Process Analytical Technology Tool Research Articles

Data‐driven identification of crystallization kinetics

AbstractA novel data‐driven methodology is presented for developing mathematical models for crystallization processes. The data‐driven approach is based on the sparse identification of nonlinear dynamics (SINDy) method, which iterates between a partial least‐squares fit and a sparsity‐promoting step leading to the discovery of sparse interpretable models. The performance of the SINDy methodology is characterized for the identification of crystallization kinetics in a mixed tank operated in a continuous mode, the isothermal crystallization of lysozyme in a batch stirred tank and cooling crystallization of paracetamol. The SINDy method is robust against noise. Good agreement is obtained between the data‐driven model and the data obtained from crystallization experiments. The presented data‐driven approach can be attractive for modeling industrial crystallization processes where process analytical technology tools are available for the measurement of process variables but functional forms of kinetic expressions are unknown.

Pioneering Just-in-Time (JIT) Strategy for Accelerating Raman Method Development and Implementation for Biologic Continuous Manufacturing.

Raman spectroscopy is a popular process analytical technology (PAT) tool that has been increasingly used to monitor and control the monoclonal antibody (mAb) manufacturing process. Although it allows the characterization of a variety of quality attributes by developing chemometric models, a large quantity of representative data is required, and hence, the model development process can be time-consuming. In recent years, the pharmaceutical industry has been expediting new drug development in order to achieve faster delivery of life-changing drugs to patients. The shortened development timelines have impacted the Raman application, as less time is allowed for data collection. To address this problem, an innovative Just-in-Time (JIT) strategy is proposed with the goal of reducing the time needed for Raman model development and ensuring its implementation. To demonstrate its capabilities, a proof-of-concept study was performed by applying the JIT strategy to a biologic continuous process for producing monoclonal antibody products. Raman spectroscopy and online two-dimensional liquid chromatography (2D-LC) were integrated as a PAT analyzer system. Raman models of antibody titer and aggregate percentage were calibrated by chemometric modeling in real-time. The models were also updated in real-time using new data collected during process monitoring. Initial Raman models with adequate performance were established using data collected from two lab-scale cell culture batches and subsequently updated using one scale-up batch. The JIT strategy is capable of accelerating Raman method development to monitor and guide the expedited biologics process development.

Robust method for chromatogram shape analysis to improve early detection of performance drifts and adverse changes in process parameters during purification column operations.

The biopharmaceutical industry is under increased pressure to maximize efficiency, enhance quality compliance, and reduce the cost of drug substance manufacturing. Ways to reduce costs associated with manufacturing of complex biological molecules include maximizing efficiency of chromatography purification steps. For example, process analytical technology (PAT) tools can be employed to improve column resin life, prevent column operating failures, and decrease the time it takes to solve investigations of process deviations. We developed a robust method to probe the shape of the chromatogram for indications of column failure or detrimental changes in the process. The approach herein utilizes raw data obtained from manufacturing followed by a pre-processing routine to align chromatograms and patch together the different chromatogram phases in preparation for multivariate analysis. A principal component analysis (PCA) was performed on the standardized chromatograms to compare different batches, and resulted in the identification specific process change that affected the profile. In addition, changes in the chromatogram peaks were used to create predictive models for impurity clearance. This approach has the potential for early detection of column processing issues, improving timely resolution in large-scale chromatographic operations.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Development of a PAT platform for the prediction of granule tableting properties

