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

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.

Usefulness of Applying Partial Least Squares Regression to T2 Relaxation Curves for Predicting the Solid form Content in Binary Physical Mixtures

This study applied partial least squares (PLS) regression to nuclear magnetic resonance (NMR) relaxation curves to quantify the free base of an active pharmaceutical ingredient powder. We measured the T₂ relaxation of intact and moisture-absorbed physical mixtures of tetracaine free base (TC) and its hydrochloride salt (TC·HCl). The obtained T₂ relaxation curves were analyzed by two methods, one using a previously reported T₂ relaxation time (T₂), and the other using PLS regression. The accuracy of estimating TC was inadequate when using previous T₂ values because the moisture-absorbed physical mixtures showed biphasic T₂ relaxation curves. By contrast, the entire measured whole of the T₂ relaxation curves was used as input variables and analyzed by PLS regression to quantify the content of TC in the moisture-absorbed TC/TC·HCl. Based on scatterplots of theoretical versus predicted TC, the obtained PLS model exhibited acceptable coefficients of determination and relatively low root mean squared error values for calibration and validation data. The statistical values confirmed that an accurate and reliable PLS model was created to quantify TC in even moisture-absorbed TC/TC·HCl. The bench-top low-field NMR instrument used to apply PLS regression to the T₂ relaxation curve may be a promising tool in process analytical technology.

Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production

The quality of tea leaves (e.g., their color, appearance, and taste) can be directly influenced by the tea production process, which is closely connected with the content of a number of chemical components formed during the production of the tea leaves. However, the production process is now controlled by people's experience, making its quality significantly different. NIRS is a time-saving, cost-saving, and nondestructive method. Therefore, it is necessary to introduce NIRS technology into the quality control of the tea production process. In this study, a quantitative analysis model of caffeine, epigallocatechin-3-gallate (EGCG), and moisture content was established by near-infrared spectroscopy (NIRS) which was united simultaneously with partial least squares (PLSR) for online process monitoring of tea production. The model parameters show that the established model has fine robustness and outstanding measuring accuracy. Then, the feasibility of the established method is verified by the traditional method. Through the verification of the precision of the instrument and the stability of the sample, it is clarified that the model can be further utilized to monitor tea product quality online in a productive process.

Time-domain NMR analysis for the determination of water content in pharmaceutical ingredients and wet granules

The application of time-domain NMR (TD-NMR) analysis to quantify water content in pharmaceutical ingredients is demonstrated. The initial phase of the study employed a range of disintegrants with defined amounts of added water (0-30% of the total weight) as samples; the disintegrants included croscarmellose sodium, corn starch, low-substituted hydroxypropyl cellulose, and crospovidone. After acquisition of the T₂ relaxation curves of the samples by TD-NMR measurements, these curves were analyzed by partial least squares (PLS) regression. According to the analysis, accurate and reliable PLS models were created that enabled accurate assessment of water content in the samples. A powder blend consisting of acetaminophen (paracetamol) and tablet excipients was also examined. Both a physical mixture of the powder blend and a wet granule prepared with a high-speed granulator were tested as samples in this study. Precise determination of water content in the powder blend was achieved by using the TD-NMR method. The accuracy of water content determination was equivalent to or better than that of the conventional loss on drying method. TD-NMR analysis samples were measured nondestructively and rapidly with low cost; thus, it could be a powerful quantitative method for determining water content in pharmaceuticals.

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling

The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters of natural products. It determines their market suitability, stability and shelf life and should preferably be constantly monitored. Miniaturized near-infrared (NIR) spectroscopy is a powerful method for on-site analysis, potentially fulfilling this requirement. Here, a feasibility study for applicability and analytical performance of three miniaturized NIR spectrometers and two benchtop instruments was evaluated in that scenario. The case study involved 192 dried plant extracts composed of five different plants harvested in different countries at various times within two years. The reference analysis by Karl Fischer titration determined the water content in this sample set between 1.36% and 6.47%. For the spectroscopic analysis half of the samples were laced with a drying agent to comply with the industry standard. The performance of various calibration models for NIR analysis was evaluated on the basis of root-mean square error of prediction (RMSEP) determined for an independent test set. Partial least squares regression (PLSR), Gaussian process regression (GPR) and artificial neural network (ANN) models were constructed for the spectral sets from each instrument. GPR and ANN models performed superior for all samples measured by handheld spectrometers and for native ones analyzed by benchtop instruments. Moreover, the accuracy penalty when analyzing native samples was lower for GPR and ANN prediction as well. With GPR or ANN calibration, miniaturized spectrometers offered the prediction performance at the level of the benchtop instruments. Therefore, in this analytical application miniaturized spectrometers can be used on-site with no penalty to the performance vs. laboratory-based NIR analysis.

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.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules' physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments. Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20-234 µm (X10), 98-1017 µm (X50), and 748-2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.

