Recent Advances in Bioprocessing A Review of Recently Reported Applications in Fermentation Monitoring
Fermentation: Inline Real-Time Monitoring of Key Process Parameters Vaso Vlachos, Mettler-Toledo AutoChem, Inc.
As consumer demand for bioproducts increases, so increases the need for fast and accurate bioprocess monitoring to ensure efficient process control – maximizing productivity and product purity. Traditional inline bioreactor measurements, such as pH, temperature, dissolved oxygen, CO2, agitation speed, etc., provide some process information but do not provide full information on the health, nutrient demand and metabolite production of the culture. Offline techniques to gain this information can be labor intensive and take time for analysis, thus not truly representing the process as it
exists in real time, resulting in delayed process control decisions and batch-tobatch variability. Inline ReactIR™ mid-IR spectroscopy simultaneously measures, in real time, the concentration of multiple components of the growth medium and metabolites during the course of a fermentation process, while Focused Beam Reflectance Measurement ® (FBRM ) tracks changes in biomass concentration and morphology in real time. In this white paper four case studies discussing the application of ReactIR™ and FBRM® in bioprocess monitoring
are presented, demonstrating the use of these non-invasive and non-destructive technologies in gaining insight into the bioprocess and how it exists in real time in the bioreactor. It is not the intent to go into detailed scientific findings as these were documented in each paper and it is recommended to read the original publications for this purpose. Instead, the author highlights the context in which the technology was used and how this helped researchers answer key questions.
Part I: Monitoring Nutrients and Metabolites in Real Time with Inline Mid-IR Spectroscopy 3 Case Study 1: Inline Real-time Monitoring of Fermentation Nutrients and Metabolites with Mid-IR Spectroscopy
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Case Study 2: A Single Spectra Library for Simple Mid-IR Spectroscopy Calibration for Online Monitoring of Fermentation Processes 6 Part II: Inline Monitoring of Changes in Biomass Concentrations and Morphology 10 Case Study 3: Monitoring Changes in Biomass Concentration with Inline Focused Beam Reflectance Measurement (FBRM®)
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Case Study 4: Inline Monitoring of Flocculation in Downstream Bioprocessing 14 General Conclusions 17 Appendix A – ReactIR™ Method of Measurement
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Appendix B – FBRM® Method of Measurement
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Part I: Monitoring Nutrients and Metabolites in Real Time with Inline Mid-IR Spectroscopy Accurate information on the nutrient demand of cells and organisms, and the consequent metabolite production, is necessary for effective process optimization to achieve maximum product quality and recovery. Such information is necessary in production processes in order to make sound decisions to effectively implement process control parameters. Several techniques for the real-time monitoring of nutrient and metabolite concentrations have been reported, viz. NIR, dielectric spectroscopy, Raman, 2-D Fluorescence.1,2,3,4 However, mid-IR spectroscopy offers superior advantages. As early as the 1990’s researchers reported investigations of inline mid-IR spectroscopy to monitor fermentation processes, exploiting the inherent advantages offered by this technique. Stated advantages of inline mid-IR spectroscopy3,5,6,7,8 include: -- Non-invasive and non-destructive to the process, biochemical components and organisms -- High signal to noise ratio (SNR) -- Eliminates the need for sampling and complicated sample preparation for offline analysis; hence reducing the risk of contamination and saving time -- Process components are measured as they exist in real time in the process, and remain unaffected by changes due to sampling -- Distinguishes and measures multiple components simultaneously (nutrients, metabolites and products), reducing the number of sensors in the bioreactor, while providing immediate information on the progression of the fermentation -- Measurements are made rapidly and information is available immediately for use, to be included in feedback controllers (advantageous for fast-growing cultures, where offline analysis time limits process control) -- Measurement remains unaffected by reactor operating conditions (agitation, airflow) Despite these advantages, a few disadvantages resulted in only a small uptake of mid-IR as a technology for fermentation monitoring. The calibration of complex growth media needed for mid-IR spectroscopy was tedious, and the hardware did not allow for easy alignment, while software platforms relied on expert users and spectroscopists. Advances in hardware and software development have significantly improved mid-IR spectroscopy as an easy to use tool by biologists and chemists, while improving sensitivity and information output. This section of the white paper reviews two case studies applying ReactIR™ mid-IR spectroscopy to monitor key process parameters and hence the progress of fermentation processes. The first example is of successful early work implementing inline ReactIR™, for real-time monitoring of nutrient consumption and metabolite production during the fermentation process. Thereafter, a new approach to using ReactIR™ by simplifying the extensive calibration process and accurately predicting the concentration of key process components is discussed.
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Case Study 1
Inline Real-Time Monitoring of Fermentation Nutrient and Metabolite Concentrations with Mid-IR Spectroscopy
In Situ Monitoring of an Escherichia coli Fermentation Using a Diamond Composition ATR Probe and Mid-Infrared Spectroscopy, Denise L. Doak and Janice A. Phillips, Department of Chemical Engineering, Lehigh University, Bethlehem, Pennsylvania, USA, Biotechnology Progress, 1999, 15, 529-39
Studies by Doak et al. (1999)5 on the batch fermentation of Escherichia coli proved a very early ReactIR™ model (ReactIR™ 1000 interfaced with an in situ DiComp™ probe through a K4 optical conduit) had excellent stability properties over time. With an SNR of ±0.00033AU, the ReactIR™ system performance remained unaffected by agitation, aeration and system shutdowns between batches.
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In unpublished work, the authors determined that the unknown component is a phosphorylated carbohydrate formed by the reaction between yeast extract and phosphates.