In this work, the feasibility of implementing a process analytical technology (PAT) platform consisting of Near Infrared Spectroscopy (NIR) and particle size distribution (PSD) analysis was evaluated for the prediction of granule downstream processability. A Design of Experiments-based calibration set was prepared using a fluid bed melt granulation process by varying the binder content, granulation time, and granulation temperature. The granule samples were characterized using PAT tools and a compaction simulator in the 100-500kg load range. Comparing the systematic variability in NIR and PSD data, their complementarity was demonstrated by identifying joint and unique sources of variation. These particularities of the data explained some differences in the performance of individual models. Regarding the fusion of data sources, the input data structure for partial least squares (PLS) based models did not significantly impact the predictive performance, as the root mean squared error of prediction (RMSEP) values were similar. Comparing PLS and artificial neural network (ANN) models, it was observed that the ANNs systematically provided superior model performance. For example, the best tensile strength, ejection stress, and detachment stress prediction with ANN resulted in an RMSEP of 0.119, 0.256, and 0.293 as opposed to the 0.180, 0.395, and 0.430 RMSEPs of the PLS models, respectively. Finally, the robustness of the developed models was assessed.

Micro simulated moving bed chromatography-mass spectrometry as a continuous on-line process analytical tool.

Continuous manufacturing is becoming increasingly important in the (bio-)pharmaceutical industry, as more product can be produced in less time and at lower costs. In this context, there is a need for powerful continuous analytical tools. Many established off-line analytical methods, such as mass spectrometry (MS), are hardly considered for process analytical technology (PAT) applications in biopharmaceutical processes, as they are limited to at-line analysis due to the required sample preparation and the associated complexity, although they would provide a suitable technique for the assessment of a wide range of quality attributes. In this study, we investigated the applicability of a recently developed micro simulated moving bed chromatography system (µSMB) for continuous on-line sample preparation for MS. As a test case, we demonstrate the continuous on-line MS measurement of a protein solution (myoglobin) containing Tris buffer, which interferes with ESI-MS measurements, by continuously exchanging this buffer with a volatile ammonium acetate buffer suitable for MS measurements. The integration of the µSMB significantly increases MS sensitivity by removing over 98% of the buffer substances. Thus, this study demonstrates the feasibility of on-line µSMB-MS, providing a versatile PAT tool by combining the detection power of MS for various product attributes with all the advantages of continuous on-line analytics.

Prediction of intensified ethanol fermentation of sugarcane using a deep learning soft sensor and process analytical technology

AbstractBACKGROUNDIntensified ethanol fermentation produces higher ethanol concentrations while reducing water and energy requirements. Nevertheless, the inhibitory and detrimental effect of the cellular stress barriers in this process further complicates the nonlinear dynamic relationship between the variables that directly reflect the fermentation quality. These key variables are hard to measure in real time and therefore cannot be directly controlled.RESULTSThis work presents the development of a soft sensor that predicts in real time the ethanol and substrate concentrations of an intensified fermentation. The soft sensor uses feedforward neural networks (FNNs) with easily measurable process analytical technology (PAT) tools. The application of advanced PAT tools such as redox potential and capacitance, in addition to temperature and pH are explored as input variables. The complex kinetic relationship between the studied variables was captured with FNN architectures with a single hidden layer and between 95 and 175 hidden neurons for the different cases studied. Acceptable predictions are achieved for the concentration of ethanol (RMSE = 9.5 and R2 = 0.97) and substrate (RMSE = 17.02 and R2 = 0.92).CONCLUSIONSThe results confirm that the proposed soft sensor can accurately predict the ethanol and substrate concentrations. Collectively, capacitance, redox potential, temperature and pH provide a powerful platform of PAT tools that can directly infer key variables showing the fermentation quality in real time. This study provides a significant step towards the systematic development of a reliable soft sensor with integration of advanced PAT tools. © 2023 Society of Chemical Industry.

