Predicting mini-tablet dissolution performance utilizing X-ray computed tomography

Borjigin, Tohn; Zhan, Xi; Li, Jiangwei; Meda, Alvin; Tran, Kenny

doi:10.1016/j.ejps.2022.106346

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The particles in contact with the fluid starts to dissolve. At the same time the fluid enters the tablet through the pores and its network resulting in the disintegration of the tablet into smaller pieces and this stage is effectively controlled by the concentration of pores [ 242 , 243 , 244 , 245 , 246 , 293 , 345 , 346 , 347 ]. By extracting the information of the pore network and its permeability from a SEM image, Zhang et al [ 348 ] has shown that one can estimate the drug release rate by computational fluid dynamic simulation.…”

Section: Tablet Characterizationmentioning

confidence: 99%

Characterization of Pharmaceutical Tablets by X-ray Tomography

et al. 2023

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Solid dosage forms such as tablets are extensively used in drug administration for their simplicity and large-scale manufacturing capabilities. High-resolution X-ray tomography is one of the most valuable non-destructive techniques to investigate the internal structure of the tablets for drug product development as well as for a cost effective production process. In this work, we review the recent developments in high-resolution X-ray microtomography and its application towards different tablet characterizations. The increased availability of powerful laboratory instrumentation, as well as the advent of high brilliance and coherent 3rd generation synchrotron light sources, combined with advanced data processing techniques, are driving the application of X-ray microtomography forward as an indispensable tool in the pharmaceutical industry.

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Section: Tablet Characterizationmentioning

confidence: 99%

Characterization of Pharmaceutical Tablets by X-ray Tomography

et al. 2023

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“…Examples include the automated high-throughput extraction of morphological traits from μCT images of rice plants [ 68 ], geological sample classification [ 69 ], the classification of urinary stones from medical μCT scans [ 70 ], permeability estimation of complex pore structures of carbonate rock [ 71 ], and the quality enhancement of low-dose μCT scans [ 72 ]. In pharmaceutical research, CNNs have been applied to predict mini-tablet dissolution performance based on film coating integrity analysis using convolutional neural networks [ 73 ].…”

Section: Introductionmentioning

confidence: 99%