Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based prediction model
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
15.09.2025
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
International Journal of Pharmaceutics
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
682
Ausgabe / Nummer
Seiten / Dauer
125918
Patentnummer
Verlag / Herausgebende Institution
Elsevier
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Mesoporous silica has emerged as a promising component in bio-enabling formulation strategy. However, there is currently a lack of predictive tools for assessing drug-silica interactions in a preformulation phase, when formulators only have minimal material to guide them. This study proposes a solution: a chromatographic method to rank apparent drug-silica affinity for mesoporous formulations. Using a dataset of 52 drugs, a hydrophilic liquid interaction chromatography (HILIC) screening method was developed, with a stationary silica phase to simulate the drug carrier. Molecular descriptors were calculated for various compounds to analyze HILIC retention times using a tree-based machine learning algorithm. For silica affinity, the distribution coefficient (LogD), the molecular shape descriptor Kappa1, and the number of conjugated bonds (NCB) were identified as possible critical parameters. Additionally, an amine-modified HILIC column was evaluated to simulate a surface-modified silica carrier. The classification tree analysis revealed that Abraham's hydrogen bonding acidity, the NCB and the pKa were determinants for a qualitative assessment of drug affinity to the modified silica. The classification into low, moderate, and high affinity to the stationary phase appeared to be useful in understanding drug release from mesoporous silica formulations, highlighting its potential for future research.
Schlagwörter
Apparent drug-silica interaction, Chromatographic screening, Classification tree model, Hydrophilic interaction liquid chromatography, Machine learning
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1873-3476
0378-5173
0378-5173
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Hybrid
Lizenz
Zitation
Niederquell, A., Vraníková, B., & Kuentz, M. (2025). Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based prediction model. International Journal of Pharmaceutics, 682, 125918. https://doi.org/10.1016/j.ijpharm.2025.125918