In-line Process Analytical Technology Research Articles

Towards Non-Destructive Quality Testing of Complex Biomedical Devices—A Generalized Closed-Loop System Approach Utilizing Real-Time In-Line Process Analytical Technology

Towards Non-Destructive Quality Testing of Complex Biomedical Devices—A Generalized Closed-Loop System Approach Utilizing Real-Time In-Line Process Analytical Technology

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.

Biokinetics modelling of lycopene-producing E. coli fermentation using PAT methodology

Process analytical technology (PAT), which espouses the use of in-line analytics to monitor critical process parameters (CPPs), critical quality attributes (CQAs) and mathematical analyses, was employed to elucidate underlying carbon-source metabolism of a lycopene-producing E. coli strain during non-induced and induced batch fermentations. In-line dielectric and Raman spectroscopies were deployed to monitor real-time culture biomass viability (X), primary substrate glucose (S), and intracellular lycopene production (P), together with off-line HPLC quantification of broth acetate (A). In-line PAT monitoring enabled identification of two distinct metabolic regimes during induced batch run with lycopene accumulation. Biokinetics mathematical models using differential-algebraic equations and Monod-type conversions were formulated to explain experimentally measured dynamic trends of the state variables X, S, A and P during the batch runs. Their optimized model parameters and seven dimensionless ratios reflected changes in concentrations or rates relative to glucose, acetate, or lycopene. The calculated rates of culture growth, glucose utilization, acetate accumulation, and lycopene production revealed salient underlying carbon metabolic shifts and transient trends that explained phenomenological observations: such as growth patterns, carbon source usage, overflow mechanism, cellular affinity or inhibitory effects, and lycopene cytotoxicity. These parameters and dimensionless ratios could be used to compare novel strains, develop in silico simulations for scale-up or troubleshoot batch-to-batch variations.

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.

Application of process analytical technology (PAT) in real-time monitoring of pharmaceutical cleaning process: Unveiling the cleaning mechanisms governing the cleaning-in-place (CIP)

We report for the first time the combination of inline process analytical technology and optical imaging to track and understand different cleaning mechanisms governing the cleaning process of Olanzapine. Clean-in-Place process parameters were studied through simultaneous inline process analytical technology and image analysis in a film flow apparatus. Methanol and water were compared as cleaning agents, and the effect of flow (0.5—1 L.min−1) and temperature (20–40 °C, and 60 °C for water) was investigated. The cleaning process was assessed in terms of cleaning time, volumetric efficiency, surface residue and governing phenomena. Raman spectroscopy was used to prove no degradation occurred during cleaning. Temperature increase improves the efficiency even when solubility is negligible. High variability was observed when detachment behaviour is dominant, and it is then advisable to prioritize dissolution in the final clean-in-place steps to improve repeatability. Results show that the cleaning process is governed by an interplay of dissolution and mechanical shear phenomena and is more efficient when solubility is significant. The combination of in-situ process analytical technology and optical imaging analysis during process development allows for the determination of the most efficient and repeatable conditions, which in turn significantly reduces solvent usage in pharmaceutical cleaning.

Control oriented modeling of twin-screw granulation in the ConsiGmaTM-25 production plant

ConsiGmaTM-25 is a continuous production plant integrating a twin-screw granulation, fluid bed drying, granule conditioning, and a tableting unit. The particle size distribution (PSD), active pharmaceutical ingredient (API) content, and liquid content of wet granules after twin-screw granulation affect the quality of intermediate and final products. This paper proposes methods for real-time monitoring of these quantities and control-oriented modeling of the granulator.The PSD of wet granules is monitored via an in-line process analytical technology (PAT) probe based on the spatial velocimetry principle. The algorithm for signal processing and evaluation of PSD characteristics is developed and applied to the acquired PSD data. A dynamic process model predicting PSD characteristics from granulation parameters is trained via the local linear model tree (LoLiMoT) approach. The experimental data required for the model training are collected via systematically designed excitation runs. Finally, the performance of the identified model is examined and verified by means of a new set of validation runs. Furthermore, an in-line PAT probe based on Raman spectroscopy is developed and integrated after the granulator. The API- and liquid content of produced wet granules are evaluated from the spectral data by means of chemometric modeling, and chemometric models are validated on a separate set of experimental data. The solutions proposed in this research can be used as a reliable (and necessary) basis for the development of advanced quality-by-design control concepts (e.g., PSD process control). Such concepts would ultimately improve the ConsiGmaTM-25 process performance in terms of robustness against disturbances and quality of intermediate and final products.

