Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams.

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

Maduramycin, a novel glycosylation modulator for mammalian fed-batch and steady-state perfusion processes.

Controlling high-mannose (HM) content of therapeutic proteins during process intensification, reformulation for subcutaneous delivery, antibody-drug conjugate or biosimilar manufacturing represents an ongoing challenge. Even though a range of glycosylation levers to increase HM content exist, modulators specially increasing M5 glycans are still scarce. Several compounds of the polyether ionophore family were screened for their ability to selectively increase M5 glycans of mAb products and compared to the well-known α-mannosidase I inhibitor kifunensine known to increase mainly M8-M9 glycans. Maduramycin, amongst other promising polyether ionophores, showed the desired effect on different cell lines. For fed-batch processes, a double bolus addition modulator feed strategy was developed maximizing the effect on glycosylation by minimizing impact on culture performance. Further, a continuous feeding strategy for steady-state perfusion processes was successfully developed, enabling consistent product quality at elevated HM glycan levels. With kifunensine and maduramycin showing inverse effects on the relative HM distribution, a combined usage of these modulators was further evaluated to fine-tune a desired HM glycan pattern. The discovered HM modulators expand the current HM modulating toolbox for biotherapeutics. Their application not only for fed-batch processes, but also steady-state perfusion processes, make them a universal tool with regards to fully continuous manufacturing processes.

Raman-controlled pyruvate feeding to control metabolic activity and product quality in continuous biomanufacturing.

BACKGROUND: Despite technological advances ensuring stable cell culture perfusion operation over prolonged time, reaching a cellular steady-state metabolism remains a challenge for certain manufacturing cell lines. This study investigated the stabilization of a steady-state perfusion process producing a bispecific antibody with drifting product quality attributes, caused by shifting metabolic activity in the cell culture. MAIN METHODS: A novel on-demand pyruvate feeding strategy was developed, leveraging lactate as an indicator for tricarboxylic acid (TCA) cycle saturation. Real-time lactate monitoring was achieved through in-line Raman spectroscopy, enabling accurate control at predefined target setpoints. MAJOR RESULTS: The implemented feedback control strategy resulted in a three-fold reduction of ammonium accumulation and stabilized product quality profiles. Stable and flat glycosylation profiles were achieved with standard deviations below 0.2% for high mannose and fucosylation. Whereas galactosylation and sialylation were stabilized in a similar manner, varying lactate setpoints might allow for fine-tuning of these glycan forms. IMPLICATION: The Raman-controlled pyruvate feeding strategy represents a valuable tool for continuous manufacturing, stabilizing metabolic activity, and preventing product quality drifting in perfusion cell cultures. Additionally, this approach effectively reduced high mannose, helping to mitigate increases associated with process intensification, such as extended culture durations or elevated culture densities.

Co-current filtrate flow in TFF perfusion processes: Decoupling transmembrane pressure from crossflow to improve product sieving.

Hollow fiber-based membrane filtration has emerged as the dominant technology for cell retention in perfusion processes yet significant challenges in alleviating filter fouling remain unsolved. In this work, the benefits of co-current filtrate flow applied to a tangential flow filtration (TFF) module to reduce or even completely remove Starling recirculation caused by the axial pressure drop within the module was studied by pressure characterization experiments and perfusion cell culture runs. Additionally, a novel concept to achieve alternating Starling flow within unidirectional TFF was investigated. Pressure profiles demonstrated that precise flow control can be achieved with both lab-scale and manufacturing-scale filters. TFF systems with co-current flow showed up to 40% higher product sieving compared to standard TFF. The decoupling of transmembrane pressure from crossflow velocity and filter characteristics in co-current TFF alleviates common challenges for hollow fiber-based systems such as limited crossflow rates and relatively short filter module lengths, both of which are currently used to avoid extensive pressure drop along the filtration module. Therefore, co-current filtrate flow in unidirectional TFF systems represents an interesting and scalable alternative to standard TFF or alternating TFF operation with additional possibilities to control Starling recirculation flow.

Temperature Upshifts in Mammalian Cell Culture: A Suitable Strategy for Biosimilar Monoclonal Antibodies?

Temperature downshifts are the gold standard when setting up control strategies for mammalian cell culture processes. These shifts are performed to prolong production phases and attain heightened levels of productivity. For the development of biosimilars, however, the bottleneck is in achieving a prespecified product quality. In a late-stage development project, we investigated the impact of temperature shifts and other process parameters with the aim of optimizing the glycosylation profile of a monoclonal antibody (mAb). We applied a design of experiments approach on a 3 L scale. The optimal glycosylation profile was achieved when performing a temperature upshift from 35.8 °C to 37 °C. Total afucosylated glycan (TAF) decreased by 1.2%, and galactosylated glycan species (GAL) increased by up to 4.5%. The optimized control strategy was then successfully taken to the manufacturing scale (1000 L). By testing two sets of set points at the manufacturing scale, we demonstrated that the statistical models predicting TAF and GAL trained with small-scale data are representative of the manufacturing scale. We hope this study encourages researchers to widen the screening ranges in process development and investigate whether temperature upshifts are also beneficial for other mAbs.

