Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations

Simple item page
dc.contributor.authorEjskjær, Lotteen
dc.contributor.authorHolm, Renéen
dc.contributor.authorKuentz, Martinen
dc.contributor.authorBox, Karl J.en
dc.contributor.authorGriffin, Brendan T.en
dc.contributor.authorO'Dwyer, Patrick J.en
dc.contributor.funderHorizon 2020en
dc.date.accessioned2025-02-11T12:39:12Z
dc.date.available2025-02-11T12:39:12Z
dc.date.issued2024-06-21en
dc.description.abstractComputational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid106833en
dc.identifier.citationEjskjær, L., Holm, R., Kuentz, M., Box, K. J., Griffin, B. T. and O'Dwyer, P. J. (2024) 'Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations', European Journal of Pharmaceutical Sciences, 200, 106833 (9pp). https://doi.org/10.1016/j.ejps.2024.106833en
dc.identifier.doihttps://doi.org/10.1016/j.ejps.2024.106833en
dc.identifier.endpage9en
dc.identifier.issn0928-0987en
dc.identifier.journaltitleEuropean Journal of Pharmaceutical Sciencesen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/17019
dc.identifier.volume200en
dc.language.isoenen
dc.publisherElsevier Ltd.en
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.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectComputational pharmaceuticsen
dc.subjectDevelopability screeningen
dc.subjectIn silico modellingen
dc.subjectMultivariate analysisen
dc.subjectPartial least squaresen
dc.subjectPhysicochemical propertiesen
dc.subjectLipid-based-formulationen
dc.titlePredictions of biorelevant solubility change during dispersion and digestion of lipid-based formulationsen
dc.typeArticle (peer-reviewed)en
oaire.citation.volume200en
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
1-s2.0-S0928098724001453-main.pdf
Size:
1.1 MB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
Thumbnail Image
Name:
ScienceDirect_files_11Feb2025_12-37-53.287.zip
Size:
184.51 KB
Format:
http://www.iana.org/assignments/media-types/application/zip
Description:
Supplementary materials
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Pharmacy - Journal Articles