Process Analytical Technology Tool Research Articles

Parameter Optimization and Capacitance-Based Monitoring of In Situ Cell Detachment in Microcarrier Cultures

This study delves into the scale-down optimization of the in situ cell detachment process for MA 104 cells cultivated on Cytodex 1 microcarriers (MCs). Through a systematic exploration, critical operational parameters—the agitation speed, incubation time, Trypsin–EDTA volume and corresponding activity, and washing steps—were identified as key factors influencing the efficiency and scalability of in situ cell detachment in microcarrier-based cell culture. Maintaining an appropriate agitation speed (1.25 × Njs, minimum agitation speed at which no microcarriers remain stationary for the signification period of 5 s), optimizing the Trypsinization incubation time (up to 60 min), and implementing multiple washing steps (two times) post-medium removal were found to be crucial for efficient cell detachment and subsequent growth. Our study demonstrates the feasibility of reducing the final Trypsin volume to 50 mL per gram of microcarrier while maintaining a Trypsin activity above 380 USP/mL. These conditions ensure complete cell dissociation and improve the cost effectiveness in large-scale productions. Additionally, we introduced real-time monitoring using a capacitance sensor during in situ cell detachment. This method has proven to be an effective process analytical technology (PAT) tool for tracking the cell detachment progress and efficiency. It allows for the prediction of cell detachment based on signals recorded between 3 and 7 min of Trypsinization, enabling rapid process decisions without the need for offline sampling, thereby enhancing the overall process control. This systematic approach not only optimizes in situ cell detachment processes but also has significant implications for the scalability and efficiency of microcarrier-based cell culture systems.

Investigation of Near Infrared and Raman Fibre Optic Process Sensors for Protein Determination in Milk Protein Concentrate

This study investigated the potential of two fibre optic process sensors based on near infrared (NIR) or Raman spectroscopic technology for protein measurement in milk protein concentrate (MPC). Partial least squares (PLS) models were developed using NIR, Raman, and fusion of NIR and Raman spectra data. Calibration models developed were optimized by selecting different spectral pre-treatment methods and spectral regions. Overall, the three optimal models (NIR, Raman, fused NIR and Raman) yielded R2p values >0.9 and RMSEP values in the range of 0.168-0.185%. The optimised fusion model outperformed all Raman models and had a similar protein prediction accuracy (R2p = 0.911 and RMSEP = 0.178%) compared to the optimised NIR model (R2p = 0.917 and RMSEP =0.168%). Results of the study demonstrated that both NIR and Raman process probes can be used as process analytical technology (PAT) tools for inline protein measurements of MPC post membrane filtration.

THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 – 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 – 7 %) whereas at low contents (5 %) significantly higher values (9 – 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.

Experimental design of a film flow cleaning rig equipped with in-line process analytical technology (PAT) tool for real-time monitoring

The main purpose of this research was to develop an experimental film flow cleaning rig that can be combined with Process analytical technology (PAT) tools to reduce cleaning time and costs. Here, we show that the use of in-line UV–Vis was successful for real-time monitoring of the cleaning process of olanzapine as a challenging residue to clean. The cleaning process was found to be affected by the properties of the olanzapine soil, and the study showed the competing effects of mechanical lift-off and dissolution action with methanol as a solvent. However, The method is limited by the cleaning mechanisms, with the dissolution being the only mechanism that can be accurately quantified using an in-line UV–Vis PAT tool. This experimental approach can be used to optimize cleaning process conditions and solvent choices at the bench scale before deployment. The material of which the cleaning rig was printed limited the solvent that could be used for this study, and future modifications will include a more chemical-resistant material.

Investigation of the rehydration characteristics of dairy and infant formula powders using focused beam reflectance measurement and electrical resistance tomography

The potential of focused beam reflectance measurement (FBRM) and electrical resistance tomography (ERT) was evaluated as on‐line process analytical technologies (PAT) for monitoring powder rehydration. Skim milk powder (SMP) and stage one infant formulas were evaluated. Among all samples, SMP had the greatest conductivity by ERT (P < 0.05), this was confirmed by off‐line methods, that is, SMP had a higher solubility, whereas infant formulas had lower solubility. Particle counts for fines of all samples reached peak values ~450 s. Results demonstrated PAT tools offer capability for rehydration characterisation of dairy and non‐dairy powders.

Simple and fast one-step FRET assay of therapeutic mAb bevacizumab using anti-idiotype DNA aptamer for process analytical technology

