Process adapted calibration improves fluorometric pH sensor precision in sophisticated fermentation processes

Miniaturization and automation have become increasingly popular in bioprocess development in recent years, enabling rapid high‐throughput screening and optimization of process conditions. In addition, advances in the bioprocessing industry have led to increasingly complex process designs, such as pH and temperature shifts, in microbial fed‐batch fermentations for optimal soluble protein expression in a range of hosts. However, in order to develop an accurate scale‐down model for bioprocess screening and optimization, small‐scale bioreactors must be able to accurately reproduce these complex process designs. Monitoring methods, such as fluorometric‐based pH sensors, provide elegant solutions for the miniaturization of bioreactors, however, previous research suggests that the intrinsic fluorescence of biomass alters the sigmoidal calibration curve of fluorometric pH sensors, leading to inaccurate pH control. In this article, we present results investigating the impact of biomass on the accuracy of a commercially available fluorometric pH sensor. Subsequently, we present our calibration methodology for more precise online measurement and provide recommendations for improved pH control in sophisticated fermentation processes.     

Generic Raman‐based calibration models enabling real‐time monitoring of cell culture bioreactors

Raman‐based multivariate calibration models have been developed for real‐time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO‐based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1004–1013, 2015     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

Advanced near‐infrared monitor for stable real‐time measurement and control of Pichia pastoris bioprocesses

Near‐infrared spectroscopy is considered to be one of the most promising spectroscopic techniques for upstream bioprocess monitoring and control. Traditionally the nature of near‐infrared spectroscopy has demanded multivariate calibration models to relate spectral variance to analyte concentrations. The resulting analytical measurements have proven unreliable for the measurement of metabolic substrates for bioprocess batches performed outside the calibration process. This paper presents results of an innovative near‐infrared spectroscopic monitor designed to follow the concentrations of glycerol and methanol, as well as biomass, in real time and continuously during the production of a monoclonal antibody by a Pichia pastoris high cell density process. A solid state instrumental design overcomes the ruggedness limitations of conventional interferometer‐based spectrometers. Accurate monitoring of glycerol, methanol, and biomass is demonstrated over 274 days postcalibration. In addition, the first example of feedback control to maintain constant methanol concentrations, as low as 1 g/L, is presented. Postcalibration measurements over a 9‐month period illustrate a level of reliability and robustness that promises its adoption for online bioprocess monitoring throughout product development, from early laboratory research and development to pilot and manufacturing scale operation. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:749–759, 2014     

Two‐dimensional fluorescence as soft sensor in the monitoring of biotransformation performed by yeast

Soft sensors are powerful tools for bioprocess monitoring due to their ability to perform online, noninvasive measurement, and possibility of detection of multiple components in cultivation media, which in turn can provide tools for the quantification of more than one metabolite/substrate/product in real time. In this work, soft sensor based on excitation‐emission fluorescence is for the first time applied for the monitoring of biotransformation production of 2‐phenylethanol (2‐PE) by yeast strains. Main process parameters—such as optical density, glucose, and 2‐PE concentrations—were determined with high accuracy and precision by fluorescence fingerprinting coupled with partial least squares regression. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:299–307, 2017     

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.     

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.     

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.     

Early‐stage sustainability assessment of biotechnological processes: A case study of citric acid production

Sustainability assessment using a life‐cycle approach is indispensable to contemporary bioprocess development. This assessment is particularly important for early‐stage bioprocess development. As early‐stage investigations of bioprocesses involve the evaluation of their ecological and socioeconomic effects, they can be adjusted more effectively and improved towards sustainability, thereby reducing environmental risk and production costs. Early‐stage sustainability assessment is an important precautionary practice and, despite limited data, a unique opportunity to determine the primary impacts of bioprocess development. To this end, a simple and robust method was applied based on the standardized life‐cycle sustainability assessment methodology and commercially available datasets. In our study, we elaborated on the yeast‐based citric acid production process with Yarrowia lipolytica assessing 11 different substrates in different process modes. The focus of our analysis comprised both cultivation and down‐stream processing. According to our results, the repeated batch raw glycerol based bioprocess alternative showed the best environmental performance. The second‐ and third‐best options were also glycerol‐based. The least sustainable processes were those using molasses, chemically produced ethanol, and soy bean oil. The aggregated results of environmental, economic, and social impacts display waste frying oil as the best‐ranked alternative. The bioprocess with sunflower oil in the batch mode ranked second. The least favorable alternatives were the chemically produced ethanol‐, soy oil‐, refined glycerol‐, and molasses‐based citric acid production processes. The scenario analysis demonstrated that the environmental impact of nutrients and wastewater treatment is negligible, but energy demand of cultivation and down‐stream processing dominated the production process. However, without energy demand the omission of neutralizers almost halves the total impact, and neglecting pasteurization also considerably decreases the environmental impact.     