Development of a PAT tool for monitoring the Wurster coater performance

Real-time process analytical technology (PAT) is proposed as an effective approach for monitoring the performance of a Wurster coater. The coater was used for coating of 0.78 mm pharmaceutical pellets. The coating solution consisted of Hydroxypropyl methylcellulose/Poly ethylene glycol. During the coating process, small amounts of pellets (∼2 g) were sampled at 10 min. intervals and the pressure fluctuations were recorded at the same time. The signals were analyzed using the wavelet transform (WT) and decomposed into different sub-signals. Principal component analysis (PCA) was employed to the energies of these sub-signals, whose outliers were eliminated using the Mahalanobis distance method. The reference coating thickness data were obtained via spectrophotometry during the coating process. The partial least squares (PLS) regression was incorporated with the PCA for the development of a model for prediction of the coating thickness. The PLS regression revealed that the pressure fluctuations can be used to evaluate the thickness with a good precision. This study demonstrated the applicability of pressure fluctuations for the prediction of the coating thickness. This method can be regarded as a new robust, fast and non-intrusive PAT approach for monitoring the coating process which can be easily used by engineers and practitioners.

Process analytical technologies and injectable drug products: Is there a future?

Parametric release was the first subset of real time release testing (RTRT), applied to terminally sterilised injectable drug products. The objective was to offer the industry an alternative to the time and money consuming sterility testing, without compromising the sterility of the products. The rationale was that quality cannot be tested into products, instead it must be planned (the principle of quality by design, QbD). This can be implemented by setting appropriate in-process controls supported on process analytical technologies (PAT). Two of the most versatile and promising PAT tools are the near infrared spectroscopy (NIRS) and the Raman spectroscopy. However, their application to injectable drug product development and manufacturing has been scarce. This review has the objective to provide a framework for the practical implementation of the QbD approach to injectable formulations, including application of diverse risk assessment and factorial design tools. Finally, the actual application of PAT, namely NIRS and Raman spectroscopy, to injectable drug product analysis is addressed.

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.

Development of a NIR Method for the In-Line Quantification of the Total Polyphenolic Content: A Study Applied on Ajuga genevensis L. Dry Extract Obtained in a Fluid Bed Process

This study describes an innovative in-line near-infrared (NIR) process monitoring method for the quantification of the total polyphenolic content (TPC) of Ajuga genevensis dry extracts. The dry extract was obtained in a fluidized bed processor, by spraying and adsorbing a liquid extract onto an inert powder support. NIR spectra were recorded continuously during the extract’s spraying process. For the calibration of the in-line TPC quantification method, samples were collected during the entire process. The TPC of each sample was assessed spectroscopically, by applying a UV-Vis reference method. The obtained values were further used in order to develop a quality OPLS prediction model by correlating them with the corresponding NIR spectra. The final dry extract registered good flowability and compressibility properties, a concentration in active principles three times higher than the one of the liquid extract and an overall process yield of 85%. The average TPC’s recovery of the NIR in-line prediction method, compared with the reference UV-Vis one, was 98.7%, indicating a reliable monitoring method which provided accurate predictions of the TPC during the process, permitting a good process overview and enabling us to establish the process’s end point at the exact moment when the product reaches the desired TPC concentration.

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.

An approach combining real-time release testing with near-infrared spectroscopy to improve quality control efficiency of Rhizoma paridis

Raw material examination is a critical process in the industrial production of traditional Chinese medicine (TCM); high accuracy and minimal time consumption are both required. In this study, near-infrared (NIR) spectroscopy was applied to improve the quality control efficiency of Rhizoma paridis. Partial least squares regression (PLSR) was first used to develop quantitative calibration models, and the discriminant analysis model was established to qualitatively discriminate the qualified samples from the unqualified samples. These two established NIR models were applied for real-time release testing (RTRT) of R. paridis. R. paridis saponins (RPS)≥0.6% and moisture ≤12% were used as the quantitative releasing criteria of RTRT according to the Chinese Pharmacopoeia. Qualified samples classified by the discriminant analysis model were deemed to meet the qualitative releasing criterion of RTRT. Using the established quantitative model, 24 samples were allowed to be released to the subsequent production processes with 100% accuracy. For the qualitative RTRT analysis, three samples were misclassified as the unqualified class and were released unsuccessfully, the accuracy of the qualitative RTRT was 90%. Therefore, the quantitative RTRT was more feasible for actual manufacturing processes. Based on this study, a rapid and effective quantitative NIR spectroscopic method was proposed for the RTRT of R. paridis. The combination of RTRT and NIR spectroscopy could be a potential tool to improve the quality control efficiency of R. paridis.

Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Design of experiments-based monitoring of critical quality attributes for the spray-drying process of insulin by NIR spectroscopy

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q (2) of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.