A simple experiment monitoring the media (excluding glucose) in the reactor during the sterilization cycle in real time using ReactIR™ revealed significant changes in the 1250 - 850cm-1 wavenumber range, with distinct features in the regions of phosphorylated compounds (1000-900cm-1) and carbohydrates (1250-950cm-1) (Figure 1.1). The formation of an unexpected component was detected and ReactIR™ data confirmed it remained in the medium upon completion of the sterilization cycle. The next step was to determine if ReactIR™ could monitor and quantify changes in the concentration of components of the growth medium during fermentation, and especially the fate of the unknown component formed during the sterilization cycle. Glucose, 250g/L, was added aseptically to the sterile medium, the fermentation pH was controlled at 7.0, and the temperature at 30˚C. Glucose consumption, acetic acid formation and the unknown component formed during sterilization were monitored by ReactIR™.
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3b. hydrated form of carbamazepine
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Figure 1.2. Profile of ReactIR™ spectra obtained during the fermentation
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Conclusion This study gave early insight to the successful implementation of inline ReactIR™ to monitor fermentation processes. With a simple experiment ReactIR™ showed that the sterilization cycle caused changes to the growth medium; this had not been detected before using other techniques. Monitoring changes in key media components confirmed the metabolic pathway of the microorganism throughout the fermentation process, and enabled detection of the fermentation endpoint. Moreover, the study confirmed the stability of ReactIR™ technology over long fermentation processes and over multiple batches over long periods of time.
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The spectral data obtained during the fermentation is shown in Figure 1.2. The glucose and unknown component are shown to be consumed, while organic acids accumulate. Trending spectral data for the same three components over time (Figure 1.3), the trend shows that initially glucose was consumed, and after a lag the unknown component was co-consumed with glucose. The acetic acid accumulated as glucose was consumed, however, upon depletion of the glucose, acetic acid was consumed. The unknown component remained present after 11 hours of fermentation.
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Figure 1.3. Changes in relative concentration of glucose, unknown component (produced during sterilization) and acetic acid through the duration of the fermentation process
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Case Study 2
Single Spectra Library for Simple Mid-IR Spectroscopy Calibration for Online Monitoring of Fermentations
Note:
The approach of the researchers assumed that absorbances added linearity, and that the Beer-Lambert Law was respected (see Appendix A – ReactIR™ Method of Measurement).
Online Monitoring of Nine Different Batch Cultures of E. coli by Mid-Infrared Spectroscopy, Using a Single Spectra Library for Calibration Jonas Schenka, Carla Viscasillasa, Ian W. Marisonb, Urs von Stockara a. Laboratory of Chemical and Biochemical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland b. School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland Journal of Biotechnology 134 (2008) 93–102.
The authors investigated the possibility of using a single spectra library, gaining a 6-fold decrease in the number of standards to prepare, to accurately predict the concentration of principal metabolites for cultures on different growth media.
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Figure 2.1. The six spectra used in the library, in the calculation range 1000–1500 cm−1. The two drift spectra were amplified by a factor 4 for clarity reasons.
While the investigations of this research paper were quite extensive, for the purpose of this white paper we will review only the report of the reliability of the spectral library for different culture media compositions, and thereafter the robustness of the library with respect to chemically undefined complex media, including additives such as peptone and yeast extract.
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Experimental Escherichia coli was the selected organism due to its extensive use in in research and industry. The concentration of the analytes of interest, glucose, glycerol, ammonium ion and acetic acid, were obtained by enzymatic analysis kits (R-Biopharm, Germany). All mid-IR spectra were obtained with a ReactIR™ spectrometer, equipped with a DiComp™ ATR sensor, and concentrations were calculated by applying traditional least squares regression.
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The perceived requirement of extensive and complicated calibration in preparation for use of mid-IR and multivariate models in bioprocess monitoring was challenged and reported by Schenk et al. (2008).8 Calibrations involved the preparation and measurement of numerous standards across the concentration range of all components of interest in the growth media. This process was time consuming and not amenable to process development work, where new media and conditions are explored. Given that mid-IR spectroscopy achieves simultaneous measurement of multiple nutrient and metabolite concentrations, it would be complementary to standard bioprocess measurements, if the calibration were simpler.
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Figure 2.2a. Glucose, ammonium and acetate concentration profiles measured offline by enzymatic assay (circles) and online by FTIR corresponding lines), during batch growth on glucose of the E. coli culture
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Results and Discussion The components of the single spectra library were glucose, glycerol, ammonium and acetate. Signal intensity drifts issues were eliminated by including 2 “drift spectra”, obtained by factor analysis, enabling the simultaneous measurement of metabolite concentration and correction of signal drift. The 6 spectra used in the library are shown in Figure 2.1.
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For the batch with the medium containing both glucose and glycerol, at the same concentration in C-mol, the ReactIR™ profile showed that glucose was consumed first, and then glycerol was rapidly consumed (Figure 2.2c). The concentration profiles obtained using ReactIR™ were in good agreement with the actual concentrations during the glucose phase, but then diverged significantly in the later stage. The authors explained this diverge to be due to the two carbon sources being similar in structure and functional groups. The authors concluded that the library-based modeling is accurate for single carbon source systems and qualitatively accurate for media where two or more substrates have similar functional groups.
