Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations. A pilot study
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Autor:innen
Bennett-Lenane, Harriett
O'Shea, Joseph
Murray, Jack
Ilie, Alexandra Roxana
Holm, Rene
Griffin, Brendan
Autor:in (Körperschaft)
Publikationsdatum
05.09.2021
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Pharmaceutics
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
13
Ausgabe / Nummer
9
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
MDPI
Verlagsort / Veranstaltungsort
Basel
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
In 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.
Schlagwörter
Lipid-based drug delivery, Computational pharmaceutics, Machine learning, Supersaturated lipid-based formulations
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1999-4923
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
Bennett-Lenane, H., O’Shea, J., Murray, J., Ilie, A. R., Holm, R., Kuentz, M., & Griffin, B. (2021). Artificial neural networks to predict the apparent degree of supersaturation in supersaturated lipid-based formulations. A pilot study. Pharmaceutics, 13(9). https://doi.org/10.3390/pharmaceutics13091398