Laser Triangulation Based In-Line Elastic Recovery Measurement for the Determination of Ribbon Solid Fraction in Roll Compaction

Process Analytical Technology (PAT) plays a crucial role in the design of today's manufacturing lines as continuous manufacturing becomes more important. Until now PAT tools to measure the ribbon solid fraction (SFribbon) in-line are not commonly used in roll compaction. The aim of this study was therefore to establish a new approach as PAT for in-line ribbon solid fraction determination. Different placebo formulations with different binders and one formulation containing active pharmaceutical ingredient were investigated using in-line laser triangulation measurement to detect the ribbon thickness after compaction. With this the ribbon elastic recovery was determined in-line (ERin−line) while the ribbons are attached to the roll surface. It was found that the ratio (ERratio) between the total elastic recovery and ERin−line is formulation specific and not influenced by any process parameters. This enables ERratio as prediction tool for SFribbon, if the solid fraction at gap (SFgap) width is known. SFgap was determined with ribbon mass flow measurement or based on the Midoux model, a simplified Johanson model, gaining two prediction models for SFribbon. Both models showed good agreement of the predicted SFribbon and the measured one.

Influence of cell specific parameters in a dielectric spectroscopy conversion model used to monitor viable cell density in bioreactors.

In the biopharmaceutical industry, the use of mammalian cells to produce therapeutic proteins is becoming increasingly widespread. Monitoring of these cultures via different analysis techniques is essential to ensure a good quality product while respecting good manufacturing practice (GMP) regulations. PAT (Process Analytical Technologies) tools provide real-time measurements of the physiological state of the culture and enable process automation. Dielectric spectroscopy is a PAT that can be used to monitor the viable cell concentration of living cells (VCC) after processing raw permittivity data. Several modeling approaches exist and estimate biomass with different accuracy. The accuracy of the Cole-Cole and Maxwell Wagner's equations are studied here in the determination of the VCC and cell radius in Chinese hamster ovary (CHO) culture. A sensitivity analysis performed on the parameters entering the equations highlighted the importance of the cell specific parameters such as internal conductivity (σi ) and membrane capacitance (Cm ) in the accuracy of the estimation of VCC and cell radius. The most accurate optimization method found to improve the accuracy involves in-process adjustments of Cm and σi in the model equations with samplings from the bioreactor. This combination of offline and in situ data improved the estimation precision of the viable cell concentration by 69% compared to a purely mechanistic model without offline adjustments. This article is protected by copyright. All rights reserved.

Optimization of radial extrusion and pellet coating processes using PAT approaches

FDA's initiative Pharmaceutical CGMPs for the 21st century opened the door for introduction of several risk based approaches in pharmaceutical industry. One significant advancement that has emerged is the implementation of process analytical technology (PAT), which has opened doors for understanding and controlling complex technological processes. Two such processes, radial extrusion and pellet coating, offer a solid foundation for the application of PAT tools due to their numerous critical process parameters. The aim of the first part of the study was to optimize the neutral pellet production to produce the pellets with properties desired for successful film coating using design of experiments (DoE). In the second part the optimized pellets underwent film coating and the coating quantity was predicted in real or near real-time using in-line and at-line NIR probes and the performance of both probes was compared.The desired properties of the pellets, narrow particle size distribution, high sphericity and high process yield, were successfully achieved. Models for film coating quantity prediction using in-line and at-line NIR probe were successfully calibrated and tested by coating two additional batches. Despite the limited sample size for model calibration, at-line NIR exhibited excellent prediction performance and enabled accurate determination of process end-point. The coating quantity determined by UV/VIS spectroscopy in both test batches deviated by less than 2.0 % from the target value. However, the in-line NIR probe, primarily due to its inferior spectral resolution, displayed a slightly lower quality of the calibrated model and notable overprediction for the tested batches.

Design of experiments for micellar electrokinetic chromatography method development for the monitoring of water-soluble vitamins in cell culture medium.

