Implementation Of Process Analytical Technology Research Articles

Use of spectroscopic process analytical technology for rapid quality evaluation during preparation of CHO cell culture media.

Media preparation parameters contribute significantly to media quality, cell culture performance, productivity, and product quality. Establishing proper media preparation procedures is critical for ensuring a robust CHO cell culture process. Process analytical technology (PAT) enables unique ways to quantify assessments and improve media quality. Here, cell culture media were prepared under a wide range of temperatures (40-80°C) and pH (7.6-10.0). Media quality profiles were compared using three real-time PATs: Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and excitation-emission matrix (EEM) spectroscopy. FTIR and Raman spectroscopies identified shifts in media quality under high preparation temperature (80°C) and at differing preparation pH which negatively impacted monoclonal antibody (mAb) production. In fed-batch processes for production of three different mAbs, viable cell density (VCD) and cell viability were mostly unaffected under all media preparation temperatures, while titer and cell specific productivity of mAb decreased when cultured in basal and feed media prepared at 80°C. High feed preparation pH alone was tolerated but cell growth and productivity profiles deviated from the control condition. Further, charge variants (main, acidic, basic species) and glycosylation (G0F, afucosylation, and high mannose) were examined. Statistically significant differences were observed for one or more of these quality attributes with any shifts in media preparation. In this study, we demonstrated strong associations between media preparation conditions and cell growth, productivity, and product quality. The rapid evaluation of media by PAT implementation enabled more comprehensive understanding of different parameters on media quality and consequential effects on CHO cell culture.

Overview: flexible and versatile approach of quality by design & process analytical technology in industries

Quality by Design (QbD) is crucial to the creation of contemporary in pharmaceuticals, microbiology, biotechnology, product management etc. In order to build proactive, scientific, and risk-based processes and products, QbD helps and supports both the industry and the FDA. Instead of waiting until the final quality check of the finished product, it is founded on the idea of establishing quality from the very beginning of the process. A successful plan that lowers batch failures and recalls is finally provided by an efficient QbD approach, which offers insights and crucial upstream information throughout the development process. The purpose of this review is to provide an overview of the implementation of Quality by Design (QbD), its tools, elements, and techniques, the relevancy with various guidelines, and the use in present-day pharmaceutical. The IQ Consortium provides information on the current situation of process analytical technology (PAT) as it relates to the creation of active pharmaceutical ingredients (API) in branded pharmaceutical firms. The article gives concrete examples of why and how the pharmaceutical industry uses PAT tools in API development by using an API process pipeline. PAT can reduce personnel risks involved with sampling dangerous compounds for in-process testing and increase R&D efficiency. Although not all chemical processes or stages are easily suited to applying the features of the PAT toolbox, PAT permits accurate and speedy (real or near time) evaluations of processes that might involve compounds that are highly dangerous, transitory, or heterogeneous when necessary. The major regulatory agencies and the biotech sector have worked hard to assist the application of the ideas of Quality by Design (QbD) and Process Analytical Technology (PAT) over the past ten years, with varying degrees of success. Despite the fact that just one biotech therapy has received approval under the QbD paradigm thus far, the tools and methods associated with these two ideas are deeply ingrained in the work procedures of the majority of international pharmaceutical firms. This overview is mainly focused on the QbD and PAT's development in the first place. Second, give an overview of QbD and PAT implementation and point out any places where more contributions are possible.

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.

Methods and Analysis of Biological Contaminants in the Biomanufacturing Industry

The advent of bioprocessing has revolutionized the biomanufacturing industry, leading to the rise of biotherapeutics derived from biologic products such as chimeric antigen receptor (CAR) T-cells used for targeted cancer treatment and the Vero cell line for the production of viral vectors and vaccines. Despite these promising developments, most biologic products are characterized by fragile macromolecular structures that are heterogenous with a purity profile that varies with each batch making them susceptible to microorganism contamination. Regulatory oversight of biologic products is imperative to ensure adherence to good manufacturing practices and compliance with quality management systems. Current quality assurance protocols during production include monoclonality during cell line development, real-time monitoring of process parameters, flow cytometry for microbial monitoring, polymerase chain reaction, and immunoassay techniques to amplify DNA sequences related to bacterial or biological contaminants. FDA guidance recommends the implementation of process analytical technology within biomanufacturing production to measure critical quality parameters, which includes screening for potential biological contamination. Future advancements in bioprocess monitoring and control should capitalize on providing cheap, real-time, and sensitive detection. Biosensors, mass spectrometry, and polymerase chain reaction present robust, rapid, and real-time capabilities for multiplexed detection of contaminant analytes and have shown promise in meeting these needs. This review discusses the main biological contaminants of bioprocesses, European Union and FDA regulatory guidelines for monitoring and control within biologics production, existing methods and their limitations, and future advancements for biological contamination detection.