We developed an aptamer-based fluorescence resonance energy transfer (FRET) assay capable of recognizing therapeutic monoclonal antibody bevacizumab and rapidly quantifying its concentration with just one mixing step. In this assay, two fluorescent dyes (fluorescein and tetramethylrhodamine) labeled aptamers bind to two Fab regions on bevacizumab, and FRET fluorescence is observed when both dyes come into close proximity. We optimized this assay in three different formats, catering to a wide range of analytical needs. When applied to hybridoma culture samples in practical settings, this assay exhibited a signal response that was concentration-dependent, falling within the range of 50–2000 μg/mL. The coefficients of determination (r2) ranged from 0.998 to 0.999, and bias and precision results were within ±24.0 % and 20.3 %, respectively. Additionally, during thermal and UV stress testing, this assay demonstrated the ability to detect denatured samples in a manner comparable to conventional Size Exclusion Chromatography. Notably, it offers the added advantage of detecting decreases in binding activity without changes in molecular weight. In contrast to many existing process analytical technology tools, this assay not only identifies bevacizumab but also directly measures the quality attributes related to mAb efficacy, such as the binding activity. As a result, this assay holds great potential as a valuable platform for providing highly reliable quality attribute information in real-time. We consider this will make a significant contribution to the worldwide distribution of high-quality therapeutic mAbs in various aspects of antibody manufacturing, including production monitoring, quality control, commercial lot release, and stability testing.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

Expediting online liquid chromatography for real-time monitoring of product attributes to advance process analytical technology in downstream processing of biopharmaceuticals

The application of Process Analytical Technology (PAT) principles for manufacturing of biotherapeutics proffers the prospect of ensuring consistent product quality along with increased productivity as well as substantial cost and time savings. Although this paradigm shift from a traditional, rather rigid manufacturing model to a more scientific, risk-based approach has been advocated by health authorities for almost two decades, the practical implementation of PAT in the biopharmaceutical industry is still limited by the lack of fit-for-purpose analytical methods. In this regard, most of the proposed spectroscopic techniques are sufficiently fast but exhibit deficiencies in terms of selectivity and sensitivity, while well-established offline methods, such as (ultra-)high-performance liquid chromatography, are generally considered as too slow for this task. To address these reservations, we introduce here a novel online Liquid Chromatography (LC) setup that was specifically designed to enable real-time monitoring of critical product quality attributes during time-sensitive purification operations in downstream processing. Using this online LC solution in combination with fast, purpose-built analytical methods, sampling cycle times between 1.30 and 2.35 min were achieved, without compromising on the ability to resolve and quantify the product variants of interest. The capabilities of our approach are ultimately assessed in three case studies, involving various biotherapeutic modalities, downstream processes and analytical chromatographic separation modes. Altogether, our results highlight the expansive opportunities of online LC based applications to serve as a PAT tool for biopharmaceutical manufacturing.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

Online monitoring of protein refolding in inclusion body processing using intrinsic fluorescence

Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01 g L-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}. The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.Graphical abstractReal-time intrinsic fluorescence monitoring in protein refolding

Multi-dimensional technology - Recent advances and applications for biotherapeutic characterization.

This review provides an overview of the latest advancements and applications in multi-dimensional liquid chromatography coupled with mass spectrometry (mD-LC-MS), covering aspects such as inter-laboratory studies, digestion strategy, trapping column, and multi-level analysis. The shift from an offline to an online workflow reduces sample processing artifacts, analytical variability, analysis time, and the labor required for data acquisition. Over the past few years, this technique has demonstrated sufficient maturity for application across a diverse range of complex products. Moreover, there is potential for this strategy to evolve into an integrated process analytical technology tool for the real-time monitoring of monoclonal antibody quality. This review also identifies emerging trends, including its application to new modalities, the possibility of evaluating biological activity within the mD-LC set-up, and the consideration of multi-dimensional capillary electrophoresis as an alternative to mD-LC. As mD-LC-MS continues to evolve and integrate emerging trends, it holds the potential to shape the next generation of analytical tools, offering exciting possibilities for enhanced characterization and monitoring of complex biopharmaceutical products.

Cryogen-free 400-MHz nuclear magnetic resonance spectrometer as a versatile tool for pharmaceutical process analytical technology.

The discovery of new ceramic materials containing Ba-La-Cu oxides in 1986 that exhibited superconducting properties at high temperatures in the range of 35 K or higher, recognized with the Nobel Prize in Physics in 1987, opened a new world of opportunities for nuclear magnetic resonance (NMRs) and magnetic resonance imaging (MRIs) to move away from liquid cryogens. This discovery expands the application of high temperature superconducting (HTS) materials to fields beyond the chemical and medical industries, including electrical power grids, energy, and aerospace. The prototype 400-MHz cryofree HTS NMR spectrometer installed at Amgen's chemistry laboratory has been vital for a variety of applications such as structure analysis, reaction monitoring, and CASE-3D studies with RDCs. The spectrometer has been integrated with Amgen's chemistry and analytical workflows, providing pipeline project support in tandem with other Kinetic Analysis Platform technologies. The 400-MHz cryofree HTS NMR spectrometer, as the name implies, does not require liquid cryogens refills and has smaller footprint that facilitates installation into a chemistry laboratory fume hood, sharing the hood with a process chemistry reactor. Our evaluation of its performance for structural analysis with CASE-3D protocol and for reaction monitoring of Amgen's pipeline chemistry was successful. We envision that the HTS magnets would become part of the standard NMR and MRI spectrometers in the future. We believe that while the technology is being developed, there is room for all magnet options, including HTS, low temperature superconducting (LTS) magnets, and low field benchtop NMRs with permanent magnets, where utilization will be dependent on application type and costs.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029g/L, respectively, and 0.2483g/L for the total protein concentration. We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

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.

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.