Advancing algorithmic drug product development. Recommendations for machine learning approaches in drug formulation
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Autor:innen
Murray, Jack D.
Lange, Justus J.
Bennett-Lenane, Harriet
Holm, René
O'Dwyer, Patrick J.
Griffin, Brendan T.
Autor:in (Körperschaft)
Publikationsdatum
2023
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
European Journal of Pharmaceutical Sciences
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
191
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Elsevier
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Artificial 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. © 2023
Schlagwörter
Artificial intelligence, Computational pharmaceutics, Data-driven modelling, Drug formulation, Property prediction
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
0928-0987
1879-0720
1879-0720
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
MURRAY, Jack D., Justus J. LANGE, Harriet BENNETT-LENANE, René HOLM, Martin KUENTZ, Patrick J. O’DWYER und Brendan T. GRIFFIN, 2023. Advancing algorithmic drug product development. Recommendations for machine learning approaches in drug formulation. European Journal of Pharmaceutical Sciences. 2023. Bd. 191. DOI 10.1016/j.ejps.2023.106562. Verfügbar unter: https://doi.org/10.26041/fhnw-7895