Biopharmaceutical production takes place in complex processes which should be thoroughly understood. Therefore, the iConsensus project focuses on developing a monitoring platform integrating several process analytical technology tools for integrated, automated monitoring of the biopharmaceutical process. Water-soluble vitamin monitoring using (microchip) capillary electrophoresis (CE) is part of this platform. This work comprises the development of conventional CE methods as the first part towards integrated vitamin monitoring. The vitamins were divided based on their physical-chemical properties to develop two robust methods. Previously, a method for the analysis of cationic vitamins (pyridoxine, pyridoxal, pyridoxamine, thiamine and nicotinamide) in cell culture medium was developed. This work focused on the development of a micellar electrokinetic chromatography method for anionic and neutral vitamins (riboflavin, d-calcium pantothenate, biotin, folic acid, cyanocobalamin and ascorbic acid). By employing multivariate design of experiments, the background electrolyte (BGE) could be optimised within one experiment testing only 11 BGEs. The optimised BGE conditions were 200mM borate with 77mM sodium dodecyl sulphate at a pH of 8.6. Using this BGE, all above-mentioned cationic, anionic and neutral vitamins could be separated in clean samples. In cell culture medium, most anionic and neutral vitamins could be separated. Combining the two methods allows for analysis of cationic, anionic and neutral vitamins in cell culture medium samples. The next step towards integrated vitamin monitoring includes transfer to microchip CE. Due to the lack of fast and reliable methods for vitamin monitoring, the developed capillary methods could be valuable as stand-alone at-line process analytical technology solutions as well.

UV/Vis spectroscopy as an in-line monitoring tool for tablet content uniformity

Continuous manufacturing provides advantages compared to batch manufacturing and is increasingly gaining importance in the pharmaceutical industry. In particular, the implementation of tablet processes in continuous plants is an important part of current research. For this, in-line real-time monitoring of product quality through process analytical technology (PAT) tools is crucial. This study focuses on an in-line UV/Vis spectroscopy method for monitoring the active pharmaceutical ingredient (API) content in tablets. UV/Vis spectroscopy is particularly advantageous here, because it allows univariate data analysis without complex data processing. Experiments were conducted on a rotary tablet press. The tablets consisted of 7– 13 wt% theophylline monohydrate as API, lactose monohydrate and magnesium stearate. Two tablet production rates were investigated, 7200 and 20000 tablets per hour. The UV/Vis probe was mounted at the ejection position and measurements were taken on the tablet sidewall. Validation was according to ICH Q2 with respect to specificity, linearity, precision, accuracy and range. The specificity for this formulation was proven and linearity was sufficient with coefficients of determination of 0.9891 for the low throughput and 0.9936 for the high throughput. Repeatability and intermediate precision were investigated. Both were sufficient, indicated by coefficients of variations with a maximum of 6.46% and 6.34%, respectively. The accuracy was evaluated by mean percent recovery. This showed a higher accuracy at 20000 tablets per hour than 7200 tablets per hour. However, both throughputs demonstrate sufficient accuracy. Finally, UV/Vis spectroscopy is a promising alternative to the common NIR and Raman Spectroscopy.

Effects of wet granulation process variables on the quantitative assay model of transmission Raman spectroscopy for pharmaceutical tablets

Transmission Raman spectroscopy (TRS) is a process analytical technology tool for nondestructive analysis of drug content in tablets. Although wet granulation is the most used tablet manufacturing method, most TRS studies have focused on tablets manufactured via direct compression. The effects of upstream process parameter variations, such as granulation, on the prediction performance of TRS quantitative models are unknown. We evaluated the effects of process parameter variations during granulation on the prediction performance of the TRS quantitative model. Tablets with a drug concentration of 1%w/w were used. We developed PLS calibration models for the drug concentration range of 70–130% label claims. Subsequently, we predicted the drug content of the tablets with different granulation parameters. The results of our study demonstrate that the variation in the predicted recovery due to the variation in granulation parameters was practically acceptable. The calibration model showed a good prediction performance for tablets manufactured at different granulation scales and thicknesses. Therefore, we conclude that TRS quantitative models are robust to variations in upstream processes, such as granulation and downstream variations in tableting parameters. These results suggest that TRS is a versatile non-destructive quantitative analysis method that can be applied in tablet manufacturing.

