrid models [2] can then be used to explain the contribution and ranking of critical process parameters (CPPs) such as pH, pO2, temperature, metabolite concentrations or metabolic rates towards particular critical quality attributes (CQAs), for instance glycoform distribution, other product quality attributes or cell growth. The knowledge of these parameters can then feed directly into the generation of a statistically verified design space which may be then used for process scale-up and validation [3,4]. Summarizing this contribution, we present a methodology to automate data enrichment. Our suggested data enrichment concept is exemplified shown on upstream micro bioreactor data and comprise one of the necessary steps to characterize and ultimately qualify scale-down process models. This project has received funding from the European Union‘s Horizon2020 research and innovation programme under the Marie Curie Skłodowska-Curie grant agreement No 643056

Robust and predictable mammalian cell culture bioprocesses

Within this thesis we implemented online process monitoring and control in our labs using PAT (Process Analytical Technology) tools and scale-free control variables in mammalian CHO (Chinese Hamster Ovary) cell culture processes to improve productivity, robustness and predictability at different scales. Predictive models for cell physiology and sensor signals were established based on historical data analysis of industrial process development data at lab (2L) and pilot plant scale (80L). The developed models proved to be scale-independent and transferable to other CHO clones, which allowed their application on different processes with limited prior information. Optimization of feeding in fed-batch mode was achieved by controlling the specific glucose consumption rate within a narrow range in real time using PAT tools, such as an online metabolic analyser and a capacitance probe for monitoring and control purposes. This led to very stable glucose, lactic acid and pH profiles, improving productivity and robustness of the platform process with scale-free parameters. Mechanistic, statistical and in-silico models under dynamic fed-batch conditions were used to gain novel insights into cell metabolism, and allowed a predictive run forecast at 2L and 12000L scale. The established methodologies facilitate and improve process transfer and scale-up of industrial mAb (monoclonal Antibody) platform processes through advanced process monitoring and control. This is in line with recommendations from the FDA (Food and Drugs Administration) and EMA (European Medicines Agency) to implement PAT & QbD (Quality by Design) approaches in the biopharmaceutical industry to ensure consistent quality of medicines for the safety of patients.15

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

Biomass and cell-specific metabolic rates usually change dynamically over time, making the “feed according to need” strategy difficult to realize in a commercial fed-batch process. We here demonstrate a novel feeding strategy which is designed to hold a particular metabolic state in a fed-batch process by adaptive feeding in real time. The feed rate is calculated with a transferable biomass model based on capacitance, which changes the nutrient flow stoichiometrically in real time. A limited glucose environment was used to confine the cell in a particular metabolic state. In order to cope with uncertainty, two strategies were tested to change the adaptive feed rate and prevent starvation while in limitation: (i) inline pH and online glucose concentration measurement or (ii) inline pH alone, which was shown to be sufficient for the problem statement. In this contribution, we achieved metabolic control within a defined target range. The direct benefit was two-fold: the lactic acid profile was improved and pH could be kept stable. Multivariate Data Analysis (MVDA) has shown that pH influenced lactic acid production or consumption in historical data sets. We demonstrate that a low pH (around 6.8) is not required for our strategy, as glucose availability is already limiting the flux. On the contrary, we boosted glycolytic flux in glucose limitation by setting the pH to 7.4. This new approach led to a yield of lactic acid/glucose (Y L/G) around zero for the whole process time and high titers in our labs. We hypothesize that a higher carbon flux, resulting from a higher pH, may lead to more cells which produce more product. The relevance of this work aims at feeding mammalian cell cultures safely in limitation with a desired metabolic flux range. This resulted in extremely stable, low glucose levels, very robust pH profiles without acid/base interventions and a metabolic state in which lactic acid was consumed instead of being produced from day 1. With this contribution, we wish to extend the basic repertoire of available process control strategies, which will open up new avenues in automation technology and radically improve process robustness in both process development and manufacturing

Universal Capacitance Model for Real-Time Biomass in Cell Culture

Capacitance probes have the potential to revolutionize bioprocess control due to their safe and robust use and ability to detect even the smallest capacitors in the form of biological cells. Several techniques have evolved to model biomass statistically, however, there are problems with model transfer between cell lines and process conditions. Errors of transferred models in the declining phase of the culture range for linear models around +100% or worse, causing unnecessary delays with test runs during bioprocess development. The goal of this work was to develop one single universal model which can be adapted by considering a potentially mechanistic factor to estimate biomass in yet untested clones and scales. The novelty of this work is a methodology to select sensitive frequencies to build a statistical model which can be shared among fermentations with an error between 9% and 38% (mean error around 20%) for the whole process, including the declining phase. A simple linear factor was found to be responsible for the transferability of biomass models between cell lines, indicating a link to their phenotype or physiology