Predictive computational models for assessing the impact of co-milling on drug dissolution

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dc.contributor.authorPätzmann, Nicolasen
dc.contributor.authorO'Dwyer, Patrick J.en
dc.contributor.authorBeránek, Josefen
dc.contributor.authorKuentz, Martinen
dc.contributor.authorGriffin, Brendan T.en
dc.contributor.funderHORIZON EUROPE Marie Sklodowska-Curie Actionsen
dc.contributor.funderHorizon 2020en
dc.date.accessioned2024-06-11T15:20:44Z
dc.date.available2024-06-11T15:20:44Z
dc.date.issued2024-04-30en
dc.description.abstractCo-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.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid106780en
dc.identifier.citationPätzmann, N., O’Dwyer, P.J., Beránek, J., Kuentz, M. and Griffin, B.T. (2024) ‘Predictive computational models for assessing the impact of co-milling on drug dissolution’, European Journal of Pharmaceutical Sciences, 198,106780. Available at: https://doi.org/10.1016/j.ejps.2024.106780en
dc.identifier.doi10.1016/j.ejps.2024.106780en
dc.identifier.endpage12en
dc.identifier.issn0928-0987en
dc.identifier.journaltitleEuropean Journal of Pharmaceutical Sciencesen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15996
dc.identifier.volume198en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofEuropean Journal of Pharmaceutical Sciencesen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::MSCA-ITN-EID/955756/EU/A fully integrated, animal-free, end-to-end modelling approach to oral drug product development/InPharmaen
dc.rights© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectCo-millingen
dc.subjectCo-grindingen
dc.subjectBall millingen
dc.subjectDissolution rate enhancementen
dc.subjectIn silico modellingen
dc.subjectPartial least squares regressionen
dc.subjectMultiple linear regressionen
dc.titlePredictive computational models for assessing the impact of co-milling on drug dissolutionen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume198en
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