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Figure 2.2b. Glycerol, ammonium and acetate concentration profiles measured offline by enzymatic assay (circles) and online by FTIR (corresponding lines), during batch growth on glycerol of the E. coli culture
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To study the effect and reliability of using the library-based method, it was applied to E. coli fermentations on different carbon sources, glucose, glycerol, and a combination of glucose and glycerol. For the cultures grown on each glucose and glycerol, the profiles of the carbon source, ammonium and acetate all corresponded well with the offline concentration (Figures 2.2a and 2.2b). The data also confirmed that these cultures were limited by the carbon source, with only 60% of the nitrogen source consumed. Standard errors of prediction were 3.2 and 8.7mM for glucose and glycerol, respectively, and 8mM for ammonium and acetate.
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Figure 2.2c. Glucose, glycerol, ammonium, and acetate concentration profiles measured offline by enzymatic assay (circles) and online by FTIR (corresponding lines), during batch growth on glucose and glycerol of the E. coli culture
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The robustness of the spectral library was challenged by culturing E. coli in a chemically undefined rich medium, containing yeast extract, peptone and glucose. The concentrations obtained with ReactIR™ were in excellent agreement with the offline data. The SEP values were 6.1mM and 3.4mM for glucose and acetate, respectively. It was concluded that the spectral library approach is robust enough to tolerate the addition of unknown compounds to the growth medium (Figure 2.3).
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Conclusions Traditional least squares regression modeling, using a single library of six spectra as a calibration set, was as accurate and predictive as the conventional chemometrics approach for online E. coli batch culture. This is significant in reducing calibration time. Also, this library approach does not apply PCR, making it amenable to those not experts in process modeling. Furthermore, monitoring the concentration of key media components and metabolites provides an immediate understanding of the metabolic pathway of the microorganism culture.
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Figure 2.3. Glucose and acetate concentration profiles measured offline by enzymatic assay (circles), and online by FTIR (corresponding lines), during batch growth on a rich medium of the E. coli.
Webinar Spotlight Conversion Prediction of an Enzymatic Esterification Prof. Dr. Andreas Liese discusses the implementation of FTIR-based inline analytics in the enzymatically mediated production of fatty acid esters. A new process using Novozym 435 as a catalyst in a highly viscous solvent free environment was developed and tested at pilot scale. The optimization of operation parameters and process control analytics using FTIR is discussed.
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Part I: General Conclusions
Monitoring Nutrients and Metabolites in Real Time with Inline Mid-IR Spectroscopy The data and discussion reported by the researchers in the two examples above show that inline ReactIR™ mid-IR spectroscopy offers significant advantages over other techniques in bioprocess monitoring. Investigation on early ReactIR™ systems demonstrated the stability of the technology through a fermentation process, from one batch to another, and over long periods of time including complete system shutdowns. These scenarios are representative of the practical needs of process environments. Undoubtedly the biggest advantage of ReactIR™ in situ mid-IR spectroscopy is the ability to simultaneously and accurately monitor the supply and consumption of nutrients, and the production and consumption of metabolites. The gained qualitative and quantitative data provides insight to fermentation progress and a true understanding of the process parameters in real time as they exist in the bioreactor. Based on real-time ReactIR™ data, process deviations are detected immediately and allow for good process control decisions to maintain batch-to-batch reproducibility and achieve the highest product quality and recovery.
Key Findings: - ReactIR™ accurately and reliably tracks changes in the concentration of media components in real time - Real time monitoring of process parameters provides insight into fermentation processes, enabling immediate process control
The single spectra library approach discussed makes ReactIR™ amenable for bioprocess monitoring in process development work. Advances in software have made ReactIR™ available for use by biologists and chemists not experts in spectroscopy and modeling. Advances in probe design include a fiber conduit, providing automated alignment and flexibility in the workspace. New fiber probes allow not only for Steam-in-Place sterilization, but also autoclaving the probe in place in the bioreactor. Multiplexing capabilities are now available for independently monitoring multiple bioprocesses. These hardware enhancements have significantly improved the usability of ReactIR™ for bioprocess monitoring.
Instrument Spotlight ReactIR™ multiplexed with flexible self-aligning fiber probes. www.mt.com/ReactIR For more information
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Part II: Inline Monitoring of Changes in Biomass Concentrations and Morphology Biomass concentration information provides insight to the growth stage of a culture. With the increased demand for bioproducts, cell lines, microorganism strains, and fermentation, process parameters have been optimized for high biomass concentration in order to maximize productivity. High density cultures require continual monitoring to implement and control feed strategies to cope with the high metabolic demand of the culture. Inline monitoring techniques such as turbidity, inline spectroscopy, fluorescence-based instruments, ARD-probes9, and capacitance4,10 have been implemented but have some shortcomings. Recent reports9,11 of Focused Beam Reflectance Measurement (FBRM速) to monitor changes in biomass concentrations of fermentations, also confirm the ability of the technology to detect changes in morphology of filamentous bacteria and plant cell cultures, as well as detect the agglomeration of cells. Detecting changes in morphology and the onset of agglomeration may have an impact on the productivity of a fermentation batch. As a probe-based inline technology, FBRM速 is non-invasive and non-disruptive to the process or culture, while providing real time information on changes in the number, size and morphology of the microorganisms or cells. An application of FBRM速 to monitor changes to filamentous bacterial cultures and high density E. coli are discussed. Key learnings of a downstream dewatering of a fermentation through flocculation monitored by FBRM速 are presented.
