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Hybrid Modelling and Multi-Parametric Control of Bioprocesses

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A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 June 2017) | Viewed by 84798

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Prof. Dr. Christoph Herwig

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Lisalis GmbH, Jenbachgasse 73-2, 1130 Vienna, Austria
Interests: biopharmaceutical process development
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

The goal of bioprocessing is to optimize process variables, such as product quantity and quality, in a reproducible, scalable, and transferable manner. However, bioprocesses are highly complex. A large number of process parameters and raw material attributes exist, which are highly interactive, and may vary from batch to batch. Those interactions need to be understood, and the source of variance must be identified and controlled.

While purely data-driven correlations, such as chemometric models of spectroscopic data, may be employed for the understanding how process parameters are related to process variables, they can hardly be deployed outside of the calibration space. Currently, mechanistic models, models based on mechanistic links and first principles, are in the focus of development. They are perceived to allow transferability and scalability, because mechanistics can be extrapolated. Moreover, the models deliver a large range of hardly-measureable states and physiological parameters.

For implementation of mechanistic models, however, models need to be simplified and linked to process parameters for real time execution. For this, hybrid models, and hence links between Data Driven and Mechanistic Models, may be a helpful solution.

Moreover, models need to be deployed in the control context: Bioprocesses need to be controlled on the one hand on different parameters simultaneously (e.g., constant precursor concentration and specific growth rate) and, on the other hand, may have different objective functions (maximum productivity and correct product quality). Hence, novel solutions and case studies for multiple input and output controls need to be developed, as they already exist in other market segments.

The current Special Issue wants to display current solutions and case studies of development and deployment of hybrid models and multi-parametric control of bioprocesses. Pure data driven solutions are discouraged. The Special Issue is open for any kind of bioprocess. Therefore, we seek contributions that address, for example:

Hybrid model solutions, combinations of data driven and mechanistic models.
Workflows how mechanistic models can be developed from data driven approaches and vice versa.
Discussion between explorative (DoE-based) and model based experimental design
Implementation of hybrid models in the real-time context
Models as PAT tool: Demonstrations of cases in which models were a solution to measure less
Observer solutions for real-time parameter optimization
Multiparametric control and event prediction
Life Cycle management of models
Knowledge management using hybrid models

Prof. Dr. Christoph Herwig
Guest Editor

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Keywords

mechanistic and hybrid models
real-time implementation
multi-parametric control of bioprocesses
model base experimental design
life cycle management of and knowledge management by models

Published Papers (10 papers)

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Research

1685 KiB

Open AccessArticle

Impact of Glycerol as Carbon Source onto Specific Sugar and Inducer Uptake Rates and Inclusion Body Productivity in E. coli BL21(DE3)

by Julian Kopp, Christoph Slouka, Sophia Ulonska, Julian Kager, Jens Fricke, Oliver Spadiut and Christoph Herwig

Bioengineering 2018, 5(1), 1; https://doi.org/10.3390/bioengineering5010001 - 21 Dec 2017

Cited by 49 | Viewed by 11000

Abstract

The Gram-negative bacterium E. coli is the host of choice for a multitude of used recombinant proteins. Generally, cultivation is easy, media are cheap, and a high product titer can be obtained. However, harsh induction procedures using isopropyl β-d-1 thiogalactopyranoside as inducer are often referred to cause stress reactions, leading to a phenomenon known as “metabolic” or “product burden”. These high expressions of recombinant proteins mainly result in decreased growth rates and cell lysis at elevated induction times. Therefore, approaches tend to use “soft” or “tunable” induction with lactose and reduce the stress level of the production host. The usage of glucose as energy source in combination with lactose as induction reagent causes catabolite repression effects on lactose uptake kinetics and as a consequence reduced product titer. Glycerol—as an alternative carbon source—is already known to have positive impact on product formation when coupled with glucose and lactose in auto-induction systems, and has been referred to show no signs of repression when cultivated with lactose concomitantly. In recent research activities, the impact of different products on the lactose uptake using glucose as carbon source was highlighted, and a mechanistic model for glucose-lactose induction systems showed correlations between specific substrate uptake rate for glucose or glycerol (q_s,C) and the maximum specific lactose uptake rate (q_s,lac,max). In this study, we investigated the mechanistic of glycerol uptake when using the inducer lactose. We were able to show that a product-producing strain has significantly higher inducer uptake rates when being compared to a non-producer strain. Additionally, it was shown that glycerol has beneficial effects on viability of cells and on productivity of the recombinant protein compared to glucose. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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2217 KiB

