Cushla M. McGoverin

Raman mapping of pharmaceuticals

Raman spectroscopy may be implemented through a microscope to provide fine scale axial and lateral chemical maps. The molecular structure of many drugs makes Raman spectroscopy particularly well suited to the investigation of pharmaceutical systems. Chemometric methods currently used to assess bulk Raman spectroscopic data are typically applied to Raman mapping data from pharmaceuticals; few reports exist where the spatial information inherent to a mapped dataset is used for the calculation of chemical maps. Both univariate and multivariate methods have been applied to Raman mapping data to determine the distribution of active pharmaceutical ingredients (APIs) in tablets, solid dispersions for increased solubility and controlled release devices. The ability to axially (depth) profile using Raman mapping has been used in studies of API penetration through membranes, cellular uptake of drug delivery liposomes, and initial API distribution and subsequent elution from coatings of medical devices. New instrumental developments will increase the efficiency of Raman mapping and lead to greater utilisation of Raman mapping for analyses of pharmaceutical systems.

Recent pharmaceutical applications of raman and terahertz spectroscopies

This review outlines recent applications of Raman and terahertz spectroscopies within the field of pharmaceutical research. Of the two approaches, Raman is better established and more accessible, and is responsible for the majority of reviewed studies. Both techniques feature limitations, however, which are discussed in the context of methods used to circumvent apparent restrictions. Regardless, the diverse range of applications illustrates the flexibility of Raman and terahertz spectroscopies when characterizing pharmaceutical systems.

Understanding the solid-state forms of fenofibrate – A spectroscopic and computational study

The aim of this study was to investigate the structure of different solid-state forms of fenofibrate, a drug that lacks strong intermolecular interactions such as hydrogen bonding. In addition to a structural analysis of crystalline and amorphous fenofibrate using infrared and Raman spectroscopy combined with density functional theory calculations [B3LYP 6-31G(d)], solid-state changes that occur upon recrystallization of amorphous fenofibrate were monitored and described using in situ Raman spectroscopy. A comparison of the calculated vibrational spectra of a fenofibrate monomer and two dimer structures with the experimental vibrational spectra of crystalline and amorphous fenofibrate revealed conformational differences in the orientation of the two benzyl rings in the fenofibrate molecule and structural differences between the different solid-state forms in aliphatic parts of the drug molecule. The spectroscopic analysis suggests that non-hydrogen-bonded drug molecules are likely to exhibit more random molecular orientations and conformations in the amorphous phase since the weak intermolecular interactions that occur between such molecules can easily be disrupted. In situ Raman spectroscopy and multivariate analysis revealed multiple solid-state forms of fenofibrate, including the metastable crystalline form II, which were structurally analyzed with reference to the quantum chemical calculations. Overall, the study showed that vibrational spectroscopy, multivariate analysis, and quantum chemical modeling are well suited to investigate and characterize the structure of drug substances that exhibit only small structural differences between different solid-state forms.

A review of triticale uses and the effect of growth environment on grain quality

Triticale (× Triticosecale sp. Wittmack ex A. Camus 1927) is an anthropogenic