A Unique Response Behavior in the Dissolved Oxygen Tension in E. coli Minibioreactor Cultivations with Intermittent Feeding.

Intermittent bolus feeding for E. coli cultivations in minibioreactor systems (MBRs) profoundly affects the cell metabolism. Bolus feeding leads to temporal substrate surplus and transient oxygen limitation, which triggers the formation of inhibitory byproducts. Due to the high oxygen demand right after the injection of the substrate, the dissolved oxygen tension (DOT) signal exhibits a negative pulse. This contribution describes and analyzes this DOT response in E. coli minibioreactor cultivations. In addition to gaining information on culture conditions, a unique response behavior in the DOT signal was observed in the analysis. This response appeared only at a dilution ratio per biomass unit higher than a certain threshold. The analysis highlights a plausible relationship between a metabolic adaptation behavior and the newly observed DOT signal segment not reported in the literature. A hypothesis that links particular DOT segments to specific metabolic states is proposed. The quantitative analysis and mechanistic model simulations support this hypothesis and show the possibility of obtaining cell physiological and growth parameters from the DOT signal.

Holistic Design of Experiments Using an Integrated Process Model.

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality.

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications.

Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output-input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle.

Specification-driven acceptance criteria for validation of biopharmaceutical processes.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

Improved Time Resolved KPI and Strain Characterization of Multiple Hosts in Shake Flasks Using Advanced Online Analytics and Data Science.

Shake flasks remain one of the most widely used cultivation systems in biotechnology, especially for process development (cell line and parameter screening). This can be justified by their ease of use as well as their low investment and running costs. A disadvantage, however, is that cultivations in shake flasks are black box processes with reduced possibilities for recording online data, resulting in a lack of control and time-consuming, manual data analysis. Although different measurement methods have been developed for shake flasks, they lack comparability, especially when changing production organisms. In this study, the use of online backscattered light, dissolved oxygen, and pH data for characterization of animal, plant, and microbial cell culture processes in shake flasks are evaluated and compared. The application of these different online measurement techniques allows key performance indicators (KPIs) to be determined based on online data. This paper evaluates a novel data science workflow to automatically determine KPIs using online data from early development stages without human bias. This enables standardized and cost-effective process-oriented cell line characterization of shake flask cultivations to be performed in accordance with the process analytical technology (PAT) initiative. The comparison showed very good agreement between KPIs determined using offline data, manual techniques, and automatic calculations based on multiple signals of varying strengths with respect to the selected measurement signal.

Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries.

BACKGROUND: Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross-correlation of various media components and artifacts throughout the process. MAIN METHODS: A systematic Raman calibration workflow for perfusion processes enabling highly specific and fast model calibration was developed. Harvest libraries consisting of frozen harvest samples from multiple CHO cell culture bioreactors collected at different process times were established. Model calibration was subsequently performed in an offline setup using a flow cell by spiking process harvest with glucose, raffinose, galactose, mannose, and fructose. MAJOR RESULTS: In a screening phase, Raman spectroscopy was proven capable not only to distinguish sugars with similar chemical structures in perfusion harvest but also to quantify them independently in process-relevant concentrations. In a second phase, a robust and highly specific calibration model for simultaneous glucose (root mean square error prediction [RMSEP] = 0.32 g L-1 ) and raffinose (RMSEP = 0.17 g L-1 ) real-time monitoring was generated and verified in a third phase during a perfusion process. IMPLICATION: The proposed novel offline calibration workflow allowed proper Raman peak decoupling, reduced calibration time from months down to days, and can be applied to other analytes of interest including lactate, ammonia, amino acids, or product titer.

Corrigendum: Reducing Organic Load From Industrial MDA Residual Process Brine With a Novel Halophilic Mixed Culture: Scale-Up and Long-Term Piloting of an Integrated Bioprocess.

[This corrects the article DOI: 10.3389/fbioe.2022.896576.].

At-line quantitative profiling of monoclonal antibody products during bioprocessing using HPLC-MS.

(1) Background: The N-glycosylation profile as well as the subunit assembly of monoclonal antibodies (mAbs) are strongly dependent on manufacturing conditions and thus need to be monitored during the bioprocess. Commonly, mAbs are characterized downstream of the fermentation process applying different analytical techniques like released glycan analysis, peptide mapping, or subunit profiling. However, these procedures are time-consuming and difficult to perform in real-time. (2) Methods: We applied a simple HPLC-MS workflow with minimal sample preparation to characterize mAb product quality at the intact protein level at different time points during fermentation. After harvest, the cell culture medium was centrifuged briefly. The supernatant containing the fermentation product was diluted and immediately subjected to HPLC-MS analysis. (3) Results: Besides the product of interest (mAb), the fermentation broth contained misassembled variants, mostly light chain and light chain dimer. The mAb's glycosylation profile changed over time showing an increase in galactosylated variants with G0F/G1F being the most abundant glycoform at all time points of fermentation. Furthermore, expressed protein species were relatively and absolutely quantified. The workflow was very robust despite analyzing a highly complex matrix. Relative standard deviations for retention times were below 0.5% for both intra and inter-day comparison, whereas relative procedural standard deviations for quantification of the different protein species ranged between 7 and 13%. (4) Conclusions: This approach allows for reliable analysis of product profiles of monoclonal antibody species including misassembled subunits and glycosylation variants directly from fermentation broth using a fast and robust HPLC-MS workflow.