Root Cause Analysis of An Inverse Relationship Between The Ice Nucleation Temperature, Process Efficiency And Quality of A Lyophilized Product

The aim of this study was to probe an unexpected relationship between the ice nucleation temperature (TIN), process efficiency and product attributes in a controlled ice nucleation (CIN) lyophilization process. An amorphous product was lyophilized with (CIN-5 °C, CIN-7 °C or CIN-10 °C) or without (NOCIN) control of ice nucleation. Process parameters and product attributes were monitored and compared using a series of advanced in-line and off-line process analytical technology (PAT) tools. Unexpectedly, an indirect relationship was observed between TIN and primary drying efficiency for the CIN processes. Further, the CIN-5 °C process was associated with higher product resistance to mass flow than corresponding CIN-7 °C and CIN-10 °C processes. Surprisingly, the air voids in some NOCIN products were larger than CIN-5 °C products but comparable to CIN-7 °C. Heat flux analysis revealed an indirect relationship between TIN and the minimum hold time required to complete solidification. The heat flux analysis also revealed all products underwent complete solidification prior to primary drying. The order of homogeneity in water activity of the products was CIN-5 °C ≥NOCIN>CIN-7 °C. The higher homogeneity in water activity of CIN-5 °C than corresponding CIN-7 °C processes indicated that the lower process efficiency of CIN-5 °C could not be attributed to unsuccessful induction of ice nucleation during CIN-5 °C. High resolution micro-CT imaging and Artificial Intelligence Image analysis revealed cake wall deformation in CIN-7 °C and NOCIN products but not in CIN-5 °C. In addition, NOCIN products had bimodal distribution in air voids with median size range of 4–5 µm and 151.9–309 µm, respectively, hence the lower process efficiency of NOCIN despite the higher D90. Thus, the observed relationship between TIN and process efficiency may be attributed to microstructural changes post freezing. This hypothesis was corroborated by visible macroscopic cake collapse in NOCIN products but not in CIN products after lyophilization at a higher shelf temperature. In conclusion, the advantages of controlling the ice nucleation temperature of a lyophilization process may only be attained through a robust process design that takes into consideration the primary and secondary drying process parameters. Further, combined use of advanced in-line and off-line PAT tools for process and product characterization may hasten the at scale adoption of advance techniques such as CIN.

In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100–2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real‐time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.

Review of the Application of PAT in the Pharmaceutical Continuous Crystallization Process.

As an important pharmaceutical process, crystallization greatly impacts the final product. In recent years, the continuous crystallization process has attracted more attention from researchers, with the promotion of continuous manufacturing (CM) by the Food and Drug Administration (FDA). The continuous crystallization process has the advantages of high economic benefit, stable and uniform quality, a short production cycle, and personalization. To carry out continuous crystallization, some related process analytical technology (PAT) tools have become the focus of breakthroughs. Infrared (IR) spectroscopy, Raman spectroscopy, and focused beam reflection measurement (FBRM) tools have gradually become research hotspots due to their fast, non-destructive, and real-time monitoring characteristics. This review compared the advantages and disadvantages of the three technologies. Their applications in the upstream mixed continuous crystallization process, the middle reaches of crystal nucleation and growth, and the process of the downstream refining were discussed to provide corresponding guidance for the practice and further development of these three technologies in the continuous crystallization process and promote the development of CM in the pharmaceutical industry.

Near-infrared spectroscopy as a process analytical technology tool for monitoring performance of membrane filtration in a whey protein fractionation process.

