In-Line Monitoring of Moisture Content in Fluid Bed Dryers Using Near-IR Spectroscopy with Consideration of Sampling Effects on Method Accuracy

Near-infrared analysis of nanofibrillated cellulose aerogel manufacturing

Biomaterial aerogel fabrication by freeze-drying must be further improved to reduce the costs of lengthy freeze-drying cycles and to avoid the formation of spongy cryogels and collapse of the aerogel structures. Residual water content is a critical quality attribute of the freeze-dried product, which can be monitored in-line with near-infrared (NIR) spectroscopy. Predictive models of NIR have not been previously applied for biomaterials and the models were mostly focused on the prediction of only one formulation at a time. We recorded NIR spectra of different nanofibrillated cellulose (NFC) hydrogel formulations during the secondary drying and set up a partial least square regression model to predict their residual water contents. The model can be generalized to measure residual water of formulations with different NFC concentrations and the excipients, and the NFC fiber concentrations and excipients can be separated with the principal component analysis. Our results provide valuable information about the freeze-drying of biomaterials and aerogel fabrication, and how NIR spectroscopy can be utilized in the optimization of residual water content.

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.

Computational Modeling of Fluidized Beds with a Focus on Pharmaceutical Applications: A Review

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model

Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor

PURPOSE

The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry-Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control.

METHODS

Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance.

RESULTS

The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection.

CONCLUSIONS

The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.

Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results.

Inline acoustic monitoring to determine fluidized bed performance during pharmaceutical coating

Fluidized beds are used by pharmaceutical manufacturers for multi-particulate drug coating. They provide effective mass and heat transfer; however, unit optimization can be difficult due to the multivariate nature of a fluidized bed system. This research explores the use of passive acoustic emissions monitoring as a method to improve temperature management during pellet coating. A piezoelectric microphone was placed inside the exhaust of a conical top spray fluidized bed. Spherical 1000 μm pellets were coated while recording acoustic emissions. Fluidization air temperature was adjusted between trials as a controlled variable to determine if pellet drying rate could be extracted from the data. During each trial, pellets became damp as the coating solution was applied. Drying stages were used to remove moisture whereby pellet fluidization continued without spraying. The moving standard deviation of the acoustic emissions increased by approximately 40 mV during each 2-min coating stage. The emissions then decreased during drying. This decrease was at a rate proportional to pellet drying independently measured at each controlled temperature. The overall coating-drying emissions profile was similar for trials using either sugar or Acryl-EZE® coating solutions. Passive acoustic emissions monitoring is non-invasive and provides reliable coating and drying information during fluidized bed operation.

Near-infrared spectroscopy monitoring and control of the fluidized bed granulation and coating processes-A review

The fluidized bed granulation and pellets coating technologies are widely used in pharmaceutical industry, because the particles made in a fluidized bed have good flowability, compressibility, and the coating thickness of pellets are homogeneous. With the popularization of process analytical technology (PAT), real-time analysis for critical quality attributes (CQA) was getting more attention. Near-infrared (NIR) spectroscopy, as a PAT tool, could realize the real-time monitoring and control during the granulating and coating processes, which could optimize the manufacturing processes. This article reviewed the application of NIR spectroscopy in CQA (moisture content, particle size and tablet/pellet thickness) monitoring during fluidized bed granulation and coating processes. Through this review, we would like to provide references for realizing automated control and intelligent production in fluidized bed granulation and pellets coating of pharmaceutical industry.

Effect of Shear Applied During a Pharmaceutical Process on Near Infrared Spectra

This study describes changes observed in the near-infrared (NIR) diffuse reflectance (DR) spectra of pharmaceutical tablets after these tablets were subjected to different levels of strain (exposure to shear) during the mixing process. Powder shearing is important in the mixing of powders that are cohesive. Shear stress is created in a system by moving one surface over another causing displacements in the direction of the moving surface and is part of the mixing dynamics of particulates in many industries including the pharmaceutical industry. In continuous mixing, shear strain is developed within the process when powder particles are in constant movement and can affect the quality attributes of the final product such as dissolution. These changes in the NIR spectra could affect results obtained from NIR calibration models. The aim of the study was to understand changes in the NIR diffuse reflectance spectra that can be associated with different levels of strain developed during blend shearing of laboratory samples. Shear was applied using a Couette cell and tablets were produced using a tablet press emulator. Tablets with different shear levels were measured using NIR spectroscopy in the diffuse reflectance mode. The NIR spectra were baseline corrected to maintain the scattering effect associated with the physical properties of the tablet surface. Principal component analysis was used to establish the principal sources of variation within the samples. The angular dependence of elastic light scattering shows that the shear treatment reduces the size of particles and produces their uniform and highly isotropic distribution. Tablet compaction further reduces the diffuse component of scattering due to realignment of particles.

The Future of Pharmaceutical Manufacturing Sciences

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

Monitoring the dissolution of Active Pharmaceutical Ingredient and TPGS in real time via IR spectroscopy during the manufacturing of liquid dosage formulation

Infrared spectroscopy is used to monitor the dissolution of the Active Pharmaceutical Ingredient (API) and an excipient (vitamin E - TPGS) during manufacturing of a liquid pharmaceutical formulation. The goal of the analysis is to explore options for real-time, on screen, and quantitative monitoring of these two components by using an iC10 instrument. As is common, the first step in the approach is to create respective calibration models for the two components and then apply those models on the spectra obtained from scale-up batches. Interestingly, while the API dissolves at the room temperature, TPGS dissolves at an acceptable rate at 50 °C so both temperatures have to be considered. It is shown that univariate models of sufficient accuracy can be developed with a straightforward applicability to the scale-up batches spectra and providing reasonably accurate estimates of the API and TPGS concentrations. Some limitations of the software on the employed instrument may diminish the prospect for the quantitative analysis of the components of interest in this formulation.