Open AccessArticle

Integrated Process Modeling—A Process Validation Life Cycle Companion

by Thomas Zahel, Stefan Hauer, Eric M. Mueller, Patrick Murphy, Sandra Abad, Elena Vasilieva, Daniel Maurer, Cécile Brocard, Daniela Reinisch, Patrick Sagmeister and Christoph Herwig

Bioengineering 2017, 4(4), 86; https://doi.org/10.3390/bioengineering4040086 - 17 Oct 2017

Cited by 21 | Viewed by 10803

Abstract

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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1491 KiB

Open AccessArticle

Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1

by Thomas Zahel, Lukas Marschall, Sandra Abad, Elena Vasilieva, Daniel Maurer, Eric M. Mueller, Patrick Murphy, Thomas Natschläger, Cécile Brocard, Daniela Reinisch, Patrick Sagmeister and Christoph Herwig

Bioengineering 2017, 4(4), 85; https://doi.org/10.3390/bioengineering4040085 - 12 Oct 2017

Cited by 9 | Viewed by 9027

Abstract

Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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1882 KiB

Open AccessArticle

Estimating Extrinsic Dyes for Fluorometric Online Monitoring of Antibody Aggregation in CHO Fed-Batch Cultivations

by Karen Schwab and Friedemann Hesse

Bioengineering 2017, 4(3), 65; https://doi.org/10.3390/bioengineering4030065 - 24 Jul 2017

Cited by 2 | Viewed by 6044

Abstract

Multi-wavelength fluorescence spectroscopy was evaluated in this work as tool for real-time monitoring of antibody aggregation in CHO fed-batch cultivations via partial least square (PLS) modeling. Therefore, we used the extrinsic fluorescence dyes 1-anilinonaphthalene-8-sulfonate (ANS), 4,4′-bis-1-anilinonaphthalene-8-sulfonate (Bis-ANS), or Thioflavin T (ThT) as medium additives. This is a new application area, since these dyes are commonly used for aggregate detection during formulation development. We determined the half maximum inhibitory concentrations of ANS (203 ± 11 µmol·L⁻¹), Bis-ANS (5 ± 0.5 µmol·L⁻¹), and ThT (3 ± 0.2 µmol·L⁻¹), and selected suitable concentrations for this application. The results showed that the emission signals of non-covalent dye antibody aggregate interaction superimposed the fluorescence signals originating from feed medium and cell culture. The fluorescence datasets were subsequently used to build PLS models, and the dye-related elevated fluorescence signals dominated the model calibration. The soft sensors based on ANS and Bis-ANS signals showed high predictability with a low error of prediction (1.7 and 2.3 mg·mL⁻¹ aggregates). In general, the combination of extrinsic dye and used concentration influenced the predictability. Furthermore, the ThT soft sensor indicated that the intrinsic fluorescence of the culture might be sufficient to predict antibody aggregation online. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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9004 KiB

Open AccessArticle

Development and Characterization of a Parallelizable Perfusion Bioreactor for 3D Cell Culture

by Dominik Egger, Monica Fischer, Andreas Clementi, Volker Ribitsch, Jan Hansmann and Cornelia Kasper

Bioengineering 2017, 4(2), 51; https://doi.org/10.3390/bioengineering4020051 - 25 May 2017

