Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

Real-time monitoring of quality attributes by in-line Fourier transform infrared spectroscopic sensors at ultrafiltration and diafiltration of bioprocess

Technologies capable of monitoring product quality attributes and process parameters in real time are becoming popular due to the endorsement of regulatory agencies and also to support the agile development of biotherapeutic pipelines. The utility of vibrational spectroscopic techniques such as Fourier transform mid-infrared (Mid-IR) and multivariate data analysis (MVDA) models allows the prediction of multiple critical attributes simultaneously in real time. This study reports the use of Mid-IR and MVDA model sensors for monitoring of multiple attributes (excipients and protein concentrations) in real time (measurement frequency of every 40 s) at ultrafiltration and diafiltration (UF/DF) unit operation of biologics manufacturing. The platform features integration of fiber optic Mid-IR probe sensors to UF/DF set up at the bulk solution and through a flow cell at the retentate line followed by automated Mid-IR data piping into a process monitoring software platform with pre-loaded partial least square regression (PLS) chemometric models. Data visualization infrastructure is also built-in to the platform so that upon automated PLS prediction of excipients and protein concentrations, the results were projected in a graphical or numerical format in real time. The Mid-IR predicted concentrations of excipients and protein show excellent correlation with the offline measurements by traditional analytical methods. Absolute percent difference values between Mid-IR predicted results and offline reference assay results were ≤5% across all the excipients and the protein of interest; which shows a great promise as a reliable process analytical technology tool.     

Application of Raman spectroscopy in monoclonal antibody producing continuous systems for downstream process intensification

Monoclonal antibodies (mAbs) are biopharmaceuticals produced by mammalian cell lines in bioreactors at a variety of scales. Cell engineering, media optimization, process monitoring, and control strategies for in vitro production have become crucial subjects to meet increasing demand for these high value pharmaceuticals. Raman Spectroscopy has gained great attention in the pharmaceutical industry for process monitoring and control to maintain quality assurance. For the first time, this article demonstrated the possibility of subclass independent quantitative mAb prediction by Raman spectroscopy in real time. The developed model estimated the concentrations of different mAb isotypes with average prediction errors of 0.2 (g/L) over the course of cell culture. In situ Raman spectroscopy combined with chemometric methods showed to be a useful predictive tool for monitoring of real time mAb concentrations in a permeate stream without sample removal. Raman spectroscopy can, therefore, be considered as a reliable process analytical technology tool for process monitor, control, and intensification of downstream continuous manufacturing. The presented results provide useful information for pharmaceutical industries to choose the most appropriate spectroscopic technology for their continuous processes.     

Hybrid modeling framework for process analytical technology: application to Bordetella pertussis cultures

In the process analytical technology (PAT) initiative, the application of sensors technology and modeling methods is promoted. The emphasis is on Quality by Design, online monitoring, and closed-loop control with the general aim of building in product quality into manufacturing operations. As a result, online high-throughput process analyzers find increasing application and therewith high amounts of highly correlated data become available online. In this study, an hybrid chemometric/mathematical modeling method is adopted for data analysis, which is shown to be advantageous over the commonly used chemometric techniques in PAT applications. This methodology was applied to the analysis of process data of Bordetella pertussis cultivations, namely online data of near-infrared, (NIR), pH, temperature and dissolved oxygen, and off-line data of biomass, glutamate, and lactate concentrations. The hybrid model structure consisted of macroscopic material balance equations in which the specific reactions rates are modeled by nonlinear partial least square (PLS). This methodology revealed a significant higher statistical confidence in comparison to PLSs, translated in a reduction of mean squared prediction errors (e.g., individual root mean squared prediction errors calibration/validation obtained through the hybrid model for the concentrations of lactate: 0.8699/0.7190 mmol/L; glutamate: 0.6057/0.2917 mmol/L; and biomass: 0.0520/0.0283 OD; and obtained through the PLS model for the concentrations of lactate: 1.3549/1.0087 mmol/L; glutamate: 0.7628/0.3504 mmol/L; and biomass: 0.0949/0.0412 OD). Moreover, the analysis of loadings and scores in the hybrid approach revealed that process features can, as for PLS, be extracted by the hybrid method.     

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.     

Real‐time monitoring and control of the load phase of a protein A capture step

The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.     

Real‐time monitoring of high‐gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real‐time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm^?1 C–C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high‐ and low‐concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two‐set calibration models for starch (R² = 0.998, percent error = 2.5%) and ethanol (R² = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS‐based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman‐based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Biotechnol. Bioeng. 2013; 110: 1654–1662. © 2013 Wiley Periodicals, Inc.     