Refractive index as a process analytical technology to measure real time protein concentration for monitoring and control of commercial scale tangential flow filtration operations

AbstractBiological therapeutics are increasingly being formulated to high protein concentrations to decrease drug substance storage space and increase the flexibility of administration to patients. With the higher protein concentration targets comes added challenges to the downstream purification manufacturing process. Tangential flow filtration (TFF) operations are typically performed to reach target protein concentration. TFF operations for a given drug substance may include an Ultrafiltration and Diafiltration (UFDF) step, or a UFDF step followed by a single pass tangential flow filtration (SPTFF) step. Whether a TFF step achieves a target protein concentration is determined by at‐line protein concentration measurements performed at the completion of a process step. If the measured protein concentration is outside the specified range, the unit operation may need to be restarted, reprocessing may need to be performed, or a batch may need to be terminated. Out of specification protein concentration measurements may be a result of the TFF operation or sample measurement. Increased viscosities associated with high concentration TFF operations pose added challenges to the TFF process and sample measurement. Implementation of an inline process analytical technology (PAT) to monitor real‐time protein concentration during TFF operations has the potential to improve the accuracy of the operations in achieving target protein concentrations. This will result in improved process consistency and efficiency, increased operator confidence and decreased likelihood of batch failures. This paper studies the performance of a K‐Patents PR‐23 refractometer (Vaisala) as a PAT to monitor and control the UFDF and SPTFF unit operations of a commercial scale monoclonal antibody purification process.

Computer vision-based smart monitoring and control system for food drying: A study on carrot slices

Convective dryer embedded with computer vision (CV) system and load cell was used to continuously monitor carrot slices that are either unblanched or blanched (90 °C for 2 min) during product drying (35 °C, 35 % R.H., 3 m s−1 airflow). The CV system and load cell were selected as in-line Process Analytical Technology tools within a proactive Quality-by-Design framework and embedded for, i) monitoring of product features (i.e., weight, colour, and size); and ii) developing shrinkage-dependent moisture prediction models using linear regression. The evaluated shrinkage-dependent linear models showed superior performances (RMSE, 0.005–0.007) benchmarked against selected thin-layer models of increasing complexity. The study tested a smart-enabled prototype dryer with the potential for automation and integrating proactive quality strategies.

Artificial Neural Network for Fast and Versatile Model Parameter Adjustment Utilizing PAT Signals of Chromatography Processes for Process Control under Production Conditions

Preparative chromatography is a well-established operation in chemical and biotechnology manufacturing. Chromatography achieves high separation performances, but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime, and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regard to autonomous operation and batch to continuous processing modes, an advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies have already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process modeling. These models can be implemented as distinct digital twins as well as statistical process operation data analyzers. In order to utilize such models for advanced process control (APC), the model parameters have to be updated with the aid of inline Process Analytical Technology (PAT) data to describe the actual operational status. This updating process also includes any operational change phenomena that occur, and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium-related separation performance decrease due to adsorbent aging or feed and buffer composition changes. In order to track these changes, an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step, based on the simulation results of a distinct and previously experimentally validated process model. The model is implemented in the open source tool CasADi for Python. This allows the implementation of interfaces to process control systems, among others, with relatively low effort. Therefore, PAT signals can easily be incorporated for sufficient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on PAT and ANN predictions to derive optimal operation points with the model.

In-line cosmetic end-point detection of batch coating processes for colored tablets using multivariate image analysis

In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control.

Monitoring the hot-air drying process of organically grown apples (cv. Gala) using computer vision

The feasibility of using a computer-vision (CV) system embedded in a hot-air dryer for non-destructive and real-time monitoring of the drying behaviour of organic apples was investigated in the present study. Apple cylinders were subjected to anti-browning treatments with different dipping solutions (water as control, trehalose (4% w/v) or trehalose + ascorbic acid (4% w/v and 1% w/v)) and dipping pressures (atmospheric, 101.3 kPa and sub-atmospheric pressure, 50 kPa) followed by drying at 60 °C to a final dry basis moisture content of 0.18 g g−1. The CV system was used as an in-line process analytical technology (PAT) tool to capture images reflecting the physico-chemical changes during product drying coupled with in-line mass changes and off-line reference analyses. The spatial and colour changes from the image analysis described well the complex and non-homogenous nature of apple drying. The results of spatial changes allowed successful development of accurate linear prediction models for moisture content as a function of area shrinkage (on scaled variables) with excellent prediction capability (|BIAS| < 8.5 10−3, RMSE < 0.04, Adj-R2 ~ = 99%). Also, the CV system identified the differently pre-treated samples, particularly the dipping pressures as reflected by two linear models and their respective parameters. The obtained results demonstrate the versatile advantages of CV systems as an in-line tool for continuous, real-time monitoring of apples during drying. The insights from this study can provide a platform for applications of CV embedded ‘Smart dryers’ as an efficient monitoring and control system for industrial drying processes.

Process Characterization of Polyvinyl Acetate Emulsions Applying Inline Photon Density Wave Spectroscopy at High Solid Contents.