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3. Monitoring Changes in Biomass Concentration with Inline Focused Beam Reflectance Measurement Use of Focussed Beam Reflectance Measurement (FBRM®) for Monitoring Changes in Biomass Concentration Jessica Whelana, Eilis Murphyb, Alan Pearsonc, Paul Jeffersd, Patricia Kierana, Susan McDonnella and Brian Glennona a. UCD School of Chemical and Bioprocess Engineering, UCD, Belfield, Dublin 4, Ireland b. Training Department, National Institute of Bioprocessing Research and Training, Foster’s Avenue, Mount Merrion, Blackrock, Dublin 4, Ireland c. Pharmaceutical Operations, MSD Ireland, Clonmel, Tipperary, Ireland d. Process Analytical Sciences Group, Pfizer Ireland Pharmaceuticals, Loughbeg, Cork, Ireland Bioprocess and Biosystems Engineering 2012, Online DOI 10.1007/s00449-012-0681-9
An investigation of the sensitivity of FBRM® to changes in biomass concentration is reported by Whelan et al. (2012).9 The authors reported their findings for investigations on plant cells, bacteria, and a hybridoma cell line. For the purpose of this white paper, only the findings for high density Escherichia coli cultures, and those of the filamentous bacterium, Streptomyces natalensis, are discussed. Experimental S. natalensis : Batch cultures of S. natalensis were grown in shake flasks and a stirred tank bioreactor, and sampled every 24 hours to determine Dry Cell Weight (DCW). Online measurements using FBRM® were conducted in a 20L bioreactor.
E. coli: High density E. coli was cultured in shake flasks and a 20L bioreactor, and sampled at one hour intervals. DCW was determined by centrifuging 2x 1.5mL of culture for 20 minutes at 14,000rpm on a bench top centrifuge in pre-weighed Eppendorf tubes, washing with deionized water and re-centrifuging and drying at 50˚C until constant weight. FBRM®: Cell counts and cord lengths were measured with FBRM®. The FBRM® probe was inserted into a well agitated region of the 20L pilot scale reactor to obtain online measurements. Results and Discussion The changes in biomass concentration were presented. The authors reminded the reader that FBRM® measures cord length distribution, and tracks the rate and degree of change to the microorganisms as they exist naturally in the bioreactor. FBRM® is sensitive to particle size, shape and count. (See Appendix B – FBRM® Method of Measurement).
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Instrument Spotlight FBRM® G400 with autoclaveable probe for inline monitoring of changes to biomass in real time www.mt.com/FBRMG400 For more information
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S. natalensis To confirm the feasibility of FBRM® to track changes to biomass concentration and size of the S. natalensis filaments, samples were filtered through mesh sieves of 150, 180, 212, 250 and 300µm. The collected fractions were resuspended and combined sequentially while monitored by FBRM®. The data presented in Figure 3.1 confirmed that FBRM® tracked both the increase in cell counts and mean cord length.
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The next step was to investigate the relationship between biomass concentration (as g DCW L-1), and FBRM® counts. The results of two batches (DA-1 and DA-2) sampled from a stirred tank reactor were reported (Figure 3.2). The data clearly shows a strong correlation between FBRM® counts and biomass up to ~4g DCW L-1. Above this biomass concentration FBRM® counts increase in a non-linear manner. This result is expected at higher cell concentrations since as the number of particles in the measurement zone increases, the likelihood of measuring each and every particle decreases (see Appendix B – FBRM® Method of Measurement). Online FBRM® measurements made using a 20L bioreactor showed the same trend as the offline analysis discussed above (data not shown).
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E. coli Similarly to their approach to the S. natalensis study, the authors investigated the sensitivity of FBRM® to changes in biomass concentration of E. coli. An overnight shake flask culture of E. coli was diluted, 10, 25, 50 and 75% (v/v), and measured by FBRM® and as DCW. FBRM® counts and DCW proved to have a linear relationship (Figure 3.3). Further investigation included the relationship between FBRM® counts and biomass concentration, of a sample representative of a process biomass concentration cultured in a 20L pilot scale bioreactor (Figure 3.4). The data shows three distinct regions. The first region is the batch phase (0-4.3 g DCW L-1), with a strong positive linear correlation between FBRM® counts and biomass. The second phase is the fed-batch stage of the fermentation (4.3 – 45 DCW L-1), again with a strong correlation between FBRM® counts and biomass, although the slope decreased. Finally, towards the end of the fermentation (>45g DCW L-1), FBRM® did not show any increase in counts. As described with the S. natalensis study, this result was expected at the high biomass concentrations.
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Conclusion The data presented from this extensive study confirms that FBRM® is sensitive to in-process changes in biomass concentrations of the filamentous bacterium, S. natalensis, and high density E. coli cultures. Inline use of FBRM® to monitor changes in biomass concentration in real time provides immediate information on the state of the biomass and enables good process control strategies to be implemented to reduce batchto-batch variability and improve product recovery.
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The authors concluded that, overall, FBRM® was sensitive to changes in biomass concentration in high density E. coli cultures up to 45g DCW L-1. Although concentrations up to 50g DCW L-1 are often achieved, monitoring the exponential growth phase is the most important as the feed-rate must be set to prevent the growth rate elevating above the critical growth rate for inhibitory by-product production. Monitoring the exponential growth phase of E. coli with FBRM® provides real-time growth rate information and would allow process control should the biomass concentration reach critical levels.
