A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination.

Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf life claims and expediting the path to clinical implementation. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, flexible error modeling and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or of limited quality. This is illustrated by modeling accelerated stability data from two solid dosage forms and is further examined by means of artificial data subsets and simulated data.

NIR spectroscopic method for the in-line moisture assessment during drying in a six-segmented fluid bed dryer of a continuous tablet production line: Validation of quantifying abilities and uncertainty assessment.

This study focuses on the thorough validation of an in-line NIR based moisture quantification method in the six-segmented fluid bed dryer of a continuous from-powder-to-tablet manufacturing line (ConsiGma™ 25, GEA Pharma Systems nv, Wommelgem, Belgium). The moisture assessment ability of an FT-NIR spectrometer (Matrix™-F Duplex, Bruker Optics Ltd, UK) equipped with a fiber-optic Lighthouse Probe™ (LHP, GEA Pharma Systems nv, Wommelgem, Belgium) was investigated. Although NIR spectroscopy is a widely used technique for in-process moisture determination, a minority of NIR spectroscopy methods is thoroughly validated. A moisture quantification PLS model was developed. Twenty calibration experiments were conducted, during which spectra were collected at-line and then regressed versus the corresponding residual moisture values obtained via Karl Fischer measurements. The developed NIR moisture quantification model was then validated by calculating the accuracy profiles on the basis of the analysis results of independent in-line validation experiments. Furthermore, as the aim of the NIR method is to replace the destructive, time-consuming Karl Fischer titration, it was statistically demonstrated that the new NIR method performs at least as good as the Karl Fischer reference method.

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

It has been previously described that when a sample's 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.

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.

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.

Prediction of quality attributes of continuously produced granules using complementary pat tools.

Manufacturers of pharmaceutical solid dosage forms aim for a reduced production time and a shorter "time-to-market." Therefore, continuous manufacturing gains increasing interest in the pharmaceutical industry. For continuous manufacturing, the quality of produced pharmaceuticals should be assessed in real-time (in-line, on-line, and at-line) and not via the traditional off-line, often destructive and time-consuming analysis methods that supply the desired information only hours after sampling. This research paper evaluates three Process Analytical Technology (PAT) tools for the real-time at-line analysis of granules, which were produced using a continuous wet twin-screw granulator being part of a from powder-to-tablet production line (ConsiGma™-25). A Raman and NIR spectrometer were used together with a photometric imaging technique in order to acquire solid-state information and granule size data. These multivariate data were then used to predict the granules' moisture content, tapped and bulk density, and flowability. The three PAT tools provided complementary information for predicting these quality attributes of the continuously produced granules. The residual moisture content was mostly correlated with the spectroscopic data, whereas the imaging data had the highest predictive capability for the flowability of the granules.