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

Highlights

• We report the combination of image analysis and Raman spectroscopy for exploring the solid form diversity of powder samples.

• This approach is based on the measurement at an individual particle level of high number of particles,

• The efficient data analysis based on multivariate methods enables visualization of the classification of a high number of particles,

• Method with potential for exploring low amounts of solid form impurities in fine chemicals, especially with particulate pharmaceuticals.

Abstract

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.

Introduction

Polymorphism of drug compounds involves the occurrence of different types of packing of the same molecule in a crystal lattice [1]. The expression “solid form” can be used as a broader term to describe not only crystalline single component systems, but also amorphous matter and binary systems, such as salts, solvates, cocrystalline and coamorphous systems [2]. Different solid forms of a drug compound can be critical for the health outcome of a patient because they may affect product performance, especially when exhibiting a different particle size, shape, solubility, dissolution rate and bioavailability [3,4]. These critical material attributes can thus affect product quality, safety and efficacy [5]. Unexpected solid form changes, such as metastable polymorphs [6], elusive crystal forms [7] and unintentional seeding caused by very low amounts of an unwanted polymorph [8], have had negative effects on the availability of otherwise affordable drugs. An example of a drug product with a detrimental uncontrolled solid form change was Norvir® (ritonavir), leaving AIDS patients temporarily without a treatment [8]. To ensure that the polymorphism landscape is properly explored by the industry, the regulatory authorities have issued guidance documents on solid form characterization and control [9]. For reasons such as the ones mentioned above, the solid form diversity of particulate matter, both in solid, semi-solid and liquid products, is of particular interest to the pharmaceutical industry when aiming for a more detailed product and process understanding. Solid form screening has become an industrial practice to cope with these challenges and in this context, different methods for generating the maximal number of new forms as well as high throughput analytical methods for quantification and detection have been developed [10].

Solid form diversity can be also a critical part of the patent portfolio of a given drug compound and its respective products. A number of litigation cases involving solid form issues have affected patent validity. For instance, the presence of forms I and II of ranitidine hydrochloride in anti-ulcer drug products led to litigation between Glaxo and Novopharm [8]. In many cases, such as in a case between Calgene and Dr Reddy's around the anti-cancer drug product Revlimid (lenalidomide), the key question is related to low amounts of a polymorphic impurity in the drug product [11]. It should be pointed out that many of these litigation cases have initiated intensive analytics in searching for very low amounts of a given solid form [8].

Conventional approaches to detect and quantify low levels of solid form impurities are based on the analysis of bulk materials [12]. Here we report an analytical technique at an individual particle level for the detection and quantification of solid form diversity. Crystals tend to grow in a specific crystallographic direction [13] and different polymorphs and crystallization/processing conditions typically result in different crystal morphologies [14]. Quantitative assessment of particle morphology has evolved with technological advancements in instrumentation and computing power. Methods based on automated particle tracking, bright/dark-field imaging and image analysis (IA) are capable of particle size and shape analysis of even hundreds of thousands of particles within a reasonable timeframe [15]. Particle morphology could therefore be used for solid form assessment, potentially resulting in a fast and sensitive analytical technique.

It has been estimated that about 90 % of studies related to polymorphism use at least two solid-state analytical techniques [16]. The use of a combination of characterization techniques enables scientific insight into the complexity of these phenomena with a higher accuracy [17]. Our study evaluates Raman spectroscopy, IA and the combination of Raman spectroscopy and IA at a single particle level in order to detect and quantify a low amount of a solid form impurity of a crystalline drug material. Partial least squares-discriminant analysis (PLS-DA) is a well-established data analytical method that combines dimensionality reduction and high prediction capability. PLS-DA is commonly used for variable selection as well as predictive and descriptive classification modeling. The PLS-DA algorithm is applicable for analyzing high dimensional data and does not assume the data to fit any distribution, making it suitable for imbalanced data with a high number of variables (n>1000) [18]. By using crystalline carbamazepine (CBZ) anhydrate (AH) and dihydrate (DH) as model solid forms, we aim to quantify solid forms in these binary mixtures, ultimately even at a single particle level. Quantification based on IA, Raman spectroscopy or a combination thereof is compared and a strategy is proposed for detection of low amounts of an unwanted solid form.