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Figure 3.4. Relationship between the total FBRM® counts and biomass concentration for the pilot scale E. coli fermentation
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4. Inline Monitoring of Flocculation in Downstream Bioprocessing Marine Microalgae Flocculation and Focused Beam Reflectance Measurement Nyomi Uduman, Ying Qi, Michael K. Danquah, Andrew F.A. Hoadley Department of Chemical Engineering, Monash University, Wellington Road, Clayton, Victoria 3800, Australia Chemical Engineering Journal 162 (2010) 935–940
The downstream clarification of fermentation processes can be difficult and time consuming, often with multiple reprocessing steps requiring large amounts of filtration media. Flocculation is a commonly used dewatering method in several industries, offering the advantages of low cost and low energy use. In bioprocessing, flocculation can be applied to separate suspended cells and cellular particulates, to reduce the time and media required for the clarification steps, hence reducing the cost while increasing process robustness and productivity.12, 13, 14
Addition of 4mg/L 71303
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In this case study, an Australian research group explored the use of a variety of flocculants for the downstream processing of a marine microalgal culture for biodiesel production. Marine microalgae have become a viable alternative and improvement to fresh water microalgae for biodiesel production, but the dilute nature of the culture and the added small size of the cells make downstream dewatering processes cumbersome and expensive. Centrifugation and filtration have been implemented but both offer shortcomings in being energy intensive and expensive, reducing the benefit of alternative biodegradable low-cost environmentally friendly biodiesel. The authors studied the flocculation process using Focused Beam Reflectance Measurement (FBRM®), to monitor in real-time time the process kinetics, and determine process endpoint and floc strength.
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Experimental A mixed marine microalgal culture (predominantly Chlorococcum sp.) from a semi-continuous culturing reactor was used for the flocculation experiments. Several cationic and anionic polymers were screened for flocculation capabilities. FBRM® was used to track changes in cord size during the flocculation process (See Appendix B – FBRM® Method of Measurement).
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Figure 4.1. FBRM® tracking changes in particle counts, for three broad ranges of particle chord size, during the flocculation of marine microalgae with cationic polymer 71303 at 4mg/L.
Results and Discussion Flocculation process efficiency depends on the molecular weight of the polymer, the probability of collision between the microalgal cells and polymers, as well as how strongly the particles adhere to one another. Monitoring the flocculation process with inline FBRM®, it can be seen immediately after the addition of the flocculant, the number of small particles (<10µm and 10µm – 50µm) decreased while the number of larger particles increased (150-300µm) (Figure 4.1). This data confirmed the effect of the polymer in agglomerating the cells, thus the decreasing counts of small particles, to form larger flocs indicated by the increase in larger particle counts. The fluctuation seen after 550s indicated breakage of the larger flocs to increase the small particle counts. This real time information is valuable in understanding and controlling downstream processing. Conclusion Flocculation is a dewatering technique widely applied in industries and it has great applicability in the downstream processing of many bioprocesses, separating cells and cellular debris from the desired product. The data provided by FBRM® is essential in understanding flocculation kinetics, understanding floc strength/stability and determining flocculation process endpoint. This information, added to other parameters such as pH and temperature, improves the size and strength of flocs and hence the efficiency of the flocculation process.
Webinar Spotlight Rapid Optimization and Control of Solids Flocculation with Inline Particle Measurement Technology This webinar reviews the application of in situ process analytical technologies to optimize and control the flocculation process. Examples include laboratory and manufacturing applications to optimize and control: Biotech cell and cell debris separations; Pulp and paper yield, retention, and product quality; Oil/sand tailings settling rates; Waste water settling rates in the mining industry; and Flocculant and chemical surfactant effectiveness Click here to view this FREE online seminar
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Part II: Conclusions
Inline Monitoring of Changes in Biomass Concentrations and Morphology FBRM® is sensitive to changes in the biomass concentration of E. coli and the filamentous S. natalensis. Tracking biomass changes inline provides real-time information on the biomass at it exists in process, enabling immediate process control adjustments and detection of process endpoint. The result is improved productivity, and increased product yield. Flocculation, as a technique for solids separation from the desired product component, in downstream processing is gaining interest due to the effectiveness of the technique as well as the low cost. Flocculation progress kinetics, endpoint, optimal polymer dosage and floc strength are determined in real time with FBRM®, to improve the speed of clarification and reduce the subsequent filtration area and time. All FBRM® probes are suitable for Steam-in-Place protocols typically used in larger scale bioprocesses. However it is the advancement of FBRM® to the new G Series with a detachable autoclavable probe which allows for efficient use of FBRM® technology in the process development study of fermentations and other bioprocesses.
Key Findings: - Tracking changes in biomass concentrations and morphology in real time is possible with FBRM® - Flocculation progress kinetics, endpoint, optimal polymer dosage and floc strength are determined in real time with FBRM® to improve the speed of clarification and reduce the subsequent filtration area and time - FBRM® probes are Steam-inPlace and autoclave compatible
Webinar Spotlight In-process Monitoring of Cell Count, Size and Shape in Fermentation Processes This webinar outlines a number of academic and industrial case studies on fermentation processes including E. Coli, mammalian cells, plant cells and filamentous organisms. Application examples include: monitoring of cell count and size in real time with no need for sampling, correlation of in-process measurements with offline data, studying the impact of shear on cell and floc size, understanding the mechanism for changes in cell size and shape, and streamlining scale-up from shaker flasks to the pilot scale. Click here to view this FREE online seminar
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General Conclusions It is clear that bioprocess applications require Process Analytical Technology (PAT) in place in order to effectively monitor and control processes for maximum production of the desired product. ReactIR™ in situ mid-IR spectroscopy and FBRM® both offer the real-time monitoring and control of bioprocesses in process development studies and production environments. ReactIR™ offers the simultaneous accurate monitoring of multiple nutrient and metabolite concentrations, while FBRM® tracks changes in biomass concentrations. These key parameters, in conjunction with standard bioreactor sensors, provide the essential information to monitor fermentation progress and to immediately detect process deviations for effective process control. While ReactIR™ and FBRM® provide the advantages of eliminating the need for sampling, being non-destructive and non-invasive to the process or culture, it is the benefit of the real-time information from these technologies which enable immediate process understanding and the implementation of appropriate process control strategies. Effective control of the process results in decreased batch-to-batch variability, and maximizes the purity and recovery of the desired product.