Affinity Chromatography: An Enabling Technology for Large‐Scale Bioprocessing

Affinity chromatography (AC) has been used in large‐scale bioprocessing for almost 40 years and is considered the preferred method for primary capture in downstream processing of various types of biopharmaceuticals. The objective of this mini‐review is to provide an overview of a) the history of bioprocess AC, b) the current state of platform processes based on affinity capture steps, c) the maturing field of custom developed bioprocess affinity resins, d) the advantages of affinity capture‐based downstream processing in comparison to other forms of chromatography, and e) the future direction for bioprocess scale AC. The use of AC can result in economic advantages by enabling the standardization of process development and the manufacturing processes and the use of continuous operations in flexible multiproduct production suites. These concepts are discussed from a growing field of custom affinity bioprocess resin perspective. The custom affinity resins not only address the need for a capture resin for non‐platformable processes, but also can be employed in polishing applications, where they are used to define and control drug substance composition by separating specific product variants from the desired product form.     

Use of uniform designs in combination with neural networks for viral infection process development

This work aimed to compare the predictive capacity of empirical models, based on the uniform design utilization combined to artificial neural networks with respect to classical factorial designs in bioprocess, using as example the rabies virus replication in BHK‐21 cells. The viral infection process parameters under study were temperature (34°C, 37°C), multiplicity of infection (0.04, 0.07, 0.1), times of infection, and harvest (24, 48, 72 hours) and the monitored output parameter was viral production. A multilevel factorial experimental design was performed for the study of this system. Fractions of this experimental approach (18, 24, 30, 36 and 42 runs), defined according uniform designs, were used as alternative for modelling through artificial neural network and thereafter an output variable optimization was carried out by means of genetic algorithm methodology. Model prediction capacities for all uniform design approaches under study were better than that found for classical factorial design approach. It was demonstrated that uniform design in combination with artificial neural network could be an efficient experimental approach for modelling complex bioprocess like viral production. For the present study case, 67% of experimental resources were saved when compared to a classical factorial design approach. In the near future, this strategy could replace the established factorial designs used in the bioprocess development activities performed within biopharmaceutical organizations because of the improvements gained in the economics of experimentation that do not sacrifice the quality of decisions. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:532–540, 2015     

Physiological description of multivariate interdependencies between process parameters,morphology and physiology during fed‐batch penicillin production

Optimization of productivity and economics of industrial bioprocesses requires characterization of interdependencies between process parameters and process performance. In the case of penicillin production, as in other processes, process performance is often closely interlinked with the physiology and morphology of the organism used for production. This study presents a systematic approach to efficiently characterize the physiological effects of multivariate interdependencies between bioprocess design parameters (spore inoculum concentration, pO₂ control level and substrate feed rate), morphology, and physiology. Method development and application was performed using the industrial model process of penicillin production. Applying traditional, statistical bioprocess analysis, multivariate correlations of raw bioprocess design parameters (high spore inoculum concentration, low pO₂ control as well as reduced glucose feeding) and pellet morphology were identified. A major drawback of raw design parameter correlation models; however, is the lack of transferability across different process scales and regimes. In this context, morphological and physiological bioprocess modeling based on scalable physiological parameters is introduced. In this study, raw parameter effects on pellet morphology were efficiently summarized by the physiological parameter of the biomass yield per substrate. Finally, for the first time to our knowledge, the specific growth rate per spore was described as time‐independent determinant for switching from pellet to disperse growth during penicillin production and thus introduced as a novel, scalable key process parameter for pellet morphology and process performance. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:689–699, 2014     

Advanced online monitoring of cell culture off‐gas using proton transfer reaction mass spectrometry

