In-line monitoring of protein concentration with MIR spectroscopy during UFDF

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real-time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid-infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in-line monitoring, Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in-line fashion through a single-use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one-point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.     

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.     

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Multi‐component, multi‐scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single‐scale and combination‐scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single‐scale and combination‐scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale‐dependencies that hindered model quality in cross‐scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross‐scale applications predicting against manufacturing scale batches. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:566–577, 2015     

Real‐time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures

The glycosylation of therapeutic monoclonal antibodies (mAbs), a known critical quality attribute, is often greatly modified during the production process by animal cells. It is essential for biopharmaceutical industries to monitor and control this glycosylation. However, current glycosylation characterization techniques involve time‐ and labor‐intensive analyses, often carried out at the end of the culture when the product is already synthesized. This study proposes a novel methodology for real‐time monitoring of antibody glycosylation site occupancy using Raman spectroscopy. It was first observed in CHO cell batch culture that when low nutrient concentrations were reached, a decrease in mAb glycosylation was induced, which made it essential to rapidly detect this loss of product quality. By combining in situ Raman spectroscopy with chemometric tools, efficient prediction models were then developed for both glycosylated and nonglycosylated mAbs. By comparing variable importance in projection profiles of the prediction models, it was confirmed that Raman spectroscopy is a powerful method to distinguish extremely similar molecules, despite the high complexity of the culture medium. Finally, the Raman prediction models were used to monitor batch and feed‐harvest cultures in situ. For the first time, it was demonstrated that the concentrations of glycosylated and nonglycosylated mAbs could be successfully and simultaneously estimated in real time with high accuracy, including their sudden variations due to medium exchanges. Raman spectroscopy can thus be considered as a promising PAT tool for feedback process control dedicated to on‐line optimization of mAb quality. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:486–493, 2018     

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.     

Generic Raman‐based calibration models enabling real‐time monitoring of cell culture bioreactors

Raman‐based multivariate calibration models have been developed for real‐time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO‐based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1004–1013, 2015     

At-line raman spectroscopy and design of experiments for robust monitoring and control of miniature bioreactor cultures

The biopharmaceutical industry is moving toward a more quality by design (QbD) approach that seeks to increase product and process understanding and process control. Miniature bioreactor systems offer a high-throughput method enabling the assessment of numerous process variables in a controlled environment. However, the number of off/at-line samples that can be taken is restricted due to the small working volume of each vessel. This limitation may be resolved through the use of Raman spectroscopy due to its ability to obtain multianalyte data from small sample volumes fast. It can, however, be challenging to implement this technique for this application due to the complexity of the sample matrix and that analytes are often present in low concentration. Here, we present a design of experiments (DOE) approach to generate samples for calibrating robust multivariate predictive models measuring glucose, lactate, ammonium, viable cell concentration (VCC) and product concentration, for unclarified cell culture that improves the daily monitoring of each miniature bioreactor vessel. Furthermore, we demonstrate how the output of the glucose and VCC models can be used to control the glucose and main nutrient feed rate within miniature bioreactor cultures to within qualified critical limits set for larger scale vessels. The DOE approach used to generate the calibration sample set is shown to result in models more robust to process changes than by simply using samples taken from the “typical” process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2740, 2019.     

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.     

In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed‐batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off‐line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe

The FDA's process analytical technology initiative encourages drug manufacturers to apply innovative ideas to better understand their processes. There are many challenges to applying these techniques to monitor mammalian cell culture bioreactors for biologics manufacturing. These include the ability to monitor multiple components in complex medium formulations non-invasively and in-line. We report results that demonstrate, for the first time, the technical feasibility of the in-line application of Raman spectroscopy for monitoring a mammalian cell culture bioreactor. A Raman probe was used for the simultaneous prediction of culture parameters including glutamine, glutamate, glucose, lactate, ammonium, viable cell density, and total cell density.     

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSE_cvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSE_Prel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.     

Chromophore based analyses of steady‐state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption

Diffuse reflectance spectroscopy is a rapidly growing technology in the biophotonics community where it has shown promise in its ability to classify different tissues. In the steady‐state domain a wide spectrum of clinical applications is supported with this technology ranging from diagnostic to guided interventions. Diffuse reflectance spectra provide a wealth of information about tissue composition; however, extracting biologically relevant information from the spectra in terms of chromophores may be more useful to gain acceptance into the clinical community. The chromophores that absorb light in the visible and near infrared wavelengths can provide information about tissue composition. The key characteristics of these chromophores and their relevance in different organs and clinical applications is the focus of this review, along with translating their use to the clinic.