Employing QbD strategies to assess the impact of cell viability and density on the primary recovery of monoclonal antibodies.

Quality by Design (QbD) is one of the most important tools for the implementation of Process Analytical Technology (PAT) in biopharmaceutical production. For optimal characterization of a monoclonal antibody(mAb) upstream process a stepwise approach was implemented. The upstream was divided into three process stages, namely inoculum expansion, production, and primary recovery, which were investigated individually. This approach enables analysis of process parameters and associated intermediate quality attributes as well as systematic knowledge transfer to subsequent process steps. Following previous research, this study focuses on the primary recovery of the mAb and thereby marks the final step toward a holistic characterization of the upstream process. Based on gained knowledge during the production process evaluation, the cell viability and density were determined as critical parameters for the primary recovery. Directed cell viability adjustment was achieved using cytotoxic camptothecin in a novel protocol. Additionally, the cell separation method was added to the Design of Experiments (DoE) as a qualitative factor and varied between filtration and centrifugation. To assess the quality attributes after cell separation, the bioactivity of the mAb was analyzed using a cell-based assay and the purity of the supernatant was evaluated by measurement of process related impurities (host cell protein proportion, residual DNA). Multivariate data analysis of the compiled data confirmed the hypothesis that the upstream process has no significant influence on the bioactivity of the mAb. Therefore, process control must be tuned towards high mAb titers and purity after the primary recovery, enabling optimal downstream processing of the product. To minimize amounts of host cell proteins and residual DNA the cell viability should be maintained above 85% and the cell density should be controlled around 15×106cells/ml during the cell removal. Thereby, this study shows the importance of QbD for the characterization of the primary recovery of mAbs and highlights the useful implementation of the stepwise approach over subsequent process stages.

Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.

Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post-translational modifications (PTMs), formulation, and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.

Control strategy definition for a drug product continuous wet granulation process: Industrial case study

This paper describes the specific control strategy of the commercial manufacturing process of an immediate release tablet formulation based on continuous twin-screw wet granulation. This control strategy has been defined by a multidisciplinary team using an enhanced approach, in alignment with the quality by design principles. During process development, experiments have been performed according to multivariate designs first to identify critical material attributes and critical process parameters and then, to define process conditions generating a product having the required quality. Hence, controls have been applied on critical quality attributes and on related critical process parameters and critical material attributes. Due to the specificity of the process that combines batch and continuous unit operations, a specific control strategy has been designed to ensure intermediate and end product quality. Therefore, controls including soft sensor model and in process controls have been developed to continuously monitor granules residual moisture content, assay and dissolution as granules and tablets critical attributes. In addition, process analytical technology implementation enabled increased process understanding and provided support for the development of the control strategy. This study is therefore considered as a real industrial case study of control strategy definition and implementation for an intended commercial continuous manufacturing process.

Process monitoring framework for cross-flow diafiltration-based virus-like particle disassembly: Tracing product properties and filtration performance.

Virus-like particles (VLPs) are an emerging biopharmaceutical modality with great potential as a platform technology. VLPs can be applied as gene therapy vectors and prophylactic or therapeutic vaccines. For non-enveloped VLPs, recombinant production of the protein subunits leads to intracellular self-assembly. The subsequent purification process includes VLP dis- and reassembly which aim at removing encapsulated impurities and improving particle properties. Filtration-based separation and processing has proven successful for VLPs but requires large product quantities and laborious experiments in early development stages. Both challenges can be tackled by implementation of process analytical technology (PAT) to efficiently obtain extensive process information. In this study, an existing PAT setup was extended to comprehensively monitor the diafiltration-based disassembly of hepatitis B core antigen (HBcAg) VLPs. Process-related signals were monitored in-line, while product-related signals, such as ultraviolet light (UV) spectra as well asstatic and dynamic light scattering (SLS and DLS), were monitored on-line. The applicability of the sensors for disassembly monitoring was evaluated under varying processing conditions. HBcAg VLP subunit concentrations were accurately predicted based on UV data using ordinary and partial least squares regression models (Q2 from 0.909 to 0.976). DLS data were used for aggregation monitoring while the SLS intensity qualitatively reflected the disassembly progress.

Investigating the Design and Implementation of an In-Line Near-Infrared Probe Using Computational Fluid Dynamics for Measurement of Non-Newtonian Fluids.

