Patrick Sagmeister

Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify “biomass” in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with “state of the art” methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

Efficient feeding profile optimization for recombinant protein production using physiological information.

A multivariate study was performed aiming at the optimization of a recombinant rhamnose inducible E. coli induction system with alkaline phosphatase as target product. The effects of typical factors with impact on post- as well as pre-induction feeding rates were investigated with respect to the space–time yield of the target product. The goal was increased understanding as well as quantitative characterization of these factors with respect to their physiological impact on the model system. The optical density (OD) at which the culture was induced had a strong positive effect on the space–time yield. Pre-induction growth rate (k) had a second-order effect, while induction feed rate drop (J), a factor defining the linear post-induction feed rate, was interacting with (k). However, explanation of the observed effects to acquire more understanding regarding their effect on cell metabolism was not straight forward. Hence, the original process parameters were transformed into physiological more meaningful parameters and served as the basis for a multivariate data analysis. The observed variance with respect to observed volumetric activity was fully explained by the specific substrate uptake rate (qs) and induction OD, merging the process parameters pre-induction growth rate (k) and feed rate drop (J) into the physiological parameter specific substrate uptake rate (qs). After transformation of the response volumetric activity (U/ml) into the biomass specific activity (U/gbiomass), the observed variance was fully explained solely by the specific substrate uptake rate (qs). Due to physiological multivariate data analysis, the interpretation of the results was facilitated and factors were reduced. On the basis of the obtained results, it was concluded that the physiological parameter qs rather than process parameters (k, J, induction OD) should be used for process optimization with respect to the feeding profile.

Model-Based Analysis on the Extractability of Information from Data in Dynamic Fed-Batch Experiments

Dynamic changes of physiological bioprocess parameters, e.g. a change in the specific growth rate μ, are frequently observed during industrial manufacturing as well as bioprocess development. A quantitative description of these variations is of great interest, since it can bring elucidation to the physiological state of the culture. The goal of this contribution was to show limitations and issues for the calculation of rates with regard to temporal resolution for dynamic fed‐batch experiments. The impact of measurement errors, temporal resolution and the physiological activity on the signal to noise ratio (SNR) of the calculated rates was evaluated using an in‐silico approach. To make use of that in practice, a generally applicable rule of thumb equation for the estimation of the SNR of specific rates was presented. The SNR calculated by this rule of thumb equation helps with definition of sampling intervals and making a decision whether an observed change is statistically significant or should be attributed to random error. Furthermore, a generic reconciliation approach to remove random as well as systematic error from data was presented. This reconciliation technique requires only little prior knowledge. The validity of the proposed tools was checked with real data from a fed‐batch culture of E. coli with dynamic variations due to feed profile.

Information Processing: Rate-Based Investigation of Cell Physiological Changes along Design Space Development

Recent initiatives summarized under the term quality by design (QbD) urge for science and risk-based pharmaceutical bioprocess development strategies. One of the most accepted concepts communicated by the regulatory authorities is the concept of design space—a multidimensional combination of critical process parameter (CPP) ranges where the quality acceptance criteria (critical quality attributes, CQAs) are fulfilled. Current design space development along QbD principles focuses on the investigation of statistical CPP/CQA interactions, while the biological mechanistic of this interaction is hardly considered. Furthermore, the plethora of available online and offline data gathered within design space development is typically not used for the demonstration of process understanding. Here we present a methodology about how typical recorded process data can be processed and used to gather mechanistic process knowledge within upstream design space development, without the need for further experiments or additional analytical procedures. Data derived from online and offline measurements (off gas quantification, air flows, substrate flows, biomass dry cell weight measurements) were processed into scale-independent information in the form of specific rates and yield coefficients (data processing). Subsequently, the obtained information was regressed with the investigated process parameters aiming at the investigation of mechanistic interactions (information processing). The power of the presented approach was demonstrated on a multivariate study involving two process parameters (induction phase temperature and induction phase feeding strategy) aiming at the production of recombinant product in an Escherichia coli K12 strain. The knowledge successfully extracted indicated a time dependency of the metabolic load posed on the system, a possible down regulation of the promoter at reduced temperatures, and reduced cell lysis at higher specific feeding regimes. The presented data and information processing methodology for mechanistic process knowledge extraction is fully complementary to the task of design space development for QbD submissions and can serve as the basis of mechanistic modeling. LAY ABSTRACT: Manufacturing of pharmaceuticals intended for human use is under tight control of government authorities. To further improve product quality and allow more manufacturing flexibility, government agencies started to encourage manufactures to investigate and understand their manufacturing processes scientifically. This should lead to quality by design (QbD), hence a manufacturing that is so well understood that final product quality can be guaranteed by the manufacturing process itself.

Tunable recombinant protein expression in E. coli: enabler for continuous processing?

Tuning of transcription is a powerful process technological tool for efficient recombinant protein production in Escherichia coli. Many challenges such as product toxicity, formation of inclusion bodies, cell death, and metabolic burden are associated with non-suitable (too high or too low) levels of recombinant protein expression. Tunable expression systems allow adjusting the recombinant protein expression using process technological means. This enables to exploit the cell’s metabolic capacities to a maximum. Within this article, we review genetic and process technological aspects of tunable expression systems in E. coli, providing a roadmap for the industrial exploitation of the reviewed technologies. We attempt to differentiate the term “expression tuning” from its inflationary use by providing a concise definition and highlight interesting fields of application for this versatile new technology. Dependent on the type of inducer (metabolizable or non-metabolizable), different process strategies are required in order to achieve tuning. To fully profit from the benefits of tunable systems, an independent control of growth rate and expression rate is indispensable. Being able to tackle problems such as long-term culture stability and constant product quality expression tuning is a promising enabler for continuous processing in biopharmaceutical production.

Tunable recombinant protein expression with E. coli in a mixed-feed environment

Controlling the recombinant protein production rate in Escherichia coli is of utmost importance to ensure product quality and quantity. Up to now, only the genetic construct, introduced into E. coli, and the specific growth rate of the culture were used to influence and stir the productivity. However, bioprocess technological means to control or even tune the productivity of E. coli are scarce. Here, we present a novel method for the process-technological control over the recombinant protein expression rate in E. coli. A mixed-feed fed-batch bioprocess based on the araBAD promoter expression system using both d-glucose and l-arabinose as assimilable C-sources was designed. Using the model product green fluorescent protein, we show that the specific product formation rate can be efficiently tuned even on the cellular level only via the uptake rate of l-arabinose. This novel approach introduces an additional degree of freedom for the design of recombinant bioprocesses with E. coli. We anticipate that the presented method will result in significant quality and robustness improvement as well as cost and process time reduction for recombinant bacterial bioprocesses in the future.

Tunable recombinant protein expression in E. coli: promoter systems and genetic constraints

Tuning of transcription is a promising strategy to overcome challenges associated with a non-suitable expression rate like outgrowth of segregants, inclusion body formation, metabolic burden and inefficient translocation. By adjusting the expression rate—even on line—to purposeful levels higher product titres and more cost-efficient production processes can be achieved by enabling culture long-term stability and constant product quality. Some tunable systems are registered for patents or already commercially available. Within this contribution, we discuss the induction mechanisms of various Escherichia coli inherent promoter systems with respect to their tunability and review studies using these systems for expression tuning. According to the current level of knowledge, some promoter systems were successfully used for expression tuning, and in some cases, analytical evidence on single-cell level is still pending. However, only a few studies using tunable strains apply a suitable process control strategy. So far, expression tuning has only gathered little attention, but we anticipate that expression tuning harbours great potential for enabling and optimizing the production of a broad spectrum of products in E. coli.

An Integrated Downstream Process Development Strategy along QbD Principles

The development, optimization, and analysis of downstream processes are challenged by a high number of potentially critical process parameters that need to be investigated using lab-scale experiments. These process parameters are spread across multiple unit operations and potentially show interactions across unit operations. In this contribution, we present a novel strategy for bioprocess development that considers the risk of parameter interactions across unit operations for efficient experimental design. A novel risk assessment tool (interaction matrix) is introduced to the Quality by Design (QbD) workflow. Using this tool, the risk of interaction across unit operations is rated. Subsequently, a design of experiments (DoE) across unit operations is conducted that has the power to reveal multivariate interdependencies. The power of the presented strategy is demonstrated for protein isolation steps of an inclusion body process, focusing on the quality attribute inclusion body purity. The concentration of Triton X-100 in the course of inclusion body (IB) purification was shown to interact with the g-number of the subsequent centrifugation step. The presented strategy targets a holistic view on the process and allows handling of a high number of experimental parameters across unit operations using minimal experimental effort. It is generically applicable for process development along QbD principles.

Fed-Batch Production of Bacterial Ghosts Using Dielectric Spectroscopy for Dynamic Process Control

The Bacterial Ghost (BG) platform technology evolved from a microbiological expression system incorporating the ϕX174 lysis gene E. E-lysis generates empty but structurally intact cell envelopes (BGs) from Gram-negative bacteria which have been suggested as candidate vaccines, immunotherapeutic agents or drug delivery vehicles. E-lysis is a highly dynamic and complex biological process that puts exceptional demands towards process understanding and control. The development of a both economic and robust fed-batch production process for BGs required a toolset capable of dealing with rapidly changing concentrations of viable biomass during the E-lysis phase. This challenge was addressed using a transfer function combining dielectric spectroscopy and soft-sensor based biomass estimation for monitoring the rapid decline of viable biomass during the E-lysis phase. The transfer function was implemented to a feed-controller, which followed the permittivity signal closely and was capable of maintaining a constant specific substrate uptake rate during lysis phase. With the described toolset, we were able to increase the yield of BG production processes by a factor of 8–10 when compared to currently used batch procedures reaching lysis efficiencies >98%. This provides elevated potentials for commercial application of the Bacterial Ghost platform technology.

Simple monitoring of cell leakiness and viability in Escherichia coli bioprocesses-A case study

In a recently published study, we developed a simple methodology to monitor Escherichia coli cell integrity and lysis during bioreactor cultivations, where we intentionally triggered leakiness. In this follow‐up study, we used this methodology, comprising the measurement of extracellular alkaline phosphatase to monitor leakiness and flow cytometry to follow viability, to investigate the effect of process parameters on a recombinant E. coli strain producing the highly valuable vascular endothelial growth factor A165 (VEGF‐A165) in the periplasm. Since the amount of soluble product was very little (<500 μg/g dry cell weight), we directly linked the effect of the three process parameters temperature, specific uptake rate of the inducer arabinose and specific growth rate (μ) to cell integrity and viability. We found that a low temperature and a high μ were beneficial for cell integrity and that an elevated temperature resulted in reduced viability. We concluded that the recombinant E. coli cells producing VEGF‐A165 in the periplasm should be cultivated at low temperature and high μ to reduce leakiness and guarantee high viability. Summarizing, in this follow‐up study we demonstrate the usefulness of our simple methodology to monitor leakiness and viability of recombinant E. coli cells during bioreactor cultivations.