Cited by 37 | Viewed by 11983

Abstract

The three dimensional (3D) cultivation of stem cells in dynamic bioreactor systems is essential in the context of regenerative medicine. Still, there is a lack of bioreactor systems that allow the cultivation of multiple independent samples under different conditions while ensuring comprehensive control over the mechanical environment. Therefore, we developed a miniaturized, parallelizable perfusion bioreactor system with two different bioreactor chambers. Pressure sensors were also implemented to determine the permeability of biomaterials which allows us to approximate the shear stress conditions. To characterize the flow velocity and shear stress profile of a porous scaffold in both bioreactor chambers, a computational fluid dynamics analysis was performed. Furthermore, the mixing behavior was characterized by acquisition of the residence time distributions. Finally, the effects of the different flow and shear stress profiles of the bioreactor chambers on osteogenic differentiation of human mesenchymal stem cells were evaluated in a proof of concept study. In conclusion, the data from computational fluid dynamics and shear stress calculations were found to be predictable for relative comparison of the bioreactor geometries, but not for final determination of the optimal flow rate. However, we suggest that the system is beneficial for parallel dynamic cultivation of multiple samples for 3D cell culture processes. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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2146 KiB

Open AccessArticle

Hypoxic Three-Dimensional Scaffold-Free Aggregate Cultivation of Mesenchymal Stem Cells in a Stirred Tank Reactor

by Dominik Egger, Ivo Schwedhelm, Jan Hansmann and Cornelia Kasper

Bioengineering 2017, 4(2), 47; https://doi.org/10.3390/bioengineering4020047 - 23 May 2017

Cited by 21 | Viewed by 7795

Abstract

Extensive expansion of mesenchymal stem cells (MSCs) for cell-based therapies remains challenging since long-term cultivation and excessive passaging in two-dimensional conditions result in a loss of essential stem cell properties. Indeed, low survival rate of cells, alteration of surface marker profiles, and reduced differentiation capacity are observed after in vitro expansion and reduce therapeutic success in clinical studies. Remarkably, cultivation of MSCs in three-dimensional aggregates preserve stem cell properties. Hence, the large scale formation and cultivation of MSC aggregates is highly desirable. Besides other effects, MSCs cultivated under hypoxic conditions are known to display increased proliferation and genetic stability. Therefore, in this study we demonstrate cultivation of adipose derived human MSC aggregates in a stirred tank reactor under hypoxic conditions. Although aggregates were exposed to comparatively high average shear stress of 0.2 Pa as estimated by computational fluid dynamics, MSCs displayed a viability of 78–86% and maintained their surface marker profile and differentiation potential after cultivation. We postulate that cultivation of 3D MSC aggregates in stirred tank reactors is valuable for large-scale production of MSCs or their secreted compounds after further optimization of cultivation parameters. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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2969 KiB

Open AccessArticle

Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors

by Maike Kuschel, Flora Siebler and Ralf Takors

Bioengineering 2017, 4(2), 27; https://doi.org/10.3390/bioengineering4020027 - 29 Mar 2017

Cited by 31 | Viewed by 8029

Abstract

Successful scale-up of bioprocesses requires that laboratory-scale performance is equally achieved during large-scale production to meet economic constraints. In industry, heuristic approaches are often applied, making use of physical scale-up criteria that do not consider cellular needs or properties. As a consequence, large-scale productivities, conversion yields, or product purities are often deteriorated, which may prevent economic success. The occurrence of population heterogeneity in large-scale production may be the reason for underperformance. In this study, an in silico method to predict the formation of population heterogeneity by combining computational fluid dynamics (CFD) with a cell cycle model of Pseudomonas putida KT2440 was developed. The glucose gradient and flow field of a 54,000 L stirred tank reactor were generated with the Euler approach, and bacterial movement was simulated as Lagrange particles. The latter were statistically evaluated using a cell cycle model. Accordingly, 72% of all cells were found to switch between standard and multifork replication, and 10% were likely to undergo massive, transcriptional adaptations to respond to extracellular starving conditions. At the same time, 56% of all cells replicated very fast, with µ ≥ 0.3 h⁻¹ performing multifork replication. The population showed very strong heterogeneity, as indicated by the observation that 52.9% showed higher than average adenosine triphosphate (ATP) maintenance demands (12.2%, up to 1.5 fold). These results underline the potential of CFD linked to structured cell cycle models for predicting large-scale heterogeneity in silico and ab initio. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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1995 KiB

Open AccessArticle

Hybrid Approach to State Estimation for Bioprocess Control

by Rimvydas Simutis and Andreas Lübbert

Bioengineering 2017, 4(1), 21; https://doi.org/10.3390/bioengineering4010021 - 08 Mar 2017