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Process Analytical Technology (PAT) for Biotech Track the critical parameters process from in the fermentatio inoculation With Mid-IR, n to multiple analytes final yield process. Real-time can be tracked monitoring in real time – such as allows the throughout the transition detection of from lag to critical informationa fermentation metabolites, exponential or the deficiency without delay of key nutrients. growth, the accumulation of inhibiting In an example of the advanced et al. (2004) application used in situ of Process
Process Analytical Technology (PAT) for Biotech
Initial pathway
ethanol Pathway
for biomass
growth
acetate
for production
fructose
Analytical ReactIR™ applied anReported Applications Technology, A Review of Recently intoFermentation and BioProcessing FTIR adaptive control simultaneousl Kornmann
Visit our BioProcessing and Fermentation webpage for resources including white papers and webinars, as well as application and product information.
biomass
of EPS
gluconace tan
Figure 1-5. Metabolic gluconacetan states for biomass production. al, 2004) (ref: Kornmann and et
strategy to batch-to-batch y monitor maximize six analytes, variability. yield of the and desired product and reduce Measurements of key fructose, ammonium,nutrients and metabolites – ethanol, and phosphate the desired – were monitored acetate, product along with with consecutive – gluconacetan. The fed-batch fermentations process was designed xylinus, in which the of Gluconacetoba bacteria was ethanol, fed cter first cultivated at a controlled on a feed of rate to maintain acetate in the bioreactor. a constant Ethanol feed level of with increasing biomass, maximizing increased exponentially avoiding ethanol biomass growth inhibition. limiting factor, while Once dissolved a fructose oxygen became feed was used etan production. a to maximize Terry P. Redman,gluconacUrs Groth, Fabio Visentin, Dominique Hebrault,
Optimization of Fermentation and BioProcessing
Ben Smith, Des O’Grady, Brian O’Sullivan, METTLER TOLEDO
The ability to measure the concentration simultaneousl of multiple y, using in situ key analytes
Mid-IR,ofhas providing Fermentation ofand bioprocessing play analysis optical (OD) and conbeendensity a “metabolic described terms critical roles Tracking in the discovery and manu- snapshot”[ centration ofSivakesava multiple analytesin(via HPLC this metabolic fingerprint methods) haset helped al, 2001]. batch facture of new pharmaceuticals underprovides an and spe- and other in real time insight throughout that cannot a routeand cialty chemicals, and in the sustainableand stand the operation the of bioto improve bioprocess be realized with offline production of bulk fuels and commodreactors, but there can optimization be significant cost sample analysis. ity chemicals. processes and time delay involved in withdrawing, TheYet fastfermentation and sensitive are often ing operated with a minimalresponse level preparing, of Mid-IRand analyzing samples on an real-time monitoring is In capable ongoing basis. fed-batch fermentation, of monitoring and control, limiting of themultiple of providcharacterizati analytes for ability toInoptimize yieldson andand production which often relies on the controlled conmore situ monitoring elucidation of the fermentation efficient rates. of critical centration of a specific limiting process.nutrient, real-time optimization process variables result in significant deand controltime-delays canwill also enable maximized of fermentation yield However, fermentation processes involve viations from optimal operation. and reduced processes batch-to-batch for living cells that can be extremely sensitive variability. to slight changes in their environment. Within the pharmaceutical industry, As a result, many bioprocesses exhibit a process analytical measurement of critisignificant amount of natural batch-to- cal process parameters is one of the key batch variability. Offline sampling and elements of the Process Analytical Tech-
nologies (PAT) framework and Quality by Design (QbD) initiative of the US FDA and other global regulatory agencies. In situ measurement of critical process parameters enables more accurate determination of process kinetics and dynamics to create more detailed understanding, more accurate models, and the capabil- Figure 1-6. Real-time ity of real-time control. Advanced Process ing two consecutive data collected durfed-batch Analytical Technologies can also be ap-G. xylinus on cultures of ethanol and plied to achieve greater understandingfeed was used to generate fructose. Ethanol Once oxygen initial biomass. uptake and optimization of fermentation andfeed was switched became limiting factor, to fructose produce gluconacetan bioprocesses. to continue to product. (ref: (EPS) as the desired Kornmann Copyright Elsevier, used et al., 2004, Fig. by permission) 3,
4
3b. hydrated
form of carbamezapine
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Next Steps in Enzymatic Catalysis
Monitoring Cell Count, Size and Shape in Fermentation Processes
Rapid Optimization and Control of Solids Flocculation
Recent Advances in Organic Chemistry
confirming the prolonged exposure of caused degradation the product of the lactone to the catalyst concluded ring moiety, and phosphate that the buffer after the acetate reaction should be stopped resulting in product loss. The authors at approximately conversion is complete, product to maximize 9-10 hours, and followed yield. In doing by the downstream immediately in isolating this,
a. 0.28
Spectra [A.U.]
