Predictive computational models for assessing the impact of co-milling on drug dissolution
Author (Corporation)
Publication date
07/2024
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
European Journal of Pharmaceutical Sciences
Special issue
DOI of the original publication
Link
Series
Series number
Volume
198
Issue / Number
Pages / Duration
106780
Patent number
Publisher / Publishing institution
Elsevier
Place of publication / Event location
Edition
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Abstract
Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.
Keywords
Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
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ISBN
ISSN
0928-0987
1879-0720
1879-0720
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
PÄTZMANN, Nicolas, Patrick J. O’DWYER, Josef BERÁNEK, Martin KUENTZ und Brendan T. GRIFFIN, 2024. Predictive computational models for assessing the impact of co-milling on drug dissolution. European Journal of Pharmaceutical Sciences. Juli 2024. Bd. 198, S. 106780. DOI 10.1016/j.ejps.2024.106780. Verfügbar unter: https://doi.org/10.26041/fhnw-11904