Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations: A pilot study

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dc.contributor.authorBennett-Lenane, Harriet
dc.contributor.authorO'Shea, Joseph P.
dc.contributor.authorMurray, Jack D.
dc.contributor.authorIlie, Alexandra-Roxana
dc.contributor.authorHolm, René
dc.contributor.authorKuentz, Martin
dc.contributor.authorGriffin, Brendan T.
dc.contributor.funderIrish Research Councilen
dc.contributor.funderHorizon 2020en
dc.date.accessioned2021-10-06T14:32:02Z
dc.date.available2021-10-06T14:32:02Z
dc.date.issued2021-09-05
dc.date.updated2021-10-06T14:21:46Z
dc.description.abstractIn response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBFCapmulMC (r2 0.90 vs. 0.56) and sLBFMaisineLC (r2 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.en
dc.description.sponsorshipIrish Research Council (Post Graduate Scholarship Project Number: GOIPG/2018/883)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid1398en
dc.identifier.citationBennett-Lenane, H., O'Shea, J. P., Murray, J. D., Ilie, A.-R., Holm, R., Kuentz, M. and Griffin, B. T. (2021) 'Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations: A pilot study', Pharmaceutics, 13(9), 1398 (14pp). doi: 10.3390/pharmaceutics13091398en
dc.identifier.doi10.3390/pharmaceutics13091398en
dc.identifier.eissn1999-4923
dc.identifier.endpage14en
dc.identifier.issued9en
dc.identifier.journaltitlePharmaceuticsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/12060
dc.identifier.volume13en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::MSCA-ITN-ETN/674909/EU/Pharmaceutical Education And Research with Regulatory Links: Innovative drug development strategies and regulatory tools tailored to facilitate earlier access to medicines/PEARRLen
dc.rights© 2021, the Authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectLipid-based drug deliveryen
dc.subjectComputational pharmaceuticsen
dc.subjectMachine learningen
dc.subjectSupersaturated lipid-based formulationsen
dc.titleArtificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations: A pilot studyen
dc.typeArticle (peer-reviewed)en
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