     

Bladder tissue characterization using probe‐based Raman spectroscopy: Evaluation of tissue heterogeneity and influence on the model prediction

Existing approaches for early‐stage bladder tumor diagnosis largely depend on invasive and time‐consuming procedures, resulting in hospitalization, bleeding, bladder perforation, infection and other health risks for the patient. The reduction of current risk factors, while maintaining or even improving the diagnostic precision, is an underlying factor in clinical instrumentation research. For example, for clinic surveillance of patients with a history of noninvasive bladder tumors real‐time tumor diagnosis can enable immediate laser‐based removal of tumors using flexible cystoscopes in the outpatient clinic. Therefore, novel diagnostic modalities are required that can provide real‐time in vivo tumor diagnosis. Raman spectroscopy provides biochemical information of tissue samples ex vivo and in vivo and without the need for complicated sample preparation and staining procedures. For the past decade there has been a rise in applications to diagnose and characterize early cancer in different organs, such as in head and neck, colon and stomach, but also different pathologies, for example, inflammation and atherosclerotic plaques. Bladder pathology has also been studied but only with little attention to aspects that can influence the diagnosis, such as tissue heterogeneity, data preprocessing and model development. The present study presents a clinical investigative study on bladder biopsies to characterize the tumor grading ex vivo, using a compact fiber probe‐based imaging Raman system, as a crucial step towards in vivo Raman endoscopy. Furthermore, this study presents an evaluation of the tissue heterogeneity of highly fluorescent bladder tissues, and the multivariate statistical analysis for discrimination between nontumor tissue, and low‐ and high‐grade tumor.     

In vivo sensing of cutaneous edema: A comparative study of diffuse reflectance,Raman spectroscopy and multispectral imaging

Quantitative noninvasive assessment of water content in tissues is important for biomedicine. Optical spectroscopy is potentially capable of solving this problem; however, its applicability for clinical diagnostics remains questionable. The presented study compares diffuse reflectance spectroscopy, Raman spectroscopy and multispectral imaging in the characterization of cutaneous edema. The source-detector geometries for each method are selected based on Monte Carlo simulations results to detect the signal from the dermis. Then, the kinetics of the edema development is studied for two models. All methods demonstrate synchronous trends for histamine-induced edema: The water content reaches a maximum of 1 hour after histamine application and then gradually decreases. For the venous occlusion, a 51% increase in water content is observed with Raman spectroscopy. The differences in water content estimation by three methods are explained based on the light propagation model. The obtained results are essential for introducing quantitative optical water measurement technology to the clinics.     

Process analytical technology case study: Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions. Published: October 6, 2005 The views presented in this article do not necessarily reflect those of the Food and Drug Administration.     

Microperoxidase 8 adsorbed on a roughened silver electrode as a monomeric high-spin penta-coordinated species: characterization by SERR spectroscopy and electrochemistry

Microperoxidase 8 (MP8), a heme octapeptide obtained by hydrolytic digestion of cytochrome c, was adsorbed at the surface of a roughened silver electrode in order to provide a new supported biomimetic system for hemoproteins. A combination of two techniques was used to study its redox and coordination properties: electrochemistry and surface-enhanced resonance Raman (SERR) spectroscopy. This allowed us to show that MP8 could be adsorbed as a monolayer at the surface of the roughened silver electrode, where it could undergo a reversible electron transfer. Under those conditions, a redox potential of –0.4 V vs. SCE (–0.16 V vs. NHE) was measured for MP8, which was almost identical to that reported for N-acetyl-MP8 in aqueous solution. In addition, whereas MP8 appeared to aggregate in solution, and led to a mixture of high-spin penta-coordinated (5cHS) and low-spin hexa-coordinated (6cLS) iron(III) or iron(II) species, it was recovered almost exclusively as a monomeric high-spin penta-coordinated species at the surface of the electrode, both in the reduced and in the oxidized states. This then allowed a free coordination site on the iron, on the distal face of MP8 accessible to ligands. Accordingly, experiments performed in the presence of potassium cyanide demonstrated that MP8 adsorbed on a silver electrode could be ligated by a sixth CN^– ligand. Thus there is the possibility of binding several kinds of ligands such as O₂ or H₂O₂, which will open the way to biocatalysis of oxidation reactions at the surface of an electrode, or ligands such as drugs which will lead to the design of new biosensors for molecules of biological interest.     

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.     

Three-Dimensional (3D) cell culture monitoring: Opportunities and challenges for impedance spectroscopy

Three-dimensional (3D) cell culture has developed rapidly over the past 5–10 years with the goal of better replicating human physiology and tissue complexity in the laboratory. Quantifying cellular responses is fundamental in understanding how cells and tissues respond during their growth cycle and in response to external stimuli. There is a need to develop and validate tools that can give insight into cell number, viability, and distribution in real-time, nondestructively and without the use of stains or other labelling processes. Impedance spectroscopy can address all of these challenges and is currently used both commercially and in academic laboratories to measure cellular processes in 2D cell culture systems. However, its use in 3D cultures is not straight forward due to the complexity of the electrical circuit model of 3D tissues. In addition, there are challenges in the design and integration of electrodes within 3D cell culture systems. Researchers have used a range of strategies to implement impedance spectroscopy in 3D systems. This review examines electrode design, integration, and outcomes of a range of impedance spectroscopy studies and multiparametric systems relevant to 3D cell cultures. While these systems provide whole culture data, impedance tomography approaches have shown how this technique can be used to achieve spatial resolution. This review demonstrates how impedance spectroscopy and tomography can be used to provide real-time sensing in 3D cell cultures, but challenges remain in integrating electrodes without affecting cell culture functionality. If these challenges can be addressed and more realistic electrical models for 3D tissues developed, the implementation of impedance-based systems will be able to provide real-time, quantitative tracking of 3D cell culture systems.