Inline real-time near-infrared granule moisture measurements of a continuous granulation-drying-milling process

The purpose of this research was to use inline real-time near-infrared (NIR) to measure the moisture content of granules manufactured using a commercial production scale continuous twin-screw granulator fluid-bed dryer milling process. A central composite response surface statistical design was used to study the effect of inlet air temperature and dew point on granule moisture content. The NIR moisture content was compared to Karl Fischer (KF) and loss on drying (LOD) moisture determinations. Using multivariate analysis, the data showed a statistically significant correlation between the conventional methods and NIR. The R(2) values for predicted moisture content by NIR versus KF and predicted moisture values by NIR versus LOD were 0.94 (p < 0.00001) and 0.85 (p < 0.0002), respectively. The adjusted R(2) for KF versus LOD correlation was 0.85 (p < 0.0001). Analysis of the response surface design data showed that inlet air temperature over a range of 35-55°C had a significant linear impact on granule moisture content as measured by predicted NIR (adjusted R(2) = 0.84, p < 0.02), KF (adjusted R(2) = 0.91, p < 0.0001), and LOD (adjusted R(2) = 0.85, p < 0.0006). The inlet air dew point range of 10-20°C did not have a significant impact on any of the moisture measurements.

In-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz based on qualitative and quantitative uses of near-infrared spectroscopy

The application of near-infrared (NIR) spectroscopy for in-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz was investigated. For NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm pathlength flow cell were utilized to collect spectra in real-time. High performance liquid chromatography (HPLC) was used as a reference method to determine scutellarein in extract solution. Partial least squares regression (PLSR) calibration model of Savitzky-Golay smoothing NIR spectra in the 5450-10,000 cm(-1) region gave satisfactory predictive results for scutellarein. The results showed that the correlation coefficients of calibration and cross validation were 0.9967 and 0.9811, respectively, and the root mean square error of calibration and cross validation were 0.044 and 0.105, respectively. Furthermore, both the moving block standard deviation (MBSD) method and conformity test were used to identify the end point of extraction process, providing real-time data and instant feedback about the extraction course. The results obtained in this study indicated that the NIR spectroscopy technique provides an efficient and environmentally friendly approach for fast determination of scutellarein and end point control of extraction process.

Multivariate analysis of phenol in freeze-dried and spray-dried insulin formulations by NIR and FTIR

Dehydration is a commonly used method to stabilise protein formulations. Upon dehydration, there is a significant risk the composition of the formulation will change especially if the protein formulation contains volatile compounds. Phenol is often used as excipient in insulin formulations, stabilising the insulin hexamer by changing the secondary structure. We have previously shown that it is possible to maintain this structural change after drying. The aim of this study was to evaluate the residual phenol content in spray-dried and freeze-dried insulin formulations by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy using multivariate data analysis. A principal component analysis (PCA) and partial least squares (PLS) projections were used to analyse spectral data. After drying, there was a difference between the two drying methods in the phenol/insulin ratio and the water content of the dried samples. The spray-dried samples contained more water and less phenol compared with the freeze-dried samples. For the FTIR spectra, the best model used one PLS component to describe the phenol/insulin ratio in the powders, and was based on the second derivative pre-treated spectra in the 850-650 cm(-1) region. The best PLS model based on the NIR spectra utilised three PLS components to describe the phenol/insulin ratio and was based on the standard normal variate transformed spectra in the 6,200-5,800 cm(-1) region. The root mean square error of cross validation was 0.69% and 0.60% (w/w) for the models based on the FTIR and NIR spectra, respectively. In general, both methods were suitable for phenol quantification in dried phenol/insulin samples.

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 granulation rate processes using three PAT tools in a pilot-scale fluidized bed

The purpose of this research was to analyze and compare the responses of three Process Analytical Technology (PAT) techniques applied simultaneously to monitor a pilot-scale fluidized bed granulation process. Real-time measurements using focused beam reflectance measurement (Lasentec FBRM) and near-infra red spectroscopy (Bruker NIR) were taken by inserting in-line probes into the fluidized bed. Non-intrusive acoustic emission measurements (Physical Acoustic AE) were performed by attaching piezoelectric sensors on the external wall of the fluidized bed. Powder samples were collected at regular intervals during the granulation process and characterized offline using laser diffraction, scanning electron microscopy, stereo-optical microscopy and loss on drying method. PAT data comprising chord length distribution and chord count (from FBRM), absorption spectra (from NIR) and average signal levels and counts (from AE) were compared with the particle properties measured using offline samples. All three PAT techniques were able to detect the three granulation regimes or rate processes (wetting and nucleation, consolidation and growth, breakage) to varying degrees of sensitivity. Being dependent on optical signals, the sensitivities of the FBRM and NIR techniques were susceptible to fouling on probe windows. The AE technique was sensitive to background fluidizing air flows and external interferences. The sensitivity, strengths and weaknesses of the PAT techniques examined may facilitate the selection of suitable PAT tools for process development and scale-up studies.