Applying computational predictions of biorelevant solubility ratio upon self-emulsifying lipid-based formulations dispersion to predict dose number

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dc.check.date2021-11-02
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorBennett-Lenane, Harriet
dc.contributor.authorKoehl, Niklas J.
dc.contributor.authorO'Dwyer, Patrick J.
dc.contributor.authorBox, Karl J.
dc.contributor.authorO'Shea, Joseph P.
dc.contributor.authorGriffin, Brendan T.
dc.contributor.funderIrish Research Councilen
dc.date.accessioned2020-12-04T12:18:02Z
dc.date.available2020-12-04T12:18:02Z
dc.date.issued2020-11-02
dc.date.updated2020-12-04T12:10:25Z
dc.description.abstractComputational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SRMC (r2 = 0.81) and SRLC (r2 = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SRMC r2 = 0.74; SRLC r2 = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (FArom_B). Through using the equations to inform drug developability classifications (DCS) for drugs that have already been licensed as lipid based formulations, merits for development with SEDDS was predicted for 2/3 drugs.en
dc.description.sponsorshipIrish Research Council (GOIPG/2018/883)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBennett-Lenane, H., Koehl, N. J., O'Dwyer, P. J., Box, K. J., O'Shea, J. P. and Griffin, B. T. (2020) 'Applying computational predictions of biorelevant solubility ratio upon self-emulsifying lipid-based formulations dispersion to predict dose number', Journal of Pharmaceutical Sciences. doi: 10.1016/j.xphs.2020.10.055en
dc.identifier.doi10.1016/j.xphs.2020.10.055en
dc.identifier.eissn1520-6017
dc.identifier.issn0022-3549
dc.identifier.journaltitleJournal of Pharmaceutical Sciencesen
dc.identifier.urihttps://hdl.handle.net/10468/10816
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.rights© 2020, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectLipid-based formulationsen
dc.subjectDevelopability screeningen
dc.subjectDrug delivery system(s)en
dc.subjectIn silico modellingen
dc.subjectMultivariate analysisen
dc.subjectPartial least squaresen
dc.subjectPhysiochemical propertiesen
dc.titleApplying computational predictions of biorelevant solubility ratio upon self-emulsifying lipid-based formulations dispersion to predict dose numberen
dc.typeArticle (peer-reviewed)en
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