Machine learning methods for prediction of food effects on bioavailability: A comparison of Support Vector Machines and Artificial Neural Networks

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dc.contributor.authorBennett-Lenane, Harriet
dc.contributor.authorGriffin, Brendan T.
dc.contributor.authorO'Shea, Joseph P.
dc.contributor.funderIrish Research Councilen
dc.date.accessioned2021-10-06T14:08:06Z
dc.date.available2021-10-06T14:08:06Z
dc.date.issued2021-09-24
dc.date.updated2021-10-06T13:58:25Z
dc.description.abstractDespite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.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.citationBennett-Lenane, H., Griffin, B. T. and O'Shea, J. P. (2021) 'Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks', European Journal of Pharmaceutical Sciences. doi: 10.1016/j.ejps.2021.106018en
dc.identifier.doi10.1016/j.ejps.2021.106018en
dc.identifier.issn1879-0720
dc.identifier.issn0928-0987
dc.identifier.journaltitleEuropean Journal of Pharmaceutical Sciencesen
dc.identifier.urihttps://hdl.handle.net/10468/12059
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.rights© 2021, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( https://creativecommons.org/licenses/by/4.0/ )en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectOral drug deliveryen
dc.subjectBio-enabling formulationsen
dc.subjectFood effecten
dc.subjectMachine learningen
dc.titleMachine learning methods for prediction of food effects on bioavailability: A comparison of Support Vector Machines and Artificial Neural Networksen
dc.typeArticle (peer-reviewed)en
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