Accelerating Microbial Bioprocess Development using Soft-Sensors

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1 Accelerating Microbial Bioprocess Development using Soft-Sensors Highlights Microbial bioprocess softsensors for bioprocess design-, analysis and control was implemented in the Infors Eve software and tested Accurate: Estimation of the biomass concentration, specific growth rates and yield coefficient in real-time Plug-and-play: Generically applicable without training data Robust and simple to use: User-focused implementation in the novel INFORS Eve bioprocess software Benefits Reduce costs for bioprocess sampling and instrumentation by extracting maximum information from your data Simplify your bioprocesses and make them more predictable with soft-sensors Application Spectrum Biologics produced in microbial production systems on defined media o P. Pastoris, E. coli Recombinant proteins, Antibody fragments, pdna, growth factors Introduction Successful commercialization of innovative biotech products demand the development of manufacturing bioprocesses that can be transferred safely to manufacturing scale. To date, the goldstandard for bioprocess development are experiments carried out in laboratory scale bioreactors. Well-designed laboratory scale bioreactors show mass transfer characteristics, aspect ratios and power inputs that enable to develop bioprocesses under conditions that are similar to large scale. Furthermore, they provide the advanced instrumentation and process automation. Therefore, they enable insights in the underlying mechanistic that drive productivity and product quality. Key understanding the mechanistic that drive productivity and product quality is to understand and monitor microbial growth and substrate utilization. For microbial processes, the specific growth rate stirs product quality (CITE, CITE) and in combination with the biomass concentration also stirs productivity (CITE). Furthermore, the biomass concentration is frequently used as a trigger for process automation (initiation of induction, harvest) and can therefore be considered key information for understanding and controlling bioprocesses. To date, the biomass concentration and the specific growth rate are measured using off-line biomass dry cell weight measurements (requiring minimal 24 hours of drying time) or estimated via optical density (OD) measurements. Both methods require sampling (violation of the sterile barrier of the reactor) and are off-line (liquid has to be transferred to the measurement device and data is available with time delay). In-line instrumentation based on backscatter or dielectric spectroscopy (capacitance) are available, however they are relatively expensive and require frequent recalibration of the measurement device. 1

2 A software sensor, or short soft-sensor, is a software component that is capable of predicting non-measured process variables based on a mathematical model. Soft-sensors can be designed for the online-monitoring of the biomass concentration and the specific growth rate in bioprocesses. Typical benefits of using software sensors compared to traditional hardtype sensors in the field of bioprocessing are minimal investment and maintenance costs and no violation of the sterile barrier of the bioreactor. Furthermore, software sensors can also be used to measure their entities in historical datasets. Materials and Methods Strains For the E. coli fermentations, a recombinant E. coli XY strain described in XY was used. For the P. Pastoris fermentations, a recombinant P. Pastoris strain producing XY as described in XY was used. Fermentation conditions The detailed description of the cultivation of E. coli is described elsewhere (Cite Wechselberger efficient feeding profile optimization). The detailed description of the cultivation of P. Pastoris is described elsewhere (Dietzsch et al., 2011a). A software sensor, or short softsensor, is a software component that used available process information to predict non-measured process information that is important for the scientist This contribution describes how a suite of microbial bioprocess soft-sensors can be applied to simplify the operation of bioprocesses and extract maximum information from bioprocess data. The power of the presented soft-sensors are demonstrated on recombinant E. coli and P. Pastoris fermentations. Furthermore, the presented soft-sensors are characterized in respect to soft-sensor accuracy and soft-sensor robustness. This is done based on a high number of historical datasets and a statistical analysis of results. Based on the results, a roadmap is provided, demonstrating how the presented softsensors can be applied for bioprocess design, analysis and control within the bioprocess lifecycle. Bioreactor set-up Fermentations were carried out in a Labfors bioreactor (Infors, Bottmingen, Switzerland) of 5 l maximum working volume. The bioreactors were equipped with standard bioreactor instrumentation: The ports on the top plate of the reactor were used for a dissolved oxygen sensor (Hamilton, Switzerland), ph probe (Hamilton, Bonadutz, Switzerland). CO 2 and O 2 in the off-gas stream was measured by a gas analyzer (Müller Systems AG, Egg, Switzerland) following non dispersive infrared (CO 2 ) and paramagnetic (O 2 ) principle. Data management and computations Data was recorded using the process information management and bioreactor control software Eve (Infors, Bottmingen, Switzerland). The described soft-sensors are provided as integrated applications in the Infors Eve software. 2

