In Situ Monitoring of Drug Precipitation from Digesting Lipid Formulations Using Low-Frequency Raman Scattering Spectroscopy

Malinda Salim, Sara J. Fraser-Miller, Kārlis Bērziņš, Joshua J. Sutton, Keith C. Gordon, Ben J. Boyd*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

4 Citationer (Scopus)
38 Downloads (Pure)

Abstract

Low-frequency Raman spectroscopy (LFRS) is a valuable tool to detect the solid state of amorphous and crystalline drugs in solid dosage forms and the transformation of drugs between different polymorphic forms. It has also been applied to track the solubilisation of solid drugs as suspensions in milk and infant formula during in vitro digestion. This study reports the use of LFRS as an approach to probe drug precipitation from a lipid-based drug delivery system (medium-chain self-nanoemulsifying drug delivery system, MC-SNEDDS) during in vitro digestion. Upon lipolysis of the digestible components in MC-SNEDDS containing fenofibrate as a model drug, sharp phonon peaks appeared at the low-frequency Raman spectral region (<200 cm−1), indicating the precipitation of fenofibrate in a crystalline form from the formulation. Two multivariate data analysis approaches (principal component analysis and partial least squares discriminant analysis) and one univariate analysis approach (band ratios) were explored to track these spectral changes over time. The low-frequency Raman data produces results in good agreement with in situ small angle X-ray scattering (SAXS) measurements with all data analysis approaches used, whereas the mid-frequency Raman requires the use of PLS-DA to gain similar results. This suggests that LFRS can be used as a complementary, and potentially more accessible, technique to SAXS to determine the kinetics of drug precipitation from lipid-based formulations.

OriginalsprogEngelsk
Artikelnummer1968
TidsskriftPharmaceutics
Vol/bind15
Udgave nummer7
Antal sider13
ISSN1999-4923
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was funded by the Bill and Melinda Gates Foundation (OPP1160404). Funding is also acknowledged by ARC Linkage (project ID LP180101147).

Publisher Copyright:
© 2023 by the authors.

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