0.25
succeeded
00:41:53 04:28:53 17:43:53
1900
1800
1700
Tr (°C)
35
34
1600
1500
1400
1300
Wavenumber
40 5.9
(pH)
39
38 37
36
1200
(cm -1)
1100
1000
900
800
700
5.8 5.7
0.22
Tr
0.20
FTIR
0.18 0.16 0.14 0.12 0.10
5.1
0.08
5
4.9 00:00
0.24
pH
5.6 5.5 5.4 5.3 5.2
0.06 0.04 03:00
06:00
0.02 09:00
12:00
Relative Time
Figure 1.3.
[A.U.]
The Next Steps in Enzymatic Catalysis
0.23 0.20 0.18 0.15 0.13 0.10
0.08 0.05 0.03 0.00
b.
1223cm -1
In situ ReactIR™ provided the biocatalytic reaction mechanism authors with the information were also able to prevent the to understand to monitor their reaction progress the undesired competing achieve an optimized reaction. They and optimize (tert-butyldimethyls procedure for the the reaction deprotection of acetyl moiety conditions to a key lactonized ilyloxy)-6-oxotetrahydro-2 in ((2S,4R statin side )-4chain precursor H-pyran-2-yl)methyl (hydroxymeth acetate to yl)-tetrahydro(4R provide mild conditions 2H-pyran-2-one. ,6S)-4-(tert-butyldimethyl proved to be silyloxy)-6The enzymatic high yielding and economically catalysis employed under favorable.
LabMax Thermostat_C/ln1
the process
Conclusion
Reaction
Alcohol 2 atApplicationstheUsing isolation of authors reported A Review of Recently Reported In Situthat Spectroscopy 95% yield. the
15:00
(hours)
0.00 18:00
21:00
-0.02
Vaso Vlachos, Mettler-Toledo AutoChem, Inc.
Enzymes, as biocatalysts, are a highly dation, reduction, addition – elimina- This white paper highlights three examsuitable, environmentally friendly alter- tion, halogenation and dehalogenation, ples – pharmaceutical, academic and native to heavy metal industrial cata- and transesterification(1,3). ReactIR™ is military – where ReactIR™ was used lysts. The “Green Chemistry” nature a real-time in situ reaction analysis sys- to monitor the enzyme-catalyzed reacof enzymes as catalysts is based on a tem that enables scientists to easily and tions in real time, to enable researchers number of characteristics. Enzymes are rapidly obtain comprehensive informa- to understand reaction mechanisms, and Technolo biodegradable and typically produced tion and understanding about reaction determine kinetic parameters. It is not gy Spotlight through fermentation of renewable feed- chemistry. ReactIR™ monitors reactive the intent to go into detailed scientific stocks(1) or easily mass produced through chemistry using well understood mid- findings as these were documented in recombinant technologies(2). Enzymes IR spectroscopy. A robust ATR probe is each paper and it is recommended to also catalyze chemical reactions under inserted directly into the reaction ves- read the original publications for this mild temperature (20°C-40˚C) and pH sel, providing a “molecular video” of the Synthesi purpose. Instead, the author highlights s Workstat conditions (pH 5-8), and perform well reaction. The concentration changes of the context in which ReactIR™ ions was used Delivering in aqueous environments(1). Examples all key reactive and transient species are andand howproducts this helped researchers answer for early bio-pharmaceu application of enzyme-catalyzed industrial-scale monitored allowing for mechanism and timekeypressures questions. tical industries can testing in the fine and to deliver be challenging. specialty optimized. products chemical, Scientists Chemists organic reactions include hydrolysis, oxi- pathway determination. using synthetic and are and
EasyMax ®
OptiMax ™
6
under engineers to increase routes that have not the chemistry personal productivity.are required to make yet been the right decisions leads to shorter Characterizing development the process faster while optimizing times and Synthesis lower costs. workstations platform such as EasyMax for product ® and process and challenges development, OptiMax™, provide of today’s competitive a global market.enabling scientists simple to meet the www.mt.com/s
To learn more ynthesis-works visit tations
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Appendix A:
ReactIR™ In situ Spectroscopy Method of Measurement ReactIR™ is proven to have the inherent performance,
in situ sampling versatility and intuitive, powerful reaction analysis software necessary to easily and rapidly provide comprehensive
www.mt.com/ReactIR
information and understanding about chemistry.
Beer’s Law: A = abc a & b are constants so A is proportional to concerntration! IR Energy to Detector
a - absorptivity, extinction coefficient b - optical path length into the sample (cm) c - solute molar concentration (mol/L)
IR Energy In
ZnSe Support Element Hostelloy Housing Infrared Beam Diamond Sensing Element/ ATR Crystal
Depth of Penetration (DP) = 2µm
Sample in contact with evanescent wave 6 Reflections
How does ReactIR™ work? ReactIR™ operates via the principle of Fourier Transform Infrared (FTIR) spectroscopy. However, chemists and engineers are interested in information, not just spectra. Therefore, ReactIR™ and iC IR™ reaction analysis software were designed as a powerful, comprehensive technology suite that utilizes FTIR spectroscopy data to allow the non-expert
to fully realize the power of the system and gain an improved understanding of their chemistry or biochemistry. This is in stark contrast to traditional FTIR spectrometers and operational software that are occasionally required to monitor chemistry.
H N N N
N H
CONH-tBu
CONH-tBu
Intermediate
0.14
N N
H2 CONH-tBu
H N N H
H2 CONH-tBu
H N N H
CONH-tBu
Concentration
0.12
300
0.10 0.08
200
0.06 0.04
100
0.02 0.00
0 1700
1650
1600
Wavenumber (cm ) -1
18
Time (mins)
Appendix B:
Focused Beam Reflectance Measurement (FBRM®) Measurement for optimization in real time – FBRM® is a precise and sensitive technology which tracks changes to particle dimension, particle shape, and particle count. Over a wide detection range from 0.5 to 2000µm, measurements are acquired in real time while particles are forming and can still be modified enabling process optimization and control. No sampling or sample preparation is required – even in highly concentrated (70% and higher) and opaque suspensions.