Chemometric modelling based on 2D-fluorescence spectra without a calibration measurement

MOTIVATION: 2D fluorescence spectra provide information from intracellular compounds. Fluorophores like trytophan, tyrosine and phenylalanin as well as NADH and flavins make the corresponding measurement systems very important for bioprocess supervision and control. The evaluation is usually based on chemometric modelling using for their calibration procedure off-line measurements of the desired process variables. Due to the data driven approach lots of off-line measurements are required. Here a methodology is presented, which enables to perform a calibration procedure of chemometric models without any further measurement. RESULTS: The necessary information for the calibration procedure is provided by means of the a priori knowledge about the process, i.e. a mathematical model, whose model parameters are estimated during the calibration procedure, as well as the fact that the substrate should be consumed at the end of the process run. The new methodology for chemometric calibration is applied for a batch cultivation of aerobically grown S. cerevisiae on the glucose Schatzmann medium. As will be presented the chemometric models, which are determined by this method, can be used for prediction during new process runs. AVAILABILITY: The MATHLAB routine is free available on request from the authors.     

Implementation of chemometrics,design of experiments,and neural network analysis for prior process knowledge assessment,failure modes and effect analysis,scale-down model development,and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.     

Analysis and purification of IgG4 bispecific antibodies by a mixed-mode chromatography

Therapeutic non-hinge-modified IgG4 molecules form bispecific hybrid antibodies with endogenous human IgG4 molecules via a process known as Fab-arm exchange (or called half molecule exchange). Analysis of the bispecific hybrids is critical for studies of half molecule exchange. A number of analytical methods are available to detect IgG4 hybrids. These methods mostly necessitate labeling or alteration of the model IgG4 molecules, or rely on time-consuming immunoassays and mass spectrometry. In addition, these methods do not allow isolation of hybrid antibodies. We report here the only analytical method to date that relies on chromatographic separation for detection of hybrids formed from intact antibodies in their native forms using pembrolizumab as an example. This method employs a mixed-mode chromatography using a Sepax Zenix SEC-300 column to separate a bispecific hybrid from the parental antibodies. The simultaneous quantitative monitoring of the newly formed hybrid and parental antibodies was achieved by UV absorption and/or protein fluorescence. The bispecific hybrid antibodies were purified with the same method for further biochemical characterization. The method has allowed monitoring of half molecule exchange between a human serum IgG4 and a tested IgG4 molecule, and has been implemented for the analysis of in vitro as well as in vivo samples.     

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.     

State analysis of fermentation using a mass spectrometer with membrane probe

A new method of continuous process analysis in fermentation using a mass spectrometer (MS) membrane probe is described. A number of samples from industrial fermentations were analyzed for the occurrence of volatiles detectable with a silicone membrane probe connected to a quadrupole MS. In all fermentations, characteristic spectra were observed which were found to change systematically during the batch process. Factor analysis was used to treat the data. The factor scores were compared with the actual product concentrations (antibiotics, toxins, etc.), which were measured using other analytical methods and were found to correlate with them. On-line analysis was also carried out on a fermentation with an MS and an Apple II microcomputer. Direct monitoring of products, which are not directly measurable with the membrane MS probe requires a new calibration each time conditions such as medium composition are changed.     

Biopharmaceutical production: Applications of surface plasmon resonance biosensors

Surface plasmon resonance (SPR) permits the quantitative analysis of therapeutic antibody concentrations and impurities including bacteria, Protein A, Protein G and small molecule ligands leached from chromatography media. The use of surface plasmon resonance has gained popularity within the biopharmaceutical industry due to the automated, label free, real time interaction that may be exploited when using this method. The application areas to assess protein interactions and develop analytical methods for biopharmaceutical downstream process development, quality control, and in-process monitoring are reviewed.     

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.     

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed‐batch fermentations

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical “quality by testing” to rational, knowledge‐based “quality by design” approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real‐time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed‐batch production processes with varying cultivation conditions employing a comprehensive on‐ and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two‐dimensional multi‐wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.     

