Characterization of the 3D microstructure of Ibuprofen tablets by means of synchrotron tomography

Abstract

A new methodology to segment the three-dimensional (3D) internal structure of Ibuprofen tablets from synchrotron tomography is presented, introducing a physically coherent trinarization for greyscale images of Ibuprofen tablets consisting of three phases: microcrystalline cellulose, Ibuprofen and pores. For this purpose, a hybrid approach is developed combining a trinarization by means of statistical learning with a trinarization based on a watershed algorithm. This hybrid approach allows us to compute microstructure characteristics of tablets using methods of statistical image analysis. A comparison with experimental results shows that there is a significant amount of pores which is below the resolution limit. At the same time, results from image analysis let us conjecture that these pores constitute the great majority of the surface between pores and solid. Furthermore, we compute microstructure characteristics, which are experimentally not accessible such as local percolation probabilities and chord length distribution functions. Both characteristics are meaningful in order to quantify the influence of tablet compaction on its microstructure. The presented approach can be used to get better insight into the relationship between production parameters and microstructure characteristics based on 3D image data of Ibuprofen tablets manufactured under different conditions and elucidate key effects on the strength and solubility kinetics of the final formulation. LAY DESCRIPTION: A typical formulation of uniaxial compacted Ibuprofen tablets consist of a mixture of an excipient (microcrystalline cellulose) with an active ingredient (a ground fraction of Ibuprofen). The final mechanical strength of the tablet as well as the release kinetics are strongly influenced by the underlying microstructure, i.e. the spatial arrangement of the microcrystalline cellulose and Ibuprofen within the tablet. In order to optimize the performance of the tablet, it is important to investigate the relationship between its microstructure and the corresponding production parameters. For this purpose, 3D imaging is a powerful tool as it allows computing microstructural properties such as the internal arrangement, interconnectivity and pore location and distribution, characteristics that cannot be computed by experimental characterization techniques. In the present study, a new algorithm for an accurate trinarization of 3D image data obtained by synchrotron tomography is presented. Trinarization means that we reconstruct microcrystalline cellulose, Ibuprofen and pores on the basis of the 3D images, where one can only observe different greyscale values, but not the different constituents themselves. For this purpose, a hybrid approach combining a trinarization by means of artificial intelligence with a trinarization based on a geometrically motivated algorithm is developed. This hybrid approach allows to compute microstructure characteristics of tablets using image analysis. A comparison with experimental results shows that there is a significant amount of pores below the resolution limit. At the same time results from image analysis lead to the conjecture that these pores constitute the major part of the surface between pores and solid. Moreover, characteristics are computed by image analysis, which are meaningful in order to quantify the influence of tablet compaction parameters on its microstructure. The presented novel approach can be used to elucidate the relationship between production parameters and microstructure characteristics based on 3D image data of Ibuprofen tablets manufactured under different mixing, loading and processing conditions.

Keywords: 3D imaging; Ibuprofen tablet; image trinarization; microstructure characterization; statistical learning; watershed algorithm.