The high solids semicontinuous emulsion polymerization of polyvinyl acetate using poly (vinyl alcohol-co-vinyl acetate) as protective colloid is investigated by optical spectroscopy. The suitability of Photon Density Wave (PDW) spectroscopy as inline Process Analytical Technology (PAT) for emulsion polymerization processes at high solid contents (>40% (w/w)) is studied and evaluated. Inline data on absorption and scattering in the dispersion is obtained in real-time. The radical polymerization of vinyl acetate to polyvinyl acetate using ascorbic acid and sodium persulfate as redox initiator system and poly (vinyl alcohol-co-vinyl acetate) as protective colloid is investigated. Starved–feed radical emulsion polymerization yielded particle sizes in the nanometer size regime. PDW spectroscopy is used to monitor the progress of polymerization by studying the absorption and scattering properties during the synthesis of dispersions with increasing monomer amount and correspondingly decreasing feed rate of protective colloid. Results are compared to particle sizes determined with offline dynamic light scattering (DLS) and static light scattering (SLS) during the synthesis.

Characterization of Propionyl Phosphate Hydrolysis Kinetics by Data-Rich Experiments and In-Line Process Analytical Technology

In-depth characterization of reaction kinetics often requires a considerable amount of experimental results under various conditions. Recent advances in data-rich experimentation enable the collect...

Peptide Isolation via Spray Drying: Particle Formation, Process Design and Implementation for the Production of Spray Dried Glucagon

PurposeSpray drying plays an important role in the pharmaceutical industry for product development of sensitive bio-pharmaceutical formulations. Process design, implementation and optimisation require in-depth knowledge of process-product interactions. Here, an integrated approach for the rapid, early-stage spray drying process development of trehalose and glucagon on lab-scale is presented.MethodsSingle droplet drying experiments were used to investigate the particle formation process. Process implementation was supported using in-line process analytical technology within a data acquisition framework recording temperature, humidity, pressure and feed rate. During process implementation, off-line product characterisation provided additional information on key product properties related to residual moisture, solid state structure, particle size/morphology and peptide fibrillation/degradation.ResultsA psychrometric process model allowed the identification of feasible operating conditions for spray drying trehalose, achieving high yields of up to 84.67%, and significantly reduced levels of residual moisture and particle agglomeration compared to product obtained during non-optimal drying. The process was further translated to produce powders of glucagon and glucagon-trehalose formulations with yields of >83.24%. Extensive peptide aggregation or degradation was not observed.ConclusionsThe presented data-driven process development concept can be applied to address future isolation problems on lab-scale and facilitate a systematic implementation of spray drying for the manufacturing of sensitive bio-pharmaceutical formulations.

The optimization of process analytical technology for the inline quantification of multiple drugs in fixed dose combinations during continuous processing

Complications associated with uncontrolled hypertension are considered the major cause of premature death worldwide. Fixed-dose combinations (FDCs) offer an alternative approach to polypharmacy with the aim to improve patient compliance. Process Analytical Technology (PAT) is gaining momentum as a non-invasive, predictive tool to control the quality of drugs during continuous processing. PAT offers real-time quality control that can be built into the production line. However, the vast majority of studies reported in the literature have focused on quantifying a single drug during continuous processing. The aim of this study was to develop non-destructive, predictive inline PAT tools allowing for the simultaneous quantification of two antihypertensive drugs, Hydrochlorothiazide (HCTZ) and Ramipril (RMP), during the continuous manufacture of FDCs. A calibration set composed of HCTZ and RMP at concentration ranges of 6.5 to 40 and 2.5-15 (% w/w), respectively, were manufactured using hot melt extrusion. The extrudates were analysed during the process using inline Raman spectroscopy. Optimum wavenumber regions were observed at 200-400 and 630-730cm-1 for HCTZ, and 980-1100cm-1 for RMP using principal component analysis. Partial least squares (PLS) regression was performed to establish the predictive calibration models. The PLS developed models showed excellent linearity (R2=0.986 and 0.974), selectivity (PC1=98.6% and 91.9%) and accuracy (RMSEcv=1.586 and 0.645%) for HCTZ and RMP, respectively. Additionally, RMSEP values were reported as 1.237 and 1.007% for HCTZ and RMP, respectively, depicting good predictability for drug content in the validation set. The output of this study demonstrated that utilisation of the full potential of chemometrics, Raman spectroscopy can be used for the simultaneous inline quantification of multiple drugs in complex formulations. This facilitates the in-process quality control of FDCs and other multicomponent systems during continuous pharmaceutical production.

Monitoring Viscosity and Total Solids Content of Milk Protein Concentrate Using an Inline Acoustic Flowmeter at Laboratory Scale.