Cited by 24 | Viewed by 5979

Abstract

An improved state estimation technique for bioprocess control applications is proposed where a hybrid version of the Unscented Kalman Filter (UKF) is employed. The underlying dynamic system model is formulated as a conventional system of ordinary differential equations based on the mass balances of the state variables biomass, substrate, and product, while the observation model, describing the less established relationship between the state variables and the measurement quantities, is formulated in a data driven way. The latter is formulated by means of a support vector regression (SVR) model. The UKF is applied to a recombinant therapeutic protein production process using Escherichia coli bacteria. Additionally, the state vector was extended by the specific biomass growth rate µ in order to allow for the estimation of this key variable which is crucial for the implementation of innovative control algorithms in recombinant therapeutic protein production processes. The state estimates depict a sufficiently low noise level which goes perfectly with different advanced bioprocess control applications. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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5178 KiB

Open AccessArticle

Multivariate Curve Resolution and Carbon Balance Constraint to Unravel FTIR Spectra from Fed-Batch Fermentation Samples

by Dennis Vier, Stefan Wambach, Volker Schünemann and Klaus-Uwe Gollmer

Bioengineering 2017, 4(1), 9; https://doi.org/10.3390/bioengineering4010009 - 25 Jan 2017

Cited by 3 | Viewed by 7195

Abstract

The current work investigates the capability of a tailored multivariate curve resolution–alternating least squares (MCR-ALS) algorithm to analyse glucose, phosphate, ammonium and acetate dynamics simultaneously in an E. coli BL21 fed-batch fermentation. The high-cell-density (HCDC) process is monitored by ex situ online attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy and several in situ online process sensors. This approach efficiently utilises automatically generated process data to reduce the time and cost consuming reference measurement effort for multivariate calibration. To determine metabolite concentrations with accuracies between ±0.19 and ±0.96·gL^−l, the presented utilisation needs primarily—besides online sensor measurements—single FTIR measurements for each of the components of interest. The ambiguities in alternating least squares solutions for concentration estimation are reduced by the insertion of analytical process knowledge primarily in the form of elementary carbon mass balances. Thus, in this way, the established idea of mass balance constraints in MCR combines with the consistency check of measured data by carbon balances, as commonly applied in bioprocess engineering. The constraints are calculated based on online process data and theoretical assumptions. This increased calculation effort is able to replace, to a large extent, the need for manually conducted quantitative chemical analysis, leads to good estimations of concentration profiles and a better process understanding. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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2890 KiB

Open AccessArticle

Fluorometric In Situ Monitoring of an Escherichia coli Cell Factory with Cytosolic Expression of Human Glycosyltransferase GalNAcT2: Prospects and Limitations

by Karen Schwab, Jennifer Lauber and Friedemann Hesse

Bioengineering 2016, 3(4), 32; https://doi.org/10.3390/bioengineering3040032 - 21 Nov 2016

Cited by 3 | Viewed by 5732

Abstract

The glycosyltransferase HisDapGalNAcT2 is the key protein of the Escherichia coli (E. coli) SHuffle^® T7 cell factory which was genetically engineered to allow glycosylation of a protein substrate in vivo. The specific activity of the glycosyltransferase requires time-intensive analytics, but is a critical process parameter. Therefore, it has to be monitored closely. This study evaluates fluorometric in situ monitoring as option to access this critical process parameter during complex E. coli fermentations. Partial least square regression (PLS) models were built based on the fluorometric data recorded during the EnPresso^® B fermentations. Capable models for the prediction of glucose and acetate concentrations were built for these fermentations with rout mean squared errors for prediction (RMSEP) of 0.19 g·L⁻¹ and 0.08 g·L⁻¹, as well as for the prediction of the optical density (RMSEP 0.24). In situ monitoring of soluble enzyme to cell dry weight ratios (RMSEP 5.5 × 10⁻⁴ µg w/w) and specific activity of the glycosyltransferase (RMSEP 33.5 pmol·min⁻¹·µg⁻¹) proved to be challenging, since HisDapGalNAcT2 had to be extracted from the cells and purified. However, fluorescence spectroscopy, in combination with PLS modeling, proved to be feasible for in situ monitoring of complex expression systems. Full article

(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)

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