A. Burggraeve

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.

In-Line and Real-Time Process Monitoring of a Freeze Drying Process Using Raman and NIR Spectroscopy as Complementary Process Analytical Technology (PAT) Tools

The aim of the present study was to examine the complementary properties of Raman and near infrared (NIR) spectroscopy as PAT tools for the fast, noninvasive, nondestructive and in-line process monitoring of a freeze drying process. Therefore, Raman and NIR probes were built in the freeze dryer chamber, allowing simultaneous process monitoring. A 5% (w/v) mannitol solution was used as model for freeze drying. Raman and NIR spectra were continuously collected during freeze drying (one Raman and NIR spectrum/min) and the spectra were analyzed using principal component analysis (PCA) and multivariate curve resolution (MCR). Raman spectroscopy was able to supply information about (i) the mannitol solid state throughout the entire process, (ii) the endpoint of freezing (endpoint of mannitol crystallization), and (iii) several physical and chemical phenomena occurring during the process (onset of ice nucleation, onset of mannitol crystallization). NIR spectroscopy proved to be a more sensitive tool to monitor the critical aspects during drying: (i) endpoint of ice sublimation and (ii) monitoring the release of hydrate water during storage. Furthermore, via NIR spectroscopy some Raman observations were confirmed: start of ice nucleation, end of mannitol crystallization and solid state characteristics of the end product. When Raman and NIR monitoring were performed on the same vial, the Raman signal was saturated during the freezing step caused by reflected NIR light reaching the Raman detector. Therefore, NIR and Raman measurements were done on a different vial. Also the importance of the position of the probes (Raman probe above the vial and NIR probe at the bottom of the sidewall of the vial) in order to obtain all required critical information is outlined. Combining Raman and NIR spectroscopy for the simultaneous monitoring of freeze drying allows monitoring almost all critical freeze drying process aspects. Both techniques do not only complement each other, they also provided mutual confirmation of specific conclusions.

Raman spectroscopy as a process analytical technology (PAT) tool for the in-line monitoring and understanding of a powder blending process

The aim of this study is to propose a strategy to implement a PAT system in the blending step of pharmaceutical production processes. It was examined whether Raman spectroscopy can be used as PAT tool for the in-line and real-time endpoint monitoring and understanding of a powder blending process. A screening design was used to identify and understand the significant effects of two process variables (blending speed and loading of the blender) and of a formulation variable (concentration of active pharmaceutical ingredient (API): diltiazem hydrochloride) upon the required blending time (response variable). Interactions between the variables were investigated as well. A Soft Independent Modelling of Class Analogy (SIMCA) model was developed to determine the homogeneity of the blends in-line and real-time using Raman spectroscopy in combination with a fiber optical immersion probe. One blending experiment was monitored using Raman and NIR spectroscopy simultaneously. This was done to verify whether two independent monitoring tools can confirm each others endpoint conclusions. The analysis of the experimental design results showed that the measured endpoints were excessively rounded due to the large measurement intervals relative to the first blending times. This resulted in effects and critical effects which cannot be interpreted properly. To be able to study the effects properly, the ratio between the blending times and the measurement intervals should be sufficiently high. In this study, it anyway was demonstrated that Raman spectroscopy is a suitable PAT tool for the endpoint control of a powder blending process. Raman spectroscopy not only allowed in-line and real-time monitoring of the blend homogeneity, but also helped to understand the process better in combination with experimental design. Furthermore, the correctness of the Raman endpoint conclusions was demonstrated for one process by using a second independent endpoint monitoring tool (NIR spectroscopy). Hence, the use of two independent techniques for the control of one response variable not only means a mutual confirmation of both methods, but also provides a higher certainty in the determined endpoint.

Process analytical tools for monitoring, understanding, and control of pharmaceutical fluidized bed granulation: A review

Fluidized bed granulation is a widely applied wet granulation technique in the pharmaceutical industry to produce solid dosage forms. The process involves the spraying of a binder liquid onto fluidizing powder particles. As a result, the (wetted) particles collide with each other and form larger permanent aggregates (granules). After spraying the required amount of granulation liquid, the wet granules are rapidly dried in the fluid bed granulator. Since the FDA launched its Process Analytical Technology initiative (and even before), a wide range of analytical process sensors has been used for real-time monitoring and control of fluid bed granulation processes. By applying various data analysis techniques to the multitude of data collected from the process analyzers implemented in fluid bed granulators, a deeper understanding of the process has been achieved. This review gives an overview of the process analytical technologies used during fluid bed granulation to monitor and control the process. The fundamentals of the mechanisms contributing to wet granule growth and the characteristics of fluid bed granulation processing are briefly discussed. This is followed by a detailed overview of the in-line applied process analyzers, contributing to improved fluid bed granulation understanding, modeling, control, and endpoint detection. Analysis and modeling tools enabling the extraction of the relevant information from the complex data collected during granulation and the control of the process are highlighted.

Particle sizing measurements in pharmaceutical applications: comparison of in-process methods versus off-line methods.

It has been previously described that when a samples particle size is determined using different sizing techniques, the results can differ considerably. The purpose of this study was to review several in-process techniques for particle size determination (Spatial Filtering Velocimetry, Focused Beam Reflectance Measurements, Photometric Stereo Imaging, and the Eyecon® technology) and compare them to well-known and widespread off-line reference methods (laser diffraction and sieve analysis). To start with, a theoretical explanation of the working mechanism behind each sizing technique is presented, and a comparison between them is established. Secondly, six batches of granules and pellets (i.e., spherical particles) having different sizes were measured using these techniques. The obtained size distributions and related D10, D50, and D90 values were compared using the laser diffraction wet dispersion method as reference technique. As expected, each technique provided different size distributions with different D values. These dissimilarities were examined and explained considering the measurement principles behind each sizing technique. The particle property measured by each particle size analyzer (particle size or chord length) and how it is measured as well as the way in which size information is derived and calculated from this measured property and how results are presented (e.g., volume or mass distributions) are essential for the interpretation of the particle size data.

