Detection of low dose of piroxicam polymorph in pharmaceutical tablets by surface-enhanced Raman chemical imaging (SER-CI) and multivariate analysis.

This study demonstrates, for the first time, the ability of surface-enhanced Raman chemical imaging (SER-CI) combined with multivariate analysis to detect low levels (0.1% (w/w)) of a polymorphic form in a pharmaceutical mixture. In the studied formulation, piroxicam was used as a model molecule to develop this approach. Piroxicam is a widely available non-steroidal anti-inflammatory drug, exhibiting an interesting case of polymorphism, with two most commonly observed forms (ß and α2). In this work, the SERS spectra of piroxicam polymorphic forms ß and α2 are presented. These forms showed clear spectral differences in terms of band position and intensity. From a crystallographic point of view, the difference of exaltation between both forms was correlated with a preferred orientation of crystallites of form α2 making its SERS detection difficult compared to form ß. A preferred orientation of the (1k0) crystallographic planes of α2 was demonstrated in samples, not promoting an appropriate molecular orientation onto the metallic surface. Additionally, a semi-quantitative approach using SER-CI combined with chemometric tools was developed enabling to detect crystallites of form ß below the detection limit of conventional Raman microscopy. The exaltation of the Raman signal in the presence of silver nanoparticles allowed a higher sensitivity and a reduction of the acquisition time by a factor of 6.

Towards a spray-coating method for the detection of low-dose compounds in pharmaceutical tablets using surface-enhanced Raman chemical imaging (SER-CI).

Surface-enhanced Raman chemical imaging (SER-CI) is a highly sensitive analytical tool recently used in the pharmaceutical field owing to the possibility to obtain high sensitivity along with spatial information. However, the covering method of the pharmaceutical samples such as tablets with metallic nanoparticles is a major issue for SER-CI analyses due to the difficulty to obtain a homogeneous covering of tablet surface with the SERS substrates. In this context, a spray-coating method was proposed in order to fully exploit the potential of SER-CI. A homemade apparatus has been developed from an electrospray ionization (ESI) probe in order to cover the pharmaceutical tablets with the colloidal suspension in a homogeneous way. The silver substrate was pulled through the airbrush by a syringe pump which was then nebulized into small droplets due to the contact of the solution with the gas flow turbulence. A robust optimization of the process was carried out by adjusting experimental parameters such as the liquid flow rate and the spraying time. Besides, the performances of this spraying technique were compared with two others covering methods found in the literature which are drop casting and absorption coating. A homogeneity study, conducted by SER-CI and matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) applied to the different covering techniques was performed. The influence of the metallic nanoparticles deposit on soluble compounds was also investigated in order to highlight the advantages of using this new spray coating approach.

An iterative approach for compound detection in an unknown pharmaceutical drug product: Application on Raman microscopy.

Raman chemical imaging provides both spectral and spatial information on a pharmaceutical drug product. Even if the main objective of chemical imaging is to obtain distribution maps of each formulation compound, identification of pure signals in a mixture dataset remains of huge interest. In this work, an iterative approach is proposed to identify the compounds in a pharmaceutical drug product, assuming that the chemical composition of the product is not known by the analyst and that a low dose compound can be present in the studied medicine. The proposed approach uses a spectral library, spectral distances and orthogonal projections to iteratively detect pure compounds of a tablet. Since the proposed method is not based on variance decomposition, it should be well adapted for a drug product which contains a low dose product, interpreted as a compound located in few pixels and with low spectral contributions. The method is tested on a tablet specifically manufactured for this study with one active pharmaceutical ingredient and five excipients. A spectral library, constituted of 24 pure pharmaceutical compounds, is used as a reference spectral database. Pure spectra of active and excipients, including a modification of the crystalline form and a low dose compound, are iteratively detected. Once the pure spectra are identified, multivariate curve resolution-alternating least squares process is performed on the data to provide distribution maps of each compound in the studied sample. Distributions of the two crystalline forms of active and the five excipients were in accordance with the theoretical formulation.

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined.

Distribution of a low dose compound within pharmaceutical tablet by using multivariate curve resolution on Raman hyperspectral images.

