Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level.
Image analysis
Modelling
Multivariate data analysis
Partial least squares - discriminant analysis
Polymorphism
Raman spectroscopy
Journal
Journal of pharmaceutical and biomedical analysis
ISSN: 1873-264X
Titre abrégé: J Pharm Biomed Anal
Pays: England
ID NLM: 8309336
Informations de publication
Date de publication:
30 Jan 2021
30 Jan 2021
Historique:
received:
25
08
2020
revised:
26
10
2020
accepted:
27
10
2020
pubmed:
21
11
2020
medline:
22
6
2021
entrez:
20
11
2020
Statut:
ppublish
Résumé
Solid form diversity of raw materials can be critical for the performance of the final drug product. In this study, Raman spectroscopy, image analysis and combined Raman and image analysis were utilized to characterize the solid form composition of a particulate raw material. Raman spectroscopy provides chemical information and is complementary to the physical information provided by image analysis. To demonstrate this approach, binary mixtures of two solid forms of carbamazepine with a distinct shape, an anhydrate (prism shaped) and a dihydrate (needle shaped), were characterized at an individual particle level. Partial least squares discriminant analysis classification models were developed and tested with known, gravimetrically mixed test samples, followed by analysis of unknown, commercially supplied carbamazepine raw material samples. Classification of several thousands of particles was performed, and it was observed that with the known binary mixtures, the minimum number of particles needed for the combined Raman spectroscopy - image analysis classification model was approximately 100 particles per solid form. The carbamazepine anhydrate and dihydrate particles were detected and classified with a classification error of 1 % using the combined model. Further, this approach allowed the identification of raw material solid form impurity in unknown raw material samples. Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level has its potential in accurate detection of low amounts of unwanted solid forms in particulate raw material samples.
Identifiants
pubmed: 33217710
pii: S0731-7085(20)31630-7
doi: 10.1016/j.jpba.2020.113744
pii:
doi:
Substances chimiques
Powders
0
Carbamazepine
33CM23913M
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
113744Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Dr Reddy’s IPDO Leiden (Leiden, Netherlands) has financed the PhD project of Andrea Sekulovic. Andrea Sekulovic and Ruud Verrijk are employed at Dr Reddy’s. Jukka Rantanen and Thomas Rades have not received any consulting fees for this work.