Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

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dc.contributor.authorMurray, Jack D.
dc.contributor.authorLange, Justus J.
dc.contributor.authorBennett-Lenane, Harriet
dc.contributor.authorHolm, René
dc.contributor.authorKuentz, Martin
dc.contributor.authorO'Dwyer, Patrick J.
dc.contributor.authorGriffin, Brendan T.
dc.contributor.funderIrish Research Council
dc.contributor.funderHorizon 2020
dc.date.accessioned2023-09-29T13:02:57Z
dc.date.available2023-09-28T17:03:35Zen
dc.date.available2023-09-29T13:02:57Z
dc.date.issued2023-09-29
dc.date.updated2023-09-28T16:03:37Zen
dc.description.abstractArtificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.en
dc.description.sponsorshipIrish Research Council (Government of Ireland Postgraduate Scholarship Programme grant number GOIPG/2022/1580)
dc.description.statusPeer revieweden
dc.description.versionPublished Version
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid106562
dc.identifier.citationMurray, J. D., Lange, J. J., Bennett-Lenane, H., Holm, R., Kuentz, M., O'Dwyer, P. J. and Griffin, B. T. (2023) 'Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation', European Journal of Pharmaceutical Sciences, 191, 106562 (13pp). doi: 10.1016/j.ejps.2023.106562
dc.identifier.doi10.1016/j.ejps.2023.106562en
dc.identifier.endpage13
dc.identifier.issn0928-0987
dc.identifier.journaltitleEuropean Journal of Pharmaceutical Sciences
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/15055
dc.identifier.volume191
dc.language.isoenen
dc.publisherElsevier B.V.
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/InPharma
dc.rights© 2023, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectComputational pharmaceutics
dc.subjectDrug formulation
dc.subjectData-driven modelling
dc.subjectProperty prediction
dc.titleAdvancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation
dc.typeArticle (peer-reviewed)
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