Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform.

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

Different modeling approaches for inline biochemical monitoring over the VLP-making upstream stages using Raman spectroscopy.

This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 106 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.

Development of DNA aptamers universally bound to single-chain fragment variables and their applications in bioprocess monitoring.

Single-chain fragment variables (scFvs), composed of variable heavy and light chains joined together by a peptide linker, can be produced using a cost-effective bacterial expression system, making them promising candidates for pharmaceutical applications. However, a versatile method for monitoring recombinant-protein production has not yet been developed. Herein, we report a novel anti-scFv aptamer-based biosensing system with high specificity and versatility. First, anti-scFv aptamers were screened using the competitive systematic evolution of ligands by exponential enrichment, focusing on a unique scFv-specific peptide linker. We selected two aptamers, P1-12 and P2-63, with KD = 2.1 µM or KD = 1.6 µM toward anti-human epidermal growth factor receptor (EGFR) scFv, respectively. These two aptamers can selectively bind to scFv but not to anti-EGFR Fv. Furthermore, the selected aptamers recognized various scFvs with different CDRs, such as anti-4-1BB and anti-hemoglobin scFv, indicating that they recognized a unique peptide linker region. An electrochemical sensor for anti-EGFR scFv was developed using anti-scFv aptamers based on square wave voltammetry. Thus, the constructed sensor could monitor anti-EGFR scFv concentrations in the range of 10-500 nM in a diluted medium for bacterial cultivation, which covered the expected concentration range for the recombinant production of scFvs. These achievements promise the realization of continuous monitoring sensors for pharmaceutical scFv, which will enable the real-time and versatile monitoring of large-scale scFv production.

An automatic glucose monitoring system based on periplasmic binding proteins for online bioprocess monitoring.

Glucose is one of the most vital nutrients in all living organisms, so its monitoring is critical in healthcare and bioprocessing. Enzymatic sensors are more popular as a technology solution to meet the requirement. However, periplasmic binding proteins have been investigated extensively for their high sensitivity, enabling microdialysis sampling to replace existing complex and expensive glucose monitoring solutions based on enzymatic sensors. The binding proteins are used as optical biosensors by introducing an environment-sensitive fluorophore to the protein. The biosensor's construction, characterization, and potential application are well studied, but a complete glucose monitoring system based on it is yet to be reported. This work documents the development of the first glucose sensor prototype based on glucose binding protein (GBP) for automatic and continuous glucose measurements. The development includes immobilizing the protein into reusable chips and a low-cost solution for non-invasive glucose sampling in bioprocesses using microdialysis sampling technique. A program was written in LabVIEW to accompany the prototype for the complete automation of measurement. The sampling technique allowed glucose measurements of a few micromolar to 260 mM glucose levels. A thorough analysis of the sampling mode and the device's performance was conducted. The reported measurement accuracy was 81.78%, with an RSD of 1.83%. The prototype was also used in online glucose monitoring of E. coli cell culture. The mode of glucose sensing can be expanded to the measurement of other analytes by switching the binding proteins.

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models.

The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy.

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

Feasibility and performance of cross-clone Raman calibration models in CHO cultivation.

Raman spectroscopy is widely used in monitoring and controlling cell cultivations for biopharmaceutical drug manufacturing. However, its implementation for culture monitoring in the cell line development stage has received little attention. Therefore, the impact of clonal differences, such as productivity and growth, on the prediction accuracy and transferability of Raman calibration models is not yet well described. Raman OPLS models were developed for predicting titer, glucose and lactate using eleven CHO clones from a single cell line. These clones exhibited diverse productivity and growth rates. The calibration models were evaluated for clone-related biases using clone-wise linear regression analysis on cross validated predictions. The results revealed that clonal differences did not affect the prediction of glucose and lactate, but titer models showed a significant clone-related bias, which remained even after applying variable selection methods. The bias was associated with clonal productivity and lead to increased prediction errors when titer models were transferred to cultivations with productivity levels outside the range of their training data. The findings demonstrate the feasibility of Raman-based monitoring of glucose and lactate in cell line development with high accuracy. However, accurate titer prediction requires careful consideration of clonal characteristics during model development.

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods.

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.

In-line monitoring of Bordetella pertussis cultivation using fluorescence spectroscopy.

Fluorescence spectroscopy is a non-invasive and highly sensitive method for bioprocess monitoring. The use of fluorescence spectroscopy is not very well established in the industry for in-line monitoring. In the present work, a 2-D fluorometer with two excitation lights (365 and 405 nm) and emission spectra in the range of 350-850 nm were used for in-line monitoring of two strains of Bordetella pertussis cultivation operated in batch and fed batch. A Partial Least Squares (PLS) based regression model was used for the estimation of cell biomass, amino acids (glutamate and proline) and antigen (Pertactin) produced. It was observed that accurate predictions were achieved when models were calibrated separately for each cell strain and nutrient media formulation. Also, prediction accuracy was improved when dissolved oxygen, agitation and culture volume are added as additional features in the regression model. The proposed approach of combining in-line fluorescence and other online measurements is shown to have good potential for in-line monitoring of bioprocesses.