Microbial fuel cell based biosensor for in situ monitoring of anaerobic digestion process

A wall-jet microbial fuel cell (MFC) was developed for the monitoring of anaerobic digestion (AD). This biofilm based MFC biosensor had a character of being portable, short hydraulic retention time (HRT) for sample flow through and convenient for continuous operation. The MFC was installed in the recirculation loop of an upflow anaerobic fixed-bed (UAFB) reactor in bench-scale where pH of the fermentation broth and biogas flow were monitored in real time. External disturbances to the AD were added on purpose by changing feedstock concentration, as well as process configuration. MFC signals had good correlations with online measurements (i.e. pH, gas flow rate) and offline analysis (i.e. COD) over 6-month operation. These results suggest that the MFC signal can reflect the dynamic variation of AD and can potentially be a valuable tool for monitoring and control of bioprocess.     

Process analytical technology case study,part III: Calibration monitoring and transfer

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T² and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.     

Determinations of fluoroquinolones and nonsteroidal anti-inflammatory drugs in urine by extractive spectrophotometry and photoinduced spectrofluorimetry using multivariate calibration

Multivariate calibration methods are chemometric tools that may be applied to the analysis of spectroscopic data with multichannel detection. Two procedures, based on spectrophotometric and fluorimetric signals, are reported for the simultaneous determination of two fluoroquinolones (ciprofloxacin and ofloxacin) and two nonsteroidal anti-inflammatory drugs (diclofenac and mefenamic acid) using first- and second-order multivariate calibration methods. In the spectrophotometric method, an extractive procedure into chloroform using trioctylmethylammonium chloride-adogen as counter ion was optimized, with the object of extracting the analytes from urine samples and eliminating matrix interferences. After separation, the absorption spectrum of the organic phase was used as the analytical signal in a partial least squares method. A photoinduced spectrofluorimetric (PIF) method using excitation-emission fluorescence matrices, is proposed, to apply three-way chemometric calibration, with the aim of analyzing ofloxacin, ciprofloxacin, and diclofenac in urine samples without the previous extractive sample-cleaning step. For both procedures, recoveries around 100% were found for all the analytes. However, the PIF three-way chemometric method provides the most sensitive and selective procedure as the urine interferences are modulated using the three-way chemometric technique.     

Quantification of glycated IgG in CHO supernatants: A practical approach

Post-translational, nonenzymatic glycation of monoclonal antibodies (mAbs) in the presence of reducing sugars (in bioprocesses) is a widely known phenomenon, which affects protein heterogeneity and potentially has an impact on quality, safety, and efficacy of the end product. Quantification of individual glycation levels is compulsory for each mAb therapeutically applied in humans. We therefore propose an analytical method for monitoring glycation levels of mAb products during the bioprocess. This is a useful tool for process-design considerations, especially concerning glucose-feed strategies and temperature as major driving factors of protein glycation. In this study, boronate affinity chromatography (BAC) was optimized for determination of the glycation level of mAbs in supernatants. In fact, the complex matrix found in supernatants is an underlying obstacle to use BAC, but with a simple clean-up step, we found that the elution profile could be significantly improved so that qualitative and quantitative determination could be reached. Complementary analytical methods confirmed the performance quality, including the correctness and specificity of the results. For quantitative determination of mAb glycation in supernatants, we established a calibration procedure for the retained mAb peak, identified as glycated antibody monomers. For this approach, an available fully characterized mAb standard, Humira®, was successfully applied, and continuous monitoring of mAbs across three repetitive fed-batch processes was finally performed. With this practical, novel approach, an insight was obtained into glycation levels during bioprocessing, in conjunction with glucose levels and product titer over time, facilitating efficient process development and batch-consistency monitoring.     

Integrated continuous biomanufacturing platform with ATF perfusion and one column chromatography operation for optimum resin utilization and productivity

A new integrated continuous biomanufacturing platform for continuous production of antibodies at fixed cell volumes and cell concentrations for extended periods with immediate capture is presented. Upstream antibody production has reached technological maturity, however, the bottleneck for continuous biomanufacturing remains the efficient and cost-effective capture of therapeutic antibodies in an initial chromatography step. In this study, the first successful attempt at using one-column continuous chromatography (OCC) for the continuous capture of therapeutic antibodies produced through alternating tangential flow perfusion is presented. By performing upstream media optimizations, the upstream perfusion rate was reduced to one vessel volume per day (vv/d), increasing antibody titer and reducing the volume of perfusate. In addition, process improvements were performed to increase productivity by 80% over previously reported values. In addition, a real-time method for evaluating column performance to make column switching decisions was developed. This improved productivity coupled with the use of a single-column improved process monitoring and control in OCC compared to multi-column systems. This approach is the first report on using a single column for the implementation of an integrated continuous biomanufacturing platform and offers a cost-effective and flexible platform process for the manufacture of therapeutic proteins.