Control of milk concentrate viscosity and total solids (TS) content prior to spray drying can improve dairy ingredient manufacture. However, the availability of hygienic and appropriately pressure rated process viscometers for inline monitoring of viscosity is limited. An acoustic flowmeter (FLOWave) is an inline process analytical technology (PAT) tool that measures changes in acoustic signals in response to changes in liquid properties (i.e., acoustic transmission (AT), acoustic impedance (AI), temperature and volume flowrate). In this study, an acoustic flowmeter is evaluated as an inline PAT tool for monitoring viscosity of milk protein concentrate (MPC85), protein and TS content of (MPC85), and standardised MPC (sMPC). Laboratory scale experiments were carried out at 45 °C for five different concentrations (4–21%) of MPC85 and sMPC. Results showed that AT decreased with an increase in MPC85 viscosity (e.g., AT was 98.79 ± 0.04% and 86.65 ± 0.17% for 4% and 21% TS content, respectively). Non-linear regression was carried out to develop a relationship between AT and offline viscosity (R2 (coefficient of determination) value = 0.97 and standard error of prediction = 1.86 mPa·s). AI was observed to increase at higher protein and TS content which was dependent on protein to total solid ratio (P_TSR). Multiple linear regression was carried out to develop the relationship between AI, protein content, TS content and P_TSR. Results demonstrated that AI could be used to monitor the protein and TS content of milk protein concentrate (R2 > 0.96). Overall this study demonstrated the potential of an inline acoustic flowmeter for monitoring process viscosity, protein and TS during dairy concentrate processing.

Operando UV–Vis spectroscopy as potential in-line PAT system for size determination of functioning metal nanocatalysts

Metal nanoparticle-catalyzed reactions such as hydrogenation, cross-coupling, carbonylation, and hydroformylation reactions are the most widely used reactions in the pharmaceutical and fine chemical industries. However, there is no operando spectroscopic technique that exists to monitor the size of functioning nanocatalyst. By exploiting localized surface plasmon resonance of catalytically relevant nanostructures, such as monometallic (e.g., Pd, Pt, Ni, Rh, Au, and Cu) nanoparticles and bimetallic core–shell (e.g., Ag-Pd) nanoparticles, we show UV–Vis spectroscopy can be used to determine the size of functioning nanocatalyst. Based on our finite-difference time-domain simulations, it is possible to detect leaching of even a monolayer of atoms from the surface of widely used metal nanocatalysts with a conventional UV–Vis spectrometer. This sensitive, inexpensive and robust spectroscopic approach can be potentially used as in-line process analytical technology (PAT) in pharmaceutical development and manufacturing.

Digital UV/VIS imaging: A rapid PAT tool for crushing strength, drug content and particle size distribution determination in tablets.

The Process Analytical Technology (PAT) and the Quality-by-Design (QbD) approaches can efficiently facilitate the shift to the desired continuous manufacturing and real time release testing (RTRT). By this, it is vital to develop new, in-line analytical methods which fulfil the pharmaceutical requirements. The fast-developing digital imaging-based machine vision systems can provide revolutionary solutions not just in the automotive industry but in the pharmaceutical technology, as well. This study aimed to explore the capabilities of UV/VIS-based machine vision in tablet inspection as a PAT tool for the determination of compression force and crushing strength, drug content and drug distribution in tablets using meloxicam a yellow model drug. In the case of determining the compression force and crushing strength, the application of multivariate wavelet texture analysis (MWTA) based models provided relatively low prediction errors. To predict the drug content of meloxicam tablets CIELAB or RGB colorspace based algorithms were successfully developed and validated. UV/VIS imaging was also used to map the particle size distribution and spatial distribution of meloxicam, the results were compared to chemical maps obtained by Raman microscopy. Digital imaging combined with multivariate data analysis might be a valuable, high throughput, in-line PAT tool for automated inspection of pharmaceutical tablets.

Flow Water Proton NMR: In-Line Process Analytical Technology for Continuous Biomanufacturing.

Continuous manufacturing of biologics is one of the priorities of the biopharmaceutical industry. However, its widespread implementation is hampered by a lack of noninvasive/nondestructive process analytical technology (PAT) systems capable of real-time in-line monitoring of product flow parameters, such as concentration and/or aggregate content. We have previously demonstrated that, under nonflow conditions, the water proton transverse relaxation rate, R2(1H2O), is sensitive to protein concentration and aggregate content in biopharmaceutical formulations. In the present work, we explored the potential of water proton NMR under flow conditions (flow-wNMR) to use R2(1H2O) as a quantitative indicator of protein concentration variations and aggregate levels in the process flow. We show that, under flow conditions, R2(1H2O) is sensitive to rather small changes in protein concentration (<1 mg/mL) and is capable to detect variations in the aggregate content of <1%. Our findings suggest that flow-wNMR could be advantageously used as a real-time in-line noninvasive PAT for continuous biomanufacturing.

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame.

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization-Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.