Figure c. Chord Length Distributions
Laser Source Laser Return Optics Module 1
2
3
4
Figure b. Figure a.
Sapphire Window How does FBRM® work? The FBRM® probe is immersed into a dilute or concentrated flowing slurry, droplet emulsion, or fluidized particle system. A laser is focused to a fine spot at the sapphire window interface (Figure a). A magnified view shows individual particle structures will backscatter the laser light back to the probe (Figure b). These pulses of backscattered light are detected by the probe and translated into Chord Lengths based on the simple calculation of the scan speed (velocity) multiplied by the pulse width (time). A chord length (a fundamental measurement of particle dimension) is simply defined as the straight line distance from one edge of a particle or particle 19
structure to another edge. Thousands of individual chord lengths are typically measured each second to produce the Chord Length Distribution (CLD) (Figure c). The CLD is a “fingerprint” of the particle system, and provides statistics to detect and monitor changes in particle dimension and particle count in real time (Figure d). Unlike other particle analysis techniques, FBRM® measurement makes no assumption of particle shape. This allows the fundamental measurement to be used to directly track changes in the particle dimension, shape, and count.
Figure d. Trended Statistics
References 1. Dabros, M.; Fe, P.; Amrhein, M.; Bonvin, D.; Marison, I.W. Data Reconciliation of Concentration Estimates from Mid-Infrared and Dielectric Spectral Measurements for Improved On-Line Monitoring of Bioprocesses. Biotechnology Progress 2009, 25, 578-588. 2. Landgrebe, D.; Haake, C.; Höpfner, T.; Beutel, S.; Hitzmann, B.; Scheper, T.; Rhiel, M.; Reardon, K.F. On-line Infrared Spectroscopy for Bioprocess Monitoring. Applied Microbiology and Biotechnology 2010, 88, 11-22. 3. Roychoudhury, P.; Harvey, L.M.; McNeil, B. The Potential of Mid Infrared Spectroscopy (MIRS) for Real Time Bioprocess Monitoring. Analytica Chimica Acta 2006, 571, 159-66. 4. Teixeira, A.P.; Oliveira, R.; Alves, P.M.; Carrondo, M.J.T. Advances in On-Line Monitoring and Control of Mammalian Cell Cultures: Supporting the PAT initiative. Biotechnology Advances 2009, 27, 726-32. 5. Doak, D.L.; Phillips, J.A. In situ Monitoring of an Escherichia coli Fermentation Using a Diamond Composition ATR Probe and Mid-Infrared Spectroscopy. Biotechnology Progress 1999, 15, 529-39. 6. Pollard, D.; Buccino, R.; Connors, N.; Kirschner, T.; Olewinski, R.; Saini, K.; Salmon, P. Real-Time Analyte Monitoring of a Fungal Fermentation, at Pilot Scale, Using In Situ Mid-Infrared Spectroscopy. Bioprocess and Biosystems Engineering 2001, 24, 13-24. 7. Rhiel, M.; Ducommun, P.; Bolzonella, I.; Marison, I.; von Stockar, U. Real-Time In Situ Monitoring of Freely Suspended and Immobilized Cell Cultures Based on Mid-Infrared Spectroscopic Measurements. Biotechnology and Bioengineering 2002, 77, 174-185. 8. Schenk, J.; Viscasillas, C.; Marison, I.W.; von Stockar, U. On-Line Monitoring of Nine Different Batch Cultures of E. coli by Mid-Infrared Spectroscopy, Using a Single Spectra Library for Calibration. Journal of Biotechnology 2008, 134, 93-102. 9. Whelan, J.; Murphy, E.; Pearson, A.; Jeffers, P.; Kieran, P.; McDonnell, S.; Glennon, B. Use of Focussed Beam Reflectance Measurement (FBRM) for Monitoring Changes in Biomass Concentration. Bioprocess and Biosystems Engineering 2012, 35, 963-975. 10. Dabros, M.; Dennewald, D.; Currie, D.J.; Lee, M.H.; Todd, R.W.; Marison, I.W.; von Stockar, U. Cole-Cole, Linear and Multivariate Modeling of Capacitance Data for On-Line Monitoring of Biomass. Bioprocess and Biosystems Engineering 2009, 32, 161-73. 11. Uduman, N.; Qi, Y.; Danquah, M.K.; Hoadley, A.F.A. Marine microalgae flocculation and focused beam reflectance measurement. Chemical Engineering Journal 2010, 162, 935-940. 12. Riske, F.; Schroeder, J.; Belliveau, J.; Kang, X.; Kutzko, J.; Menon, M.K. The use of chitosan as a flocculant in mammalian cell culture dramatically improves clarification throughput without adversely impacting monoclonal antibody recovery. Journal of Biotechnology 2007, 128, 813-23. 13. Senczuk, A. (Amgen) Understanding Flocculation: Particle Size, Filterability. Presented at AIChE Puget Sound Local Section Meeting, November 2010. 14. Miranda, R.; Negro, C.; Blanco, A. Internal Treatment of Process Waters in Paper Production by Dissolved Air Flotation with Newly Developed Chemicals. 2. Field Trials. Industrial & Engineering Chemistry Research 2009, 48, 2199-2205.
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