Mass spectrometry has been frequently applied to monitor the O₂ and CO₂ content in the off‐gas of animal cell culture fermentations. In contrast to classical mass spectrometry the proton transfer reaction mass spectrometry (PTR‐MS) provides additional information of volatile organic compounds by application of a soft ionization technology. Hence, the spectra show less fragments and can more accurately assigned to particular compounds. In order to discriminate between compounds of non‐metabolic and metabolic origin cell free experiments and fed‐batch cultivations with a recombinant CHO cell line were conducted. As a result, in total eight volatiles showing high relevance to individual cultivation or cultivation conditions could be identified. Among the detected compounds methanethiol, with a mass‐to‐charge ratio of 49, qualifies as a key candidate in process monitoring due to its strong connectivity to lactate formation. Moreover, the versatile and complex data sets acquired by PTR MS provide a valuable resource for statistical modeling to predict non direct measurable parameters. Hence, partial least square regression was applied to the complete spectra of volatiles measured and important cell culture parameters such as viable cell density estimated (R² = 0.86). As a whole, the results of this study clearly show that PTR‐MS provides a powerful tool to improve bioprocess‐monitoring for mammalian cell culture. Thus, specific volatiles emitted by cells and measured online by the PTR‐MS and complex variables gained through statistical modeling will contribute to a deeper process understanding in the future and open promising perspectives to bioprocess control. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:496–504, 2014     

Soft sensors in bioprocessing: a status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.     

Bioprocess engineering: now and beyond 2000

Abstract: Bioprocess engineering may be defined as the translation of life-science discoveries into practical products, processes, or systems capable of serving the needs of society. It is a critical link from discovery to commercialization. Current bioprocess engineering is primarily focused on biopharmaceutical products of high dollar value per gram such as erythropoietin or growth hormones. However, other products of current interest include ethanol, amino acids, organic acids, antibiotics, and specialty chemicals. Current challenges for increased use of bioprocesses for producing bulk and semi-bulk chemicals include both technical and infrastructural barriers. Technical barriers are easy to identify and at times can be overcome by engineering improvements or changes brought about radical developments in science (e.g. recombinant DNA). Infrastructural barriers, such as raw-material substitutions or educational limitations are more difficult to define and change. Recently the National Academy of Sciences examined barriers to bioprocess engineering and issued a report entitled: "Putting Biotechnology to Work: Bioprocess Engineering". A key recommendation was the establishment of a coordinated long-range plan of research, development, training and education in bioprocess engineering involving participation by industry, academe and the federal government. The report was the first national analysis devoted entirely to bioprocess engineering and covered new topics such as space bioprocess engineering. Other topics covered by the author include the current state of the US chemical industry and future directions in three promising areas of bioprocess engineering environmental bioprocess engineering, marine bioprocess engineering and microsystem bioprocess engineering.     

Implementation of proton transfer reaction‐mass spectrometry (PTR‐MS) for advanced bioprocess monitoring

We report on the implementation of proton transfer reaction‐mass spectrometry (PTR‐MS) technology for on‐line monitoring of volatile organic compounds (VOCs) in the off‐gas of bioreactors. The main part of the work was focused on the development of an interface between the bioreactor and an analyzer suitable for continuous sampling of VOCs emanating from the bioprocess. The permanently heated sampling line with an inert surface avoids condensation and interaction of volatiles during transfer to the PTR‐MS. The interface is equipped with a sterile sinter filter unit directly connected to the bioreactor headspace, a condensate trap, and a series of valves allowing for dilution of the headspace gas, in‐process calibration, and multiport operation. To assess the aptitude of the entire system, a case study was conducted comprising three identical cultivations with a recombinant E. coli strain, and the volatiles produced in the course of the experiments were monitored with the PTR‐MS. The high reproducibility of the measurements proved that the established sampling interface allows for reproducible transfer of volatiles from the headspace to the PTR‐MS analyzer. The set of volatile compounds monitored comprises metabolites of different pathways with diverse functions in cell physiology but also volatiles from the process matrix. The trends of individual compounds showed diverse patterns. The recorded signal levels covered a dynamic range of more than five orders of magnitude. It was possible to assign specific volatile compounds to distinctive events in the bioprocess. The presented results clearly show that PTR‐MS was successfully implemented as a powerful bioprocess‐monitoring tool and that access to volatiles emitted by the cells opens promising perspectives in terms of advanced process control. Biotechnol. Bioeng. 2012; 109: 3059–3069. © 2012 Wiley Periodicals, Inc.