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.

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).

Augmentation of near infrared diffuse reflectance and transmittance spectral data for the development of robust PLSBC models for classifying double blind clinical trial tablets.

The water content of clinical trial tablets can be different between and within different tablet batches, depending on the relative humidity conditions during their production, packaging, storage and analysis. These water variations lead to important spectral variations in the near infrared spectral region which can lead to a wrong identification if the classification model was based on unrepresentative data towards the water content. As model development for clinical trial studies needs to be extremely fast - within one working day - with generally only one batch available, the principle of data augmentation has to be applied to render more robust classification models. Therefore, tablets available for constructing the model are being processed in order to increase or decrease their water content and to make them more representative for tablets to be tested in the future. The inclusion of a deliberate water variation is the most efficient way to develop a model, for which no additional model redevelopment will be required to pass the system suitability tests and to obtain a correct identification.

Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets.

Near infrared transmission spectroscopy combined with chemometrical methods can be applied for identity confirmation of double-blind clinical trial tablets. Samples of two clinical studies, investigating the dose and placebo effect of an experimental drug, were studied. The identity of the blistered tablets was checked using partial least squares beta classification (PLSBC) applied to their NIR transmission spectra. PLSBC is a new supervised classification approach based on partial least squares (PLS) regression combined with beta-error driven class boundaries. It has the ability to limit the probability for misclassification to a known number and therefore providing the method developer a tool for deciding whether the NIR spectra of the different strengths of tablets are specific enough to obtain a robust classification model. The presented approach has the advantage to be applicable on most commercial available near infrared spectroscopy (NIRS) instrumentation software and it can be applied in a GMP environment since validation according to the ICH Q2A and Q2B guidelines on analytical method validation is fast and relatively easy.