In this work, Raman hyperspectral images and multivariate curve resolution-alternating least squares (MCR-ALS) are used to study the distribution of actives and excipients within a pharmaceutical drug product. This article is mainly focused on the distribution of a low dose constituent. Different approaches are compared, using initially filtered or non-filtered data, or using a column-wise augmented dataset before starting the MCR-ALS iterative process including appended information on the low dose component. In the studied formulation, magnesium stearate is used as a lubricant to improve powder flowability. With a theoretical concentration of 0.5% (w/w) in the drug product, the spectral variance contained in the data is weak. By using a principal component analysis (PCA) filtered dataset as a first step of the MCR-ALS approach, the lubricant information is lost in the non-explained variance and its associated distribution in the tablet cannot be highlighted. A sufficient number of components to generate the PCA noise-filtered matrix has to be used in order to keep the lubricant variability within the data set analyzed or, otherwise, work with the raw non-filtered data. Different models are built using an increasing number of components to perform the PCA reduction. It is shown that the magnesium stearate information can be extracted from a PCA model using a minimum of 20 components. In the last part, a column-wise augmented matrix, including a reference spectrum of the lubricant, is used before starting MCR-ALS process. PCA reduction is performed on the augmented matrix, so the magnesium stearate contribution is included within the MCR-ALS calculations. By using an appropriate PCA reduction, with a sufficient number of components, or by using an augmented dataset including appended information on the low dose component, the distribution of the two actives, the two main excipients and the low dose lubricant are correctly recovered.

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets.

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl(®) tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution-alternating least squares (MCR-ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.

Synthesis and pharmacological evaluation of new 1,2-dithiolane based antioxidants.

Molecules containing a dithiolane moiety are widely investigated due to their antioxidant properties. The archetypal representative of this class of compounds is lipoic acid and indeed the lipoic acid-dihydrolipoic acid couple is part of the antioxidant defence system of the cell. In the course of a program aiming to find improved antioxidants effective in vivo, we designed, synthesised and pharmacologically investigated new lipoic acid analogs. The salient feature of these structures is the connection, via a thioamide or a thiocarbamate, of a 1,2-dithiolane moiety bearing a carbon chain and a N-alkyl-substituted morpholine ring. It was expected that the antioxidant and chelating properties of these functional groups combined with the basicity of the morpholine ring will impact on the antioxidant as well as on the partition and solubility characteristics of the compounds. Indeed in vitro and in vivo pharmacological investigation showed that these new molecules and especially those containing a thiocarbamate linker possess superior antioxidant properties compared with alpha-lipoic acid and to the amide or carbamate linker analogs. In particular, some of these compounds efficiently cross the blood brain barrier (BBB) thus providing efficient protection from lethality in a situation of induced oxidative stress. Moreover the absence of the 1,2-dithiolane moiety does not completely abolish antioxidant effects thus demonstrating that these compounds are distinct new chemical entities and not merely lipoic acid prodrugs. The chemical and pharmacological features of these new antioxidants are presented and discussed in the following paper.

Application of independent component analysis on Raman images of a pharmaceutical drug product: pure spectra determination and spatial distribution of constituents.

Independent component analysis (ICA) was used as a blind source separation method on a Raman image of a pharmaceutical tablet. Calculations were performed without a priori knowledge concerning the formulation. The aim was to extract the pure signals from the initial data set in order to examine the distribution of actives and major excipients within the tablet. As a method based on the decomposition of a matrix of mixtures of several components, the number of independent component to choose is a critical step of the analysis. The ICA_by_blocks method, based on the calculation of several models using an increasing number of independent components on initial matrix blocks, was used. The calculated ICA signals were compared with the pure spectra of the formulation compounds. High correlations between the two active principal ingredient spectra and their corresponding calculated signals were observed giving a good overview of the distributions of these compounds within the tablet. Information from the major excipients (lactose and avicel) was found in several independent components but the ICA approach provides high level of information concerning their distribution within the tablet. However, the results could vary considerably by changing the number of independent components or the preprocessing method. Indeed, it was shown that under-decomposition of the matrix could lead to better signal quality (compared to the pure spectra) but in that case the contributions due to minor components or effects were not correctly identified and extracted. On the contrary, over-decomposition of the original dataset could provide information about low concentration compounds at the expense of some loss of signal interpretability for the other compounds.

Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: model optimisation, calibration transfer and confidence intervals.

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation.