Soft-Sensors: Efficient PAT Tools for Bioprocess Development Along QbD Principles

Patrick Sagmeister                 patrick.sagmeister@tuwien.ac.at

Patrick Wechselberger           patrick.wechselberger@tuwien.ac.at

Mohammadhadi Jazini            mohammadhadi.jazini@tuwien.ac.at

Andea Meitz                           andrea.meitz@tuwien.ac.at

Timo Langemann                   timo.langemann@tuwien.ac.at

Christoph Herwig                   christoph.herwig@tuwien.ac.at

The following abstract is an extension of the following contribution:

Sagmeister P, Wechselberger P, Jazini M, Meitz A, Langemann T, Herwig C. 2013. Soft sensor assisted dynamic bioprocess control: Efficient tools for bioprocess development. Chem. Eng. Sci. 96:190–198.

Introduction

Bioprocess development along Quality by Design (QbD) principles aims at the identification and understanding of interactions between process parameters and product quality and process performance attributes. Since bioprocess developing time is limited and the investigation and optimization of a bioprocess is a time, cost and labour intensive task, strategies are required to accelerate process development and reduce time-to market latency of biotechnological products.

Soft-Sensors are process analytical devices that grant access to important non-measured process variables by mathematical processing of readily available process data. While the potential of soft sensors for manufacturing is generally recognized and reflected by many excellent contributions, their potential for efficient bioprocess development and bioprocess design purposes has not been exploited so far.

The task of soft-sensing in process control and monitoring in process development differs conceptually from soft-sensor applications in manufacturing. In the latter case huge amounts of process data are typically available that can be used to construct first-principle based or statistical process models. In process development no or very little prior data on the strain or process is available and soft-sensing acts as a tool for the development of strain- and process knowledge. This conceptual difference discards artificial neural networks (ANNs) and multivariate soft-sensing approaches for the investigation of unexplored systems within process development. In contrast, elemental balancing approaches for bioprocesses (Jobé et al., 2003) are attractive for the design of a process development soft sensing strategy: no training data sets are required, simple measurable input parameters and variables are used, they can be checked for gross errors via reconciliation procedures (van der Heijden et al., 1994).

In this contribution we present what is to our knowledge the first soft-sensor based control strategy capable of effective (dynamic) control of specific substrate uptake rates that is independent of training data sets, elaborate strain specific information, fixed or known yield coefficients or offline measurements. Therefore, the first soft sensor control strategy that qualifies as process development tool is presented.

Materials and Methods

A soft-sensor on the basis of a redundant equation system involving the Degree of Reduction (DoR) and Carbon (C) balance was used for the estimation of the real-time biomass concentration and used for closed-loop control of specific uptake rates using a mass balance based control approach.

Results and Discussion

The power of the presented soft sensing approach for bioprocess development was demonstrated on microbial bioprocess development and bioprocess design tasks: (1) the control of the specific substrate uptake rates in a non-induced E. coli decellerostat culture aiming at the determination of the specific acetate production as a function of the specific substrate uptake, (2) the static control of the specific substrate uptake rate in an induced P. Pastoris AOX expression system, (3) the dynamic control of specific substrate uptake rates under induced conditions in a E. coli pET expression system and (4) the simultaneous dynamic control of the specific glucose and arabinose uptake rates in an E. colipBAD expression system.

(1)   Dynamic control of a decellerostat culture aiming at the investigation of specific acetate production as a function of the specific substrate uptake rate

Acetate as overflow metabolite in E. coli has reported negative effects on recombinant protein production as well as on RNA, lipid and protein synthesis and can therefore be considered critical for recombinant processes (Koh et al., 1992). In fed-batch processes, acetate formation can be avoided by limiting and controlling the specific substrate uptake rate through the fed-batch feeding profile. However, this demands the strain specific knowledge of the onset of acetate production, i.e., which is maximum specific substrate uptake rate q_swithout the formation of acetate.

The real-time biomass concentration was estimated via the biomass estimation device .The feed rate set point was calculated in real-time using the estimated biomass concentration, the estimated broth volume as well as the constant feed concentration (400 g/l) and feed density (1100 g/l). Offline samples were taken every 30 min for the determination of biomass dry cell weight as well as acetate and used for the calculation of the specific acetate production rate q_ace as well as the specific substrate uptake rate q_s. An offset between the q_s calculated from the offline biomass dry-cell weight determination and the q_s set-point was detected, converging in within to small q_s values. The calculation of q_s/q_ace from offline data followed by linear regression allows the extraction of the strain specific function q_acef(q_s). The q_s threshold for acetate production can be read from the interception of this function with the x-axis.

The bioprocess developer can obtain the information how long it takes for acetate (accumulated in the batch phase) to be consumed within the fed-batch at a specific substrate uptake rate q_s.

(2)   Static control of the specific substrate uptake rate in an induced P. pastoris AOX expression system

Because of the dependency of the productivity of horseradish peroxidase produced by the recombinant P. pastoris on substrate uptake rate, the control of a static qs set point is of great interest (Dietzsch et al., 2011). Here it is demonstrated, that the control of a static qsset point can be achieved without knowledge on known yield coefficients and the assumption of constant yield coefficients.

A batch phase and consequently a fed batch phase were conducted to achieve a high cell density culture ready for induction. Consequently, the presented control strategy was used for the control of the specific methanol uptake rate in the induction phase. The desired control of the specific substrate uptake rate was achieved without knowledge of the yield coefficient with a maximum deviation of 10%.

(3)    Dynamic control of the specific substrate uptake rate in an induced E. coli C41 (pBMPPET) expression system

The control of dynamically changing setpoints of the specific substrate uptake rate qs is considered of practical interest for bioprocess development, especially for the design of feeding strategies for recombinant processes (Dietzsch et al., 2011). However, dynamically changing yield coefficients are frequently observed in induced conditions, restricting the applicability of feed-forward control strategies as well as control strategies based on total carbon dioxide evolution or total oxygen consumption.

Here, the applicability of the presented soft-sensor control strategy for the dynamic control of the specific substrate uptake rate qs on an induced process with dynamically changing yield coefficients due to the effect of recombinant protein production is demonstrated.

(4)   Simultaneous dynamic control of the specific substrate and inducer uptake rates in an E. coli expression system aiming at the investigation of mixed feed metabolic capabilities

Aiming at the investigation of mixed feed capabilities of an E. coli C41 strain carrying the plasmid pBK-BMP, the culture was subjected to two independent C sources (glucose as main C-source, arabinose as second C-source and inducer for recombinant protein production) following the qscontrol strategy. The goal of the dynamic experiment was to investigate the capability of l-arabinose metabolization in a range of 0 to 0.3 g/g/h specific uptake of l-arabinose in the presence of an uptake of d-glucose of -0.2 g/g/h.

The qs glucose setpoint was kept constant at a level of −0.2 g/g/h and the qsarabinose was subjected to a linear ramp starting from -0.05 g/g/h to −0.3 g/g/h. No l-arabinose was found to accumulate, demonstrating that the culture is able to metabolize l-arabinose in a flux range from −0.05 g/g/h to −0.3 g/g/h in the presence of specific glucose uptake of −0.2 g/g/h. Obtained specific substrate uptake rates were calculated on the basis of biomass dry cell weight measurements. A deviation of 3 % was observed.

Conclusion

In this contribution the dynamic controllability of multiple specific substrate uptake rates using a first-principle soft sensor independent of known or dynamically changing yield coefficients in recombinant bioprocesses is demonstrated for the first time. The presented control strategy holds potential to become a key process analytical technology (PAT) tool for bioprocess development; hence a tool for efficient design, analyzing and control of manufacturing processes.

 

 

 

References

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