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media.

In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

Biochemical monitoring throughout all stages of rabies virus-like particles production by Raman spectroscopy using global models.

This work aimed to quantify growth and biochemical parameters (viable cell density, Xv; cell viability, CV; glucose, lactate, glutamine, glutamate, ammonium, and potassium concentrations) in upstream stages to obtain rabies virus-like particles (rabies VLP) from insect cell-baculovirus system using on-line and off-line Raman spectra to calibrate global models with minimal experimental data. Five cultivations in bioreactor were performed. The first one comprised the growth of uninfected Spodoptera frugiperda (Sf9) cells, the second and third runs to obtain recombinant baculovirus (rBV) bearing Rabies G glycoprotein and matrix protein, respectively. The fourth one involved the generation of rabies VLP from rBVs and the last one was a repetition of the third one with cell inoculum infected by rBV. The spectra were acquired through a Raman spectrometer with a 785-nm laser source. The fitted Partial Least Square models for nutrients and metabolites were comparable with those previously reported for mammalian cell lines (Relative error < 15 %). However, the use of this chemometrics approach for Xv and CV was not as accurate as it was for other parameters. The findings from this work established the basis for bioprocess Raman spectroscopical monitoring using insect cells for VLP manufacturing, which are gaining ground in the pharmaceutical industry.

Sensitive real-time on-line estimator for oxygen transfer rates in fermenters.

Recombinant Escherichia coli grown in large-scale fermenters are used extensively to produce plasmids and biopharmaceuticals. One method commonly used to control culture growth is predefined glucose feeding, often an exponential feeding profile. Predefined feeding profiles cannot adjust automatically to metabolic state changes, such as the metabolic burden associated with recombinant protein expression or high-cell density associated stresses. As the culture oxygen consumption rates indicates a culture's metabolic state, there exist several methods to estimate the oxygen uptake rate (OUR). These common OUR methods have limited application since these approaches either disrupt the oxygen supply, rely on empirical relationships, or are unable to account for latency and filtering effects. In this study, an oxygen transfer rate (OTR) estimator was developed to aid OUR prediction. This non-disruptive OTR estimator uses the dissolved oxygen and the off-gas oxygen concentration, in parallel. This new OTR estimator captures small variations in OTR due to physical and chemical manipulations of the fermenter, such as in stir speed variation, glucose feeding rate change, and recombinant protein expression. Due its sensitivity, this non-disruptive real-time OTR estimator could be integrated with feed control algorithms to maintain the metabolic state of a culture to a desired setpoint.

A Simplified Algorithm for Setting the Observer Parameters for Second-Order Systems with Persistent Disturbances Using a Robust Observer.

The properties of the convergence region of the estimation error of a robust observer for second-order systems are determined, and a new algorithm is proposed for setting the observer parameters, considering persistent but bounded disturbances in the two observation error dynamics. The main contributions over closely related studies of the stability of state observers are: (i) the width of the convergence region of the observer error for the unknown state is expressed in terms of the interaction between the observer parameters and the disturbance terms of the observer error dynamics; (ii) it was found that this width has a minimum point and a vertical asymptote with respect to one of the observer parameters, and their coordinates were determined. In addition, the main advantages of the proposed algorithm over closely related algorithms are: (i) the definition of observer parameters is significantly simpler, as the fulfillment of Riccati equation conditions, solution of LMI constraints, and fulfillment of eigenvalue conditions are not required; (ii) unknown bounded terms are considered in the dynamics of the observer error for the known state. Finally, the algorithm is applied to a model of microalgae culture in a photobioreactor for the estimation of biomass growth rate and substrate uptake rate based on known concentrations of biomass and substrate.

NMR as powerful technology for noninvasively monitoring cell health and expansion during bioprocessing.

Over the last decades, the success of advanced cell therapies and the increasing production volumes of vaccines, proteins, or viral vectors have raised the need of robust cell-based manufacturing processes for ensuring product quality and satisfying good manufacturing practice requirements. The cultivation process of cells needs to be highly controlled for improved productivity, reduced variability, and optimized bioprocesses. Cell cultures can be easily monitored using different technologies, which could deliver direct or indirect assessment of the cells' viability. Among these techniques, nuclear magnetic resonance (NMR) spectroscopy is a powerful technology that permits the evaluation and the identification of key endogenous metabolites. NMR can provide information on the cell metabolic pathways, on the bioprocesses, and is also capable to quickly test for impurities. In this study, NMR was successfully used as a technology for monitoring cell viability and expansion in different supports for cell growth (including bioreactors), to predict the bioprocess output and for the early identification of key metabolites linked to cell starvation. This investigation will allow the timely control of culture conditions and favor the optimization of the bioprocesses.

