Academic Journal
Predictive selection rule of favourable image processing methods for X-ray micro-computed tomography images of tablets.
| Title: | Predictive selection rule of favourable image processing methods for X-ray micro-computed tomography images of tablets. |
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| Authors: | Bollmann S; Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstrasse 1, Duesseldorf 40225, Germany., Kleinebudde P; Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstrasse 1, Duesseldorf 40225, Germany. Electronic address: kleinebudde@hhu.de. |
| Source: | International journal of pharmaceutics [Int J Pharm] 2021 Dec 15; Vol. 610, pp. 121207. Date of Electronic Publication: 2021 Oct 29. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7804127 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3476 (Electronic) Linking ISSN: 03785173 NLM ISO Abbreviation: Int J Pharm Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| MeSH Terms: | Excipients* , Image Processing, Computer-Assisted*, Algorithms ; Drug Compounding ; Tablets ; X-Ray Microtomography |
| Abstract: | Adequately processed X-ray micro-computed tomography images of real pharmaceutical formulations provide the possibility to validate in silico tools for the prediction of disintegration and/or dissolution. However, the evaluation of suitable image processing pathways is time consuming. The objective of this study was to prove the transferability of image processing methods and to develop an approach to select probable favourable image processing approaches for data sets with similar properties to accelerate the evaluation process. Therefore, data from a previously performed evaluation of image processing approaches and parameters were used to analyse the robustness of the image processing by statistical resampling and to develop a predictive rule set. The rule set was verified by both one new ratio of API and excipient within and outside of the ratios used to develop the rule. The rule was applied to images of a binary mixture with new compounds with similar determined image properties to prove the transferability of the rule set. It was possible to identify robust image processing pathways with narrow ranges of input parameters. The prediction of the image processing pathways led to high desirabilities which were confirmed by visual verification for ratios within the calibrated range. The transfer to the new binary mixture was successful and confirmed as well. (Copyright © 2021 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Image processing; Image processing rules; In silico development; X-ray micro-computed tomography; XμCT |
| Substance Nomenclature: | 0 (Excipients) 0 (Tablets) |
| Entry Date(s): | Date Created: 20211108 Date Completed: 20211208 Latest Revision: 20211214 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.ijpharm.2021.121207 |
| PMID: | 34743999 |
| Database: | MEDLINE |
| ISSN: | 1873-3476 |
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| DOI: | 10.1016/j.ijpharm.2021.121207 |