Category: Formulation and Quality
Purpose: The purpose of this research was to develop a robust analysis process that can provide a quantitative measure of crystalline content in tablets formulated as amorphous solid dispersions using X-ray Microscopy (XRM).
Methods: A model amorphous solid dispersion tablet system was created composed of amorphous fenofibrate in copovidone using hot melt extrusion. The tablets had known levels of crystalline fenofibrate spiked at six different levels from 0% to 3% of total tablet weight. The tablets were analyzed with a Rigaku Nano3DX XRM using optimized instrument parameters. Representative crystalline features from top, bottom, periphery and core of the scan volume were used to determine threshold values for segmentation. Final quantification of the reconstructed tomographic data was performed using ScanIP image processing software. A data analysis process was developed to extract quantitative information from the XRM data set. The analysis included a slight Gaussian 3D filtering (Sigma=1) to suppress most of the noise, a process to select regions of interest for crystal thresholding and separate the crystalline features for load calculations. The system robustness was checked by varying different data analysis operators and varying crystalline ROIs within a tablet.
Results: Crystalline API, cracks and voids are plainly visible in representative images of the XRM data (Figure 1). The primary challenge for quantitation is to remove erroneous phase boundaries for proper segmentation. Proper signal smoothing does reduce signal noise and potential artifacts. However, this filtering runs the risk of removing small crystals and underestimating total crystalline content.
The data analysis process to extract quantitative measurement is diagramed in Figure 2. The thresholding process is a critical step in extracting quantitative data. Measures of robustness for the thresholding process are shown in Figure 3 where the range of measurements for four operators are compared and the repeatability of the analysis recipe is compared by using the same ROI Selection on multiple scans of the same location. The analysis process is robust in terms of operators and choices of threshold values based on selection of ROIs.
Conclusion: We conclude that XRM analysis of crystals in amorphous pharmaceutical systems provides significant benefits to standard crystal detection systems. The technique can easily detect and quantify crystalline material in an amorphous system. XRM is non-destructive so it can allow for additional collaborative studies including dissolution or potentially bio studies. The data is three dimensional which can help determine if crystals are the result of residual crystallinity or shed light on the mechanism of crystal formation.
XRM analysis does have its challenges. Extracting quantitative measures of crystalline content in amorphous solid dispersions of fenofibrate poses several challenges. Primary to these challenges is the low contrast between the crystalline API and the rest of the matrix, resulting in overall low signal to noise. Optimization of data collection procedures and data analysis to reduce noise can improve the overall data analysis process. This should ultimately lead to a process that can be automated in a data analysis program and a robust method for tablet analysis. However, the human factor for threshold selection is still the largest source of error.
Based on the current protocol this process can be confirmed on other API systems. Ultimately this should be applicable to quantify residual crystallinity from manufactured systems or induced crystallinity from stressed systems.