Process analytical technology (PAT) has developed significantly since its introduction in pharma where many in situ analytical probes and measuring devices are now commercially available, replacing the use of off-line quality control measurements that are typically laborious and time intensive. The use of PAT instrumentation should not interfere with the process itself and subsequently should have no effect on the product whilst measuring representative samples. Implementation of these devices is typically arbitrary using empirical means. Therefore, the objective of this study is to highlight the use of computational fluid dynamics modeling to investigate the effect of interfacing parameters and process parameters of an inline near-infrared (NIR) probe used to determine the viscosity of a non-Newtonian micellar liquid. The parameters investigated for the probe were immersion depth, immersion angle, gap size, and fluid velocity. The results conclude that the immersion angle and depth should both be optimized to prevent stagnant fluid accumulating in the measuring gap ensuring that the NIR measurements are representative of the bulk. The gap size determines the optical pathlength and therefore was also investigated against an existing predictive viscosity model showing no changes in model performance with varying gap size. The use of computational modeling to develop a digital twin prior to PAT implementation at the equipment design stage ensures the technology can perform at its best and will also aid in calibration transfer studies.

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation.

Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T2 and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

PAT Implementation on a Mobile Continuous Pharmaceutical Manufacturing System: Real-Time Process Monitoring with In-Line FTIR and Raman Spectroscopy

The strategies and experimental methods for implementation of process analytical technology (PAT) on the mobile pharmaceutical manufacturing system, Pharmacy on Demand (PoD), are discussed. With mu...

Process Analytical Technologies – Advances in bioprocess integration and future perspectives

Process Analytical Technology (PAT) instruments include analyzers capable of measuring physical and chemical process parameters and key attributes with the goal of optimizing process controls. PAT in the form of a probe or sensor is designed to integrate within the pharmaceutical manufacturing line and is coupled with computing equipment to perform chemometric modeling for result interpretation and multilayer statistical control of processes. PAT solutions are intended for understanding bioprocesses with a goal to control quality at all stages of product manufacturing and achieve quality by design (QbD). The goal of PAT implementation is to promote real-time release of products to decrease the cycle time and cost of production. This review focuses on the applications of PAT solutions at different stages of the manufacturing process for vaccine production, the advantages, challenges at present state, and the vision of the future development of biopharmaceutical industries.

De-risking excipient particle size distribution variability with automated robust mixing: Integrating quality by design and process analytical technology

BackgroundParticle size distribution (PSD) variability in excipients affects mixing. In response, manufacturers rely on raw material control and rigidly defined process parameters to achieve quality. However, this status quo is costly; and diverges from regulatory exceptions for process robustness. Although robustness improves cost and material usage efficiency, it remains under-adopted. MethodTo address this gap, a robust batch mixing operation that mitigated the impact of PSD variability was evaluated, with blends comprising chlorpheniramine, microcrystalline cellulose and lactose. PSD of lactose was varied to simulate commercially-relevant variability. Due to PSD-induced rheological variations, the blends had different optimal mixing speeds. For the automation study, near infrared (NIR) spectroscopy; process optimization and endpoint detection algorithms; and control hardware were integrated within a cluster of software environments. NIR spectroscopy was employed for in-line PSD characterization and blend monitoring, to modulate mixing speed and detect endpoint (feedforward and feedback control). ResultsNIR spectroscopy rapidly detected PSD variations by the 6th–9th rotations, to activate feedforward control, which mitigated the effect of PSD variability and reduced the mixing time by 13–34%. Endpoints were correctly detected. PSD variations and blend homogeneity were accurately predicted (relative standard error of prediction ≤ 2%). ConclusionThe automated robust mixing operation was successful. Pertinently, NIR spectrometer can be adopted for multimodal sensing. Its applicability for production-driven characterization of raw materials in batch and continuous pharmaceutical processing should be further explored. Lastly, this study laid the groundwork for end-to-end implementation of process analytical technology in robust batch processing.

Continuous Manufacturing of a Polymer Stabilized Emulsion Monitored with Process Analytical Technology.

Moving from batch to continuous manufacturing (CM) requires implementation of process analytical technology (PAT), as it is crucial to monitor and control these processes. CM of semi-solids has been demonstrated but implementation of a broader range of PAT tools with in- or on-line process interfacing at the end of the CM line has not been demonstrated. The goal of this work was to continuously manufacture creams and to investigate whether in- and on-line measurement of viscosity, changes in the concentration of active pharmaceutical ingredient (API), and pH could be used to support optimization of a model cream product. Additionally, the torque of the mixers was assessed for determination of the physical properties of the cream. Two Raman probes with different probe optics were compared for characterization of the API concentration. The API concentration, amount of neutralizer, and mixing speed of the CM line were systematically varied. Both the PhAT probe with a larger sampling volume and immersion Raman probe with a smaller sampling volume could detect the step changes in the API concentration. The torque from the mixer was compared with the viscosity measurements, but the torque signal could not be correlated with the viscosity due to the dynamic nature of the polymer conformation and the time-dependency of this property. Adjustment of pH of the cream could be monitored with the current installation. The investigated PAT tools could be implemented into a continuous line and, further, be used to support the optimization of a model cream composition and related process parameters.