3 Results and Discussion Batch phase soft-sensor Following inoculation of the bioreactor, the softsensor was started by entering the subsequent parameters; initial volume, estimated initial biomass (based on OD measurement), and substrate amount at batch start. The used substrate and organism had to be selected from a preconfigured list. After 2 hours of batch-duration, the biomass soft-sensor delivered the first predictions of future trajectories of glucose, biomass and the duration to batch end. At 3 hours of batch-duration one measurement for a biomass concentration (based on the OD measurement) was measured and entered to improve the prediction precision. Predictions of the biomass soft-sensor at 2.5 hours after process start are depicted in Figure 1. The batch-phase soft-sensor predictions were updated in real-time in 1 minute intervals. Analysis of accuracy Goal was to analyze the batch phase soft-sensor in respect to the prediction accuracy. Therefore, the soft-sensor was tested on 16 historical datasets of E. coli batch-processes performed at Vienna University of Technology. The predictions of the soft-sensor of the end-of-batch time-point as well as the predicted end-biomass concentration were analyzed. As a common reference point the prediction accuracy at 50% overall batch duration was analyzed. Figure 2 shows a scatter plot between the batch duration prediction after half of the full batch duration and the actual batch duration. The predictions were made without any reference OD measurement. It is further possible to include additional information and improve the precision by including DCW values from OD measurements. We found out that especially for the accurate prediction of the biomass concentration, at least one DCW value from an OD measurement should be included. Figure 1: Based on real-time signals the end of the batch phase as well as the current process states are predicted. This information is updated and provided to the user in real-time. Current and past biomass concentration and substrate concentrations are plotted as dots. Predicted future behavior is plotted in lines. Figure 2: Analyzed accuracy of the batch-phase soft-sensor (applied at 50% of batch time). End-of-batch biomass predictions show an error of XX. 3

4 End-of-batch detector The end-of-batch detector was configured prior to process start for the detection of the batch-end. A dead time of 3 hours after batch start was used. After this dead time the end-of-batch detector waits for a typical increase in the dissolved oxygen concentration. The threshold was set to 20 %. Once the batch end was detected (Figure 3), the fed-batch process was initiated (start of feeding ramp, start of pumps, change of PID parameters, see Figure 4). Figure 3: Based on real-time signals, the end-of the batch phase is detected and this information is used for process automation.. Analysis of accuracy Since the end-of-batch detector is used for process automation, high accuracy and robustness is prerequisite for successful operation. Therefore, the soft-sensor was tested on 54 historical datasets of E. coli and P. Pastoris batch processes performed at Vienna University of technology. In most of the cases the batch end was detected successfully. In two batches, the batch end was set at wrong points in time. Analysis of the data revealed that there was a sharp DO increase due to an immediate start of the oxygen supplementation flow present in the data set. There are two possibilities to prevent this behavior: 1. Starting the oxygen flow in the dead-time of the algorithm 2. Usage of the oxygen flow control strategy that is implemented in EVE Figure 4: Illustration of the soft-sensor assisted automation strategy. End-of-batch is automatically detected. This information is used to trigger the start of exponential feeding and adaptation of bioprocess control parameters. 4

5 Fed-batch phase soft-sensor Prior starting the fed-batch soft-sensor, the optical density of the fermentation broth was measured to estimate the total amount of biomass (DCW) in the reactor. This amount was used to parametrize this soft-sensor. Furthermore, the soft-sensor needed information regarding the initial amount of biomass before fed-batch start, as well as, concentration and density of the feed medium. After starting the soft-sensor, it started immediately with real-time predictions of biomass (Figure 5), volumetric rates (Figure 6), and specific rates (Figure 7). The maximal volumetric oxygen uptake rate is an important criteria for the design of the large-scale reactor. Specific rates can be used to control processes based on physiological parameters, e.g. running experiments with controlled specific growth rates (µ) or specific substrate uptake rate (q s ). Figure 5 Estimated and predicted biomass concentrations in the fed-batch phase. The estimated biomass concentration is used to trigger the time-point of induction (1 st dashed arrow) and the time-point of harvest (2 nd arrow). Figure 6: Soft-sensor outputs of volumetric turnover rates. The maximum oxygen uptake rate (OUR) is highlighted with an arrow. Figure 7: Soft-sensor outputs of the biomass yield coefficient (Y X/S) and specific rates (q s, µ). The biomass yield coefficient is changing in the induction time as a result of the metabolic load due to recombinant protein production. 5

6 Analysis of accuracy Goal was to analyze the fed-batch phase softsensor in respect to prediction accuracy. Therefore, the soft-sensor was tested on 9 historical datasets of E. coli and P. Pastoris fedbatch-processes performed at Vienna University of technology. The predictions of the soft-sensor biomass were compared to the measured centrifuged offline-biomass samples. It can be seen, that there is a systematic overestimation of biomass. This becomes more apparent at the end of the processes or in the induction phase, where cell lysis leads to cell debris in the fermentation broth which cannot be separated through centrifugation. Figure 8: Relative difference between the biomass formation rate between soft-sensor estimation and offline values. Especially at the end of the process, the soft-sensor tends to overestimate the biomass. 6

7 Conclusions and Roadmap Precision, technological maturity and robustness of the investigated soft-sensors enable the industrial application of the investigated softsensors. The implementation in the Infors-HT Eve software enable scientists and operators to apply the soft-sensors in a powerful process automation framework. The soft-sensors allow scientists to gain more information out of the already available data. The information can be used to improve process understanding by the analysis of specific rates and yield coefficients as discussed in several recent publications. Furthermore the implemented softsensors can be used for simple bioprocess control strategies (control of specific growth rate or specific substrate uptake rate). Figure 9: The soft-sensors can be used in different biotechnological process phases. Benefits: Accelerating bioprocess development Figure 9 illustrated the broad application spectrum of the implemented microbial softsensors. Reduced effort for sampling due to robust estimation of biomass dry cell weight and specific rates and yield coefficients Efficient and accurate alternative to hardtype sensors for the estimation of the biomass concentration Simplified bioprocessing due to automatic detection of batch-end and automatic start of feeding Reduction of failed batches in bioprocess develop due to increased process transparency (early detection of sensor miscalibration. Faster evaluation of process data due to on-line calculation of specific rates and yield coefficients 7

8 purus et tincidunt. Lorem ipsum dolor sit amet, consectetur adipiscing elit. About Infors HT Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed at nulla eros. Vestibulum id purus erat. Mauris consequat, dui quis viverra cursus, erat libero ornare tortor, in molestie quam ex nec velit. Nulla nisi dolor, luctus sed interdum id, porta vitae nisi. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus eget semper massa. Fusce lacinia, felis nec dictum varius, massa mauris ultricies massa, at mattis nibh magna in lectus. Nam ipsum metus, efficitur consequat tortor in, condimentum ornare risus. Curabitur tristique sollicitudin velit, ac molestie ante egestas vitae. Aliquam porttitor nibh lacinia, volutpat risus rutrum, accumsan nisl. Nam auctor at purus et tincidunt. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed at nulla eros. Eve from Infors ABOUT THE EVE SOFTWARE About Exputec Exputec is a technology-driven consulting and software company delivering solutions for the biotech and chemical process industry. Exputec uniquely combines data science and engineering competencies to solve manufacturing challenges. Exputec data science services and software solutions provide efficient data science support for projects ranging from fast trouble shootings finished within weeks to sustainable data science support for process development, technology transfer and continuous process optimization. Exputec s proven data science workflows and engineering competencies ensure delivery of comprehensive results to the customer. Exputec s customers increase their competitive advantage by the elimination of root causes for process variance, efficient process optimization and the reduction of failed batches. Vestibulum id purus erat. Mauris consequat, dui quis viverra cursus, erat libero ornare tortor, in molestie quam ex nec velit. Nulla nisi dolor, luctus sed interdum id, porta vitae nisi. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Vivamus eget semper massa. Fusce lacinia, felis nec dictum varius, massa mauris ultricies massa, at mattis nibh magna in lectus. Nam ipsum metus, efficitur consequat tortor in, condimentum ornare risus. Curabitur tristique sollicitudin velit, ac molestie ante egestas vitae. Aliquam porttitor nibh lacinia, volutpat risus rutrum, accumsan nisl. Nam auctor at ABOUT Exputec Soft-sensor Technologies LinkedIn: 8