TY - JOUR

T1 - Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level

AU - Sekulovic, Andrea

AU - Verrijk, Ruud

AU - Rades, Thomas

AU - Grabarek, Adam

AU - Jiskoot, Wim

AU - Hawe, Andrea

AU - Rantanen, Jukka

PY - 2021

Y1 - 2021

N2 - 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. (C) 2020 Published by Elsevier B.V.

AB - 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. (C) 2020 Published by Elsevier B.V.

KW - Polymorphism

KW - Raman spectroscopy

KW - Image analysis

KW - Multivariate data analysis

KW - Partial least squares - discriminant analysis

KW - Modelling

KW - CRYSTAL HABIT

KW - CARBAMAZEPINE

KW - POLYMORPHS

KW - STABILITY

KW - IMPACT

KW - FORMS

U2 - 10.1016/j.jpba.2020.113744

DO - 10.1016/j.jpba.2020.113744

M3 - Journal article

C2 - 33217710

VL - 193

JO - Journal of Pharmaceutical and Biomedical Analysis

JF - Journal of Pharmaceutical and Biomedical Analysis

SN - 0731-7085

M1 - 113744

ER -