On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.

The real-time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on-line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)-2,3-dihydroxy-isovalerate and (R)-2,3-dihydroxy-3-methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes.

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

Capillary and microchip electrophoresis method development for amino acid monitoring during biopharmaceutical cultivation.

The increased use of biopharmaceuticals calls for improved means of bioprocess monitoring. In this work, capillary electrophoresis (CE) and microchip electrophoresis (MCE) methods were developed and applied for the analysis of amino acids (AAs) in cell culture supernatant. In samples from different days of a Chinese hamster ovary cell cultivation process, all 19 proteinogenic AAs containing primary amine groups could be detected using CE, and 17 out of 19 AAs using MCE. The relative concentration changes in different samples agreed well with those measured by high-performance liquid chromatography (HPLC). Compared to the more commonly employed HPLC analysis, the CE and MCE methods resulted in faster analysis, while significantly lowering both the sample and reagent consumption, and the cost per analysis.

Bioanalytics for Influenza Virus-Like Particle Characterization and Process Monitoring.

Virus-like particles (VLPs) are excellent platforms for the development of influenza vaccine candidates. Nonetheless, their characterization is challenging due to VLPs' unique biophysical and biochemical properties. To cope with such complexity, multiple analytical techniques have been developed to date (e.g., single-particle analysis, thermal stability, or quantification assays), most of which are rarely used or have been successfully demonstrated for being applicable for virus particle characterization. In this study, several biophysical and biochemical methods have been evaluated for thorough characterization of monovalent and pentavalent influenza VLPs from diverse groups (A and B) and subtypes (H1 and H3) produced in insect cells using the baculovirus expression vector system (IC-BEVS). Particle size distribution and purity profiles were monitored during the purification process using two complementary technologies - nanoparticle tracking analysis (NTA) and tunable resistive pulse sensing (TRPS). VLP surface charge at the selected process pH was also assessed by this last technique. The morphology of the VLP (size, shape, and presence of hemagglutinin spikes) was evaluated using transmission electron microscopy. Circular dichroism was used to assess VLPs' thermal stability. Total protein, DNA, and baculovirus content were also assessed. All VLPs analyzed exhibited similar size ranges (90-115 nm for NTA and 129-141 nm for TRPS), surface charges (average of -20.4 mV), and morphology (pleomorphic particles resembling influenza virus) exhibiting the presence of HA molecules (spikes) uniformly displayed on M1 protein scaffold. Our data shows that HA titers and purification efficiency in terms of impurity removal and thermal stability were observed to be particle dependent. This study shows robustness and generic applicability of the tools and methods evaluated, independent of VLP valency and group/subtype. Thus, they are most valuable to assist process development and enhance product characterization.

Secretomics: a biochemical footprinting tool for developing microalgal cultivation strategies.

Microalgae offer a promising source of biofuel and a wide array of high-value biomolecules. Large-scale cultivation of microalgae at low density poses a significant challenge in terms of water management. High-density microalgae cultivation, however, can be challenging due to biochemical changes associated with growth dynamics. Therefore, there is a need for a biomarker that can predict the optimum density for high biomass cultivation. A locally isolated microalga Cyanobacterium aponinum CCC734 was grown with optimized nitrogen and phosphorus in the ratio of 12:1 for sustained high biomass productivity. To understand density-associated bottlenecks secretome dynamics were monitored at biomass densities from 0.6 ± 0.1 to 7 ± 0.1 g/L (2 to 22 OD) in batch mode. Liquid chromatography coupled with mass spectrometry identified 880 exometabolites in the supernatant of C. aponinum CCC734. The PCA analysis showed similarity between exometabolite profiles at low (4 and 8 OD) and mid (12 and 16 OD), whereas distinctly separate at high biomass concentrations (20 and 22 OD). Ten exometabolites were selected based on their role in influencing growth and are specifically present at low, mid, and high biomass concentrations. Taking cues from secretome dynamics, 5.0 ± 0.5 g/L biomass concentration (16 OD) was optimal for C. aponinum CCC734 cultivation. Further validation was performed with a semi-turbidostat mode of cultivation for 29 days with a volumetric productivity of 1.0 ± 0.2 g/L/day. The secretomes-based footprinting tool is the first comprehensive growth study of exometabolite at the molecular level at variable biomass densities. This tool may be utilized in analyzing and directing microalgal cultivation strategies and reduction in overall operating costs.