The development and validation of a quality by design based process analytical tool for the inline quantification of Ramipril during hot-melt extrusion.

Continuous processing is superseding conventional batch processing as a means of manufacturing within the pharmaceutical research/industry. This paradigm shift has led to the implementation of Process Analytical Technology (PAT) as a semi-automatic, predictive tool offering real-time quality control that can be built into the production line. However, PAT tools have been mainly utilised to monitor a single process (e.g. powder blending, synthesis of biopharmaceuticals and small molecules) rather than a full continuous manufacturing process. In addition, there is a paucity of guidance documents that consider the continuous and dynamic conditions of real-time measurements for validation purposes. In this study, the feasibility of developing and validating a predictive and reliable Raman method based on quality by design (QbD) and PAT frameworks for the real-time quantification of Ramipril (RMP) during hot-melt extrusion (HME) were investigated. Through QbD, a design space elucidating the quality attributes of RMP stability was successfully identified based on offline HPLC measurements. Process temperature and powder feeding rate were the main quality attributes to affect the stability of RMP during HME. The optimum combination of process and formulation variables were extracted from the validated design space and used to extrude RMP at a concentration range of 2.5-12.5 %w/w. Three calibration models were established using PLS regression analysis. The developed PLS calibration models showed excellent linearity (R2=0.989, 0.995, 0.992), accuracy (RMSEcv=0.31, 0.26, 0.30%) and specificity (PC1=81, 85, 89%) for models 1, 2 and 3, respectively. Furthermore, the developed QbD-PAT model was able to predict the quantity of RMP at varied process feed rate (10, 35rpm) operating under long processing time (60min). The output of this study allows in-process optimisation of formulation and process variables to control the quality and quantity of RMP during HME. Furthermore, it allows the implementation of PAT tools as routine methods of analysis within the laboratory.

Process Analytical Technology Implementation for Peptide Manufacturing: Cleavage Reaction of Recombinant Lethal Toxin Neutralizing Factor Concatemer as a Case Study.

The α-chymotrypsin-based cleavage reaction is necessary for manufacturing peptides using rDNA technology with tandem repeats. The current work showcases application of process analytical technology (PAT) tools for monitoring and control of this reaction, using recombinant Lethal Toxin Neutralizing Factor (rLTNF) as a case study. At-line Fourier Transform infrared spectroscopy (ATR-FTIR) combined with attenuated total internal reflectance sampling accessory was exploited to monitor the reaction. PLS spectral calibration models were created for real-time quantification of concentrations of rLTNF concatemer and urea in the reaction mixture. An end-to-end PAT monitoring and control strategy was developed to address potential deviations and ensure that targets for yield, purity, and impurity profile are met for each batch. The impact of various deviations of process parameters outside the operating space, such as deviations in the reaction buffer, concentration of concatemer in the IBs, enzyme loading relative to protein concentration, and reaction time with late quenching were investigated. Variation in impurity profile over time in the case of late reaction quenching was determined through HPLC and mass spectrometry. It has been demonstrated how process signatures from the PAT tools across various batches and campaigns can be analyzed to facilitate real-time process monitoring and control.

Characterization of Protein Aggregation Using Hydrogel-Encapsulated nIR Fluorescent Nanoparticle Sensors.

The monitoring of biopharmaceutical critical quality attributes in-process, at both the process development and manufacturing stages, is necessary for the implementation of process analytical technology and quality-by-design principles. Among these attributes, it is important to monitor and control protein aggregation during the manufacturing of biological therapeutics to prevent adverse immunogenic responses and minimize negative impacts on drug deliverability. In this work, we explore hydrogel-encapsulated, label-free fluorescent nanosensors for the characterization of protein aggregation. A mathematical model is used to describe the diffusion and binding of a series of stressed pharmaceutical samples to such sensors, describing their dynamic response. We use mathematical modeling to map the influence of hydrogel properties on the separation performance, given the composition of UV-stressed IgG1 samples. Using this modified model, the compositions of light-stressed IgG1 samples were fit to experimental data and correlated with size-exclusion chromatography data. The results demonstrate the ability to detect the presence of high-molecular-weight protein species at a concentration as low as 1%. This work represents a significant step toward the development and deployment of rapid process analytical technologies for biopharmaceutical characterization.

Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology

In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and implementation of Process Analytical Technology (PAT), which aims to develop rapid and high-throughput strategies for real-time monitoring of bioprocesses key variables, in order to improve their quality control lines. In this context, spectroscopic techniques for data generation in combination with chemometrics represent alternative analytical methods for on-line critical process variables prediction. In this work, a novel multivariate calibration strategy for the at-line prediction of etanercept, a recombinant protein produced in a mammalian cells-based perfusion process, is presented. For data generation, samples from etanercept processes were daily obtained, from which fluorescence excitation-emission matrices were generated in the spectral ranges of 225.0 and 495.0 nm and 250.0 and 599.5 nm for excitation and emission modes, respectively. These data were correlated with etanercept concentration in supernatant (measured by an off-line HPLC-based reference univariate technique) by implementing different chemometric strategies, in order to build predictive models. Partial least squares (PLS) regression evidenced a non-linear relation between signal and concentration when observing actual vs. predicted concentrations. Hence, a non-parametric approach was implemented, based on a multilayer perceptron artificial neural network (MLP). The MLP topology was optimized by means of the response surface methodology. The prediction performance of MLP model was superior to PLS, since the first is able to cope with non-linearity in calibration models, reaching percentage mean relative error in predictions of about 7.0% (against 12.6% for PLS). This strategy represents a fast and inexpensive approach for etanercept monitoring, which conforms the principles of PAT.

Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques.

Near-infrared spectroscopy (NIRS) combined with chemometrics was used to analyze the main active ingredients including chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid, and chenodeoxycholic acid in the Tanreqing injection. In this paper, first, two hundred samples collected in the product line were divided into the calibration set and prediction set, and the reference values were determined by the High Performance Liquid Chromatography- Diode Array Detector/Evaporative Light Scattering Detector (HPLC-DAD/ELSD) method. Partial least squares (PLS) analysis was implemented as a linear method for models calibrated with different preprocessing means. Wavelet transformation (WT) was introduced as a variable selection technique by means of multiscale decomposition, and wavelet coefficients were employed as the input for modeling. Furthermore, two nonlinear approaches, least squares support vector machine (LS-SVM) and Gaussian process (GP), were applied to exploit the complicated relationship between the spectra and active ingredients. The optimal models for each ingredient were obtained by LS-SVM and GP methods. The performance of the final models was evaluated by the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). All of the models in the paper give a good calibration ability with an R value above 0.92, and the prediction ability is also satisfactory, with an R value higher than 0.85. The overall results demonstrate that nonlinear models are more stable and predictable than linear ones, and they will be more suitable for the CHM system when high accuracy analysis is required. It can be concluded that NIRS with the LS-SVM and GP modeling methods is promising for the implementation of process analytical technology (PAT) in the pharmaceutical industry of Chinese herbal injections (CHIs).

Process monitoring of virus-like particle reassembly by diafiltration with UV/Vis spectroscopy and light scattering.

Virus‐like particles (VLPs) have shown great potential as biopharmaceuticals in the market and in clinics. Nonenveloped, in vivo assembled VLPs are typically disassembled and reassembled in vitro to improve particle stability, homogeneity, and immunogenicity. At the industrial scale, cross‐flow filtration (CFF) is the method of choice for performing reassembly by diafiltration. Here, we developed an experimental CFF setup with an on‐line measurement loop for the implementation of process analytical technology (PAT). The measurement loop included an ultraviolet and visible (UV/Vis) spectrometer as well as a light scattering photometer. These sensors allowed for monitoring protein concentration, protein tertiary structure, and protein quaternary structure. The experimental setup was tested with three Hepatitis B core Antigen (HBcAg) variants. With each variant, three reassembly processes were performed at different transmembrane pressures (TMPs). While light scattering provided information on the assembly progress, UV/Vis allowed for monitoring the protein concentration and the rate of VLP assembly based on the microenvironment of Tyrosine‐132. VLP formation was verified by off‐line dynamic light scattering (DLS) and transmission electron microscopy (TEM). Furthermore, the experimental results provided evidence of aggregate‐related assembly inhibition and showed that off‐line size‐exclusion chromatography does not provide a complete picture of the particle content. Finally, a Partial‐Least Squares (PLS) model was calibrated to predict VLP concentrations in the process solution. Q2 values of 0.947–0.984 were reached for the three HBcAg variants. In summary, the proposed experimental setup provides a powerful platform for developing and monitoring VLP reassembly steps by CFF.