A successful transition from a laboratory proof-of-principle to a functioning industrial Process Analytical Technology (PAT) application of spectroscopy and chemometrics is still an active area of research and development. A comprehensive understanding on how the design and implementation of the optical instrumentation affect the data quality, and how this will affect the performance of the prediction models is vital for the successful implementation of in-line monitoring. In a previous study, we have demonstrated that near-infrared spectroscopy (NIRS), combined with chemometric techniques, can be used to quantify α-lactalbumin and β-lactoglobulin in aqueous whey solutions. This work demonstrates the potential, both at-line and in-line, of monitoring a protein fractionation process in a full-scale production. An in-line near infrared spectrometer (NIRS) was used to monitor the individual protein concentrations in a protein fractionation process over 20 days. In addition, samples were extracted from the process and analysed with at-line NIRS and an in-house HPLC reference method. The developed models could predict β-lactoglobulin and α-lactalbumin concentrations with satisfactory precision and accuracy, yielding a root mean square error of cross-validation of 0.08 and 0.18 w/w% proteins, respectively. For the first time, the whey proteins concentrations were measured continuously in a production facility to demonstrate the potential of NIRS for at-line and in-line rapid monitoring of protein composition in industrial whey streams. A PAT tool was developed from NIRS data where Partial Least Squares regression modelling and an Exponentially Weighted Moving Average filtering were used to extract and visualize valuable information on the process dynamics and performance. The study demonstrates that continuous monitoring by in-line NIRS allowed elucidation of some process behaviours not readily detectable with the current sampling frequency and that it offers novel process optimisation opportunities by providing increased process understanding and decision support information concerning preventive maintenance and real-time process validation.

Can Continuous Manufacturing of Topical Semisolids by Hot Melt Extrusion Soon Be a Reality?

For more than five decades, pharmaceutical manufacturers have been relying heavily on batch manufacturing that is a sequential, multistep, laborious, and time-consuming process. However, late advances in manufacturing technologies have prompted manufacturers to consider continuous manufacturing (CM) is a feasible manufacturing process that encompasses fewer steps and is less tedious and quick. Global regulatory agencies are taking a proactive role to facilitate pharmaceutical industries to adopt CM that assures product quality by employing robust manufacturing technologies encountering fewer interruptions, thereby substantially reducing product failures and recalls. However, adopting innovative CM is known to pose technical and regulatory challenges. Hot melt extrusion (HME) is one such state-of-the-art enabling technology that facilitates CM of diverse pharmaceutical dosage forms, including topical semisolids. Efforts have been made to continuously manufacture semisolids by HME integrating the principles of Quality by Design (QbD) and Quality Risk Management (QRM) and deploying Process Analytical Technologies (PAT) tools. Attempts have been made to systematically elucidate the effect of critical material attributes (CMA) and critical process parameters (CPP) on product critical quality attributes (CQA) and Quality Target Product Profiles (QTPP) deploying PAT tools. The article critically reviews the feasibility of one of the enabling technologies such as HME in CM of topical semisolids. The review highlights the benefits of the CM process and challenges ahead to implement the technology to topical semisolids. Once the CM of semisolids adopting melt extrusion integrated with PAT tools becomes a reality, the process can be extended to manufacture sterile semisolids that usually involve more critical processing steps.

An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development.

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.

Real-time detection of mAb aggregates in an integrated downstream process.

The implementation of continuous processing in the biopharmaceutical industry is hindered by the scarcity of process analytical technologies (PAT). To monitor and control a continuous process, PAT tools will be crucial to measure real-time product quality attributes such as protein aggregation. Miniaturizing these analytical techniques can increase measurement speed and enable faster decision-making. A fluorescent dye (FD)-based miniaturized sensor has previously been developed: a zigzag microchannel which mixes two streams under 30 s. Bis-ANS and CCVJ, two established FDs, were employed in this micromixer to detect aggregation of the biopharmaceutical monoclonal antibody (mAb). Both FDs were able to robustly detect aggregation levels starting at 2.5%. However, the real-time measurement provided by the microfluidic sensor still needs to be implemented and assessed in an integrated continuous downstream process. In this work, the micromixer is implemented in a lab-scale integrated system for the purification of mAbs, established in an ÄKTA™ unit. A viral inactivation and two polishing steps were reproduced, sending a sample of the product pool after each phase directly to the microfluidic sensor for aggregate detection. An additional UV sensor was connected after the micromixer and an increase in its signal would indicate that aggregates were present in the sample. The at-line miniaturized PAT tool provides a fast aggregation measurement, under 10 min, enabling better process understanding and control.