Impact of screw configuration on the particle size distribution of granules produced by twin screw granulation.

Twin screw granulation (TSG) has been reported by different research groups as an attractive technology for continuous wet granulation. However, in contrast to fluidized bed granulation, granules produced via this technique typically have a wide and multimodal particle size distribution (PSD), resulting in suboptimal flow properties. The aim of the current study was to evaluate the impact of granulator screw configuration on the PSD of granules produced by TSG. Experiments were performed using a 25 mm co-rotating twin screw granulator, being part of the ConsiGma™-25 system (a fully continuous from-powder-to-tablet manufacturing line from GEA Pharma Systems). Besides the screw elements conventionally used for TSG (conveying and kneading elements), alternative designs of screw elements (tooth-mixing-elements (TME), screw mixing elements (SME) and cutters) were investigated using an α-lactose monohydrate formulation granulated with distilled water. Granulation with only conveying elements resulted in wide and multimodal PSD. Using kneading elements, the width of the PSD could be partially narrowed and the liquid distribution was more homogeneous. However, still a significant fraction of oversized agglomerates was obtained. Implementing additional kneading elements or cutters in the final section of the screw configuration was not beneficial. Furthermore, granulation with only TME or SME had limited impact on the width of the PSD. Promising results were obtained by combining kneading elements with SME, as for these configurations the PSD was narrower and shifted to the size fractions suitable for tableting.

Evaluation of in-line spatial filter velocimetry as PAT monitoring tool for particle growth during fluid bed granulation

In this study, the feasibility of spatial filter velocimetry (SFV) as process analytical technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distribution during processing, and the end product particle size distribution, tapped density and Hausner ratio was examined using a design of experiments (DOE) (2-level full factorial design, 19 experiments). The trend in end granule particle size distributions of all DOE batches measured with in-line SFV was similar to the off-line laser diffraction (LD) data. Analysis of the DOE results showed that mainly the HPMC concentration and slightly the inlet air temperature during drying had a positive effect on the average end granule size. The in-line SFV particle size data, obtained every 10s during processing, further allowed to explain and better understand the (in)significance of the studied DOE variables, which was not possible based on the LD data as this technique only supplied end granule size information. The variation in tapped density and Hausner ratio among the end granules of the different DOE batches could be explained by their difference in average end granule size. Univariate, multivariate PLS and multiway N-PLS models were built to relate these end granule properties to the in-line-measured particle size distribution. The multivariate PLS tapped density model and the multiway N-PLS Hausner ratio model showed the highest R(2) values in combination with the lowest RMSEE values (R(2) of 82% with an RMSEE of 0.0279 for tapped density and an R(2) of 52% with an RMSEE of 0.0268 for Hausner ratio, respectively).

Importance of using complementary process analyzers for the process monitoring, analysis, and understanding of freeze drying.

The aim of the present paper is to demonstrate the importance of using complementary process analyzers (PAT tools) for the process monitoring, analysis, and understanding of freeze drying. A mannitol solution was used as a model system. Raman spectroscopic, near-infrared (NIR) spectroscopic, plasma emission spectroscopic, and wireless temperature measurements (TEMPRIS) were simultaneously performed in-line and real-time during each freeze-drying experiment. The combination of these four process analyzers to monitor a freeze-drying process is unique. The Raman and NIR data were analyzed using principal component analysis (PCA) and multivariate curve resolution (MCR), while the plasma emission spectroscopic and wireless temperature measurement data were analyzed using univariate data analysis. It was shown that the considered process analyzers do not only complement but also mutually confirm each other with respect to process step end points, physical phenomena occurring during freeze drying (process understanding), and product characterization (solid state). Furthermore and most important, the combined use of the process analyzers helped to identify flaws in previous studies in which these process analyzers were studied individually. Process analyzers might wrongly indicate that some process steps are fulfilled. Finally, combining the studied process analyzers also showed that more information per process analyzer can be obtained than previously described. A combination of Raman and plasma emission spectroscopy seems favorable for the monitoring of nearly all critical freeze-drying process aspects.

Development of a fluid bed granulation process control strategy based on real-time process and product measurements

This article describes the results of three case studies conducted consecutively, in order to develop a process control strategy for a top-spray fluid bed granulation process. The use of several real-time particle size (i.e., spatial filter velocimetry and focused beam reflectance measurement) and moisture (i.e., near infrared (NIR) and Lighthouse near infrared spectroscopy) analyzers was examined. A feed-forward process control method was developed, where in-line collected granulation information during the process spraying phase was used to determine the optimum drying temperature of the consecutive drying phase. Via real-time monitoring of process (i.e., spraying temperature and spray rate) and product (i.e., granule size distribution and moisture) parameters during the spraying period, the batch bulk density was predicted at the end of the spraying cycle, using a PLS model. When this predicted bulk density was not meeting the desired value, the developed control method allowed the calculation of an adjusted drying temperature leading to the desired batch bulk density at the end of the granulation process. Besides the development of the feed-forward control strategy, a quantitative PLS model for in-line moisture content prediction of the granulated end product was built using the NIR data.

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements

Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model.