Applying terahertz technology for nondestructive detection of crack initiation in a film-coated layer on a swelling tablet

Here, we describe a nondestructive approach using terahertz wave to detect crack initiation in a film-coated layer on a drug tablet. During scale-up and scale-down of the film coating process, differences in film density and gaps between the film-coated layer and the uncoated tablet were generated due to differences in film coating process parameters, such as the tablet-filling rate in the coating machine, spray pressure, and gas-liquid ratio etc. Tablets using the PEO/PEG formulation were employed as uncoated tablets. We found that heat and humidity caused tablets to swell, thereby breaking the film-coated layer. Using our novel approach with terahertz wave nondestructively detect film surface density (FSD) and interface density differences (IDDs) between the film-coated layer and an uncoated tablet. We also found that a reduced FSD and IDD between the film-coated layer and uncoated tablet increased the risk of crack initiation in the film-coated layer, thereby enabling us to nondestructively predict initiation of cracks in the film-coated layer. Using this method, crack initiation can be nondestructively assessed in swelling tablets after the film coating process without conducting accelerated stability tests, and film coating process parameters during scale-up and scale-down studies can be appropriately established.

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

Within the Process Analytical Technology (PAT) framework, it is of utmost importance to obtain critical process and formulation information during pharmaceutical processing. Process analyzers are the essential PAT tools for real-time process monitoring and control as they supply the data from which relevant process and product information and conclusions are to be extracted. Since the last decade, near infrared (NIR) and Raman spectroscopy have been increasingly used for real-time measurements of critical process and product attributes, as these techniques allow rapid and nondestructive measurements without sample preparations. Furthermore, both techniques provide chemical and physical information leading to increased process understanding. Probes coupled to the spectrometers by fiber optic cables can be implemented directly into the process streams allowing continuous in-process measurements. This paper aims at reviewing the use of Raman and NIR spectroscopy in the PAT setting, i.e., during processing, with special emphasis in pharmaceutics and dosage forms.

Monitoring fluidized bed drying of pharmaceutical granules

Placebo granules consisting of lactose monohydrate, corn starch, and polyvinylpyrrolidone were prepared using de-ionized water in a high-shear mixer and dried in a conical fluidized bed dryer at various superficial gas velocities. Acoustic, vibration, and pressure data obtained over the course of drying was analyzed using various statistical, frequency, fractal, and chaos techniques. Traditional monitoring methods were also used for reference. Analysis of the vibration data showed that the acceleration levels decreased during drying and reached a plateau once the granules had reached a final moisture content of 1–2 wt.%; this plateau did not differ significantly between superficial gas velocities, indicating a potential criterion to support drying endpoint identification. Acoustic emissions could not reliably identify the drying endpoint. However, high kurtosis values of acoustic emissions measured in the filtered air exhaust corresponded to high entrainment rates. This could be used for process control to adjust the fluidization gas velocity to allow drying to continue rapidly while minimizing entrainment and possible product losses.

Near infrared spectroscopy in the development of solid dosage forms

The use of near infrared (NIR) spectroscopy has rapidly grown partly due to demands of process analytical applications in the pharmaceutical industry. Furthermore, newest regulatory guidelines have advanced the increase of the use of NIR technologies. The non-destructive and non-invasive nature of measurements makes NIR a powerful tool in characterization of pharmaceutical solids. These benefits among others often make NIR advantageous over traditional analytical methods. However, in addition to NIR, a wide variety of other tools are naturally also available for analysis in pharmaceutical development and manufacturing, and those can often be more suitable for a given application. The versatility and rapidness of NIR will ensure its contribution to increased process understanding, better process control and improved quality of drug products. This review concentrates on the use of NIR spectroscopy from a process research perspective and highlights recent applications in the field.

Process analytical applications of Raman spectroscopy

There is an increasing demand for new approaches to understand the chemical and physical phenomena that occur during pharmaceutical unit operations. Obtaining real-time information from processes opens new perspectives for safer and more efficient manufacture of pharmaceuticals. Raman spectroscopy provides a molecular level insight into processing, and therefore it is a future process analytical tool. In this review, different applications of Raman spectroscopy in the field of process analysis of pharmaceutical solid dosage forms are summarized. In addition, pitfalls associated with interfacing to the process environment and challenges within data management are discussed.

Granule Characterization During Fluid Bed Drying by Development of a Near Infrared Method to Determine Water Content and Median Granule Size

PURPOSE

Water content and granule size are recognized as critical process and product quality parameters during drying. The purpose of this study was to enlighten the granule behavior during fluid bed drying by monitoring the major events i.e. changes in water content and granule size.

METHODS

NIR spectra collected during drying and water content of sampled granules were correlated by principal component analysis (PCA) and partial least squares regression (PLSR). NIR spectra of dried granules were correlated to median granule size in a second PCA and PLSR.

RESULTS

The NIR water model discriminates between various stages in fluid-bed drying. The water content can be continuously predicted with errors comparable to the reference method. The four PLS factors of the granule size model are related to primary particle size of lactose, median granule size exceeding primary particle size and amorphous content of granules. The small prediction errors enable size discrimination between fines and granules.

CONCLUSION

For product quality reasons, discrimination between drying stages and end-point monitoring is highly important. Together with the possibilities to determine median granule size and to distinguish fines this approach provides a tool to design an optimal drying process.