Reducing Organic Load From Industrial Residual Process Brine With a Novel Halophilic Mixed Culture: Scale-Up and Long-Term Piloting of an Integrated Bioprocess.

Integrating bioprocess solutions for treatment and subsequent reuse of saline residual process brine into industrial processes could increase the sustainability of production chains. However, such bioprocesses require large-scales and a robust operation over a prolonged period. Consequently, the aim of this study was to analyze scale-up equivalence as well as continuous and stable process performance of a previously established lab scale process for the degradation of organic contaminants (formate and aromatic compounds) in an industrial context. To that end, a pilot-scale bubble column bioreactor system equipped with a membrane-based cell retention system for process intensification was integrated at an industrial production site. The process was successfully scaled-up and continuously operated for more than 210 days. Overall, the process proved to be robust towards changing compositions of the residual process brine stream and degradation rates for organic contaminants were close to 100%. Interestingly, due to the unsterile process conditions, the original Haloferax mediterranei culture was replaced by a novel halophilic bacterial community consisting of three bacterial genera. To further improve process economics and productivity, an optimization of the co-substrate feeding strategy for glycerol is required, as results indicated a potential correlation between glycerol feeding and formate degradation rates. To that end, decoupling of the glycerol feeding from the residual process brine feed is a potential way to increase process control options and allow for easy adaptation of the process to changing residual process brine compositions. Ultimately, the process described here could be a promising alternative for chemical or physical methods of treating residual process brine and once more underlines the potential to exploit natural microbial diversity for industrial purposes.

Mixotrophic co-utilization of glucose and carbon monoxide boosts ethanol and butanol productivity of continuous Clostridium carboxidivorans cultures.

In this study, continuous cultivations of C.carboxidivorans to study heterotrophic and mixotrophic conversion of glucose and H2, CO2, and CO were established. Glucose fermentations at pH 6 showed a high ratio of alcohol-to-acid production of 2.79 mol mol-1. While H2 or CO2 were not utilized together with glucose, CO feeding drastically increased the combined alcohol titer to 9.1 g l-1. Specifically, CO enhanced acetate (1.9-fold) and ethanol (1.7-fold) production and triggered chain elongation to butanol (1.5-fold) production but did not change the alcohol:acid ratio. Flux balance analysis showed that CO served both as a carbon and energy source, and CO mixotrophy displayed a carbon and energy efficiency of 45 and 77%, respectively. This study expands the knowledge on physiology and metabolism of C.carboxidivorans and can serve as the starting point for rational engineering and process intensification to establish efficient production of alcohols and acids from carbon waste.

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture.

Model-based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time-variable metabolic capacities that lead to a considerable model-plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model-based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off-gas measurements. Additional information on current parameter importance is derived from time-resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long-term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify time-variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by-product streams.

Integrated Process Model Applications Linking Bioprocess Development to Quality by Design Milestones.

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses.

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models.

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of Escherichia coli (E. coli) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.

Establishing recombinant production of pediocin PA-1 in Corynebacterium glutamicum.

Bacteriocins are antimicrobial peptides produced by bacteria to inhibit competitors in their natural environments. Some of these peptides have emerged as commercial food preservatives and, due to the rapid increase in antibiotic resistant bacteria, are also discussed as interesting alternatives to antibiotics for therapeutic purposes. Currently, commercial bacteriocins are produced exclusively with natural producer organisms on complex substrates and are sold as semi-purified preparations or crude fermentates. To allow clinical application, efficacy of production and purity of the product need to be improved. This can be achieved by shifting production to recombinant microorganisms. Here, we identify Corynebacterium glutamicum as a suitable production host for the bacteriocin pediocin PA-1. C. glutamicum CR099 shows resistance to high concentrations of pediocin PA-1 and the bacteriocin was not inactivated when spiked into growing cultures of this bacterium. Recombinant C. glutamicum expressing a synthetic pedACDCgl operon releases a compound that has potent antimicrobial activity against Listeria monocytogenes and Listeria innocua and matches size and mass:charge ratio of commercial pediocin PA-1. Fermentations in shake flasks and bioreactors suggest that low levels of dissolved oxygen are favorable for production of pediocin. Under these conditions, however, reduced activity of the TCA cycle resulted in decreased availability of the important pediocin precursor l-asparagine suggesting options for further improvement. Overall, we demonstrate that C. glutamicum is a suitable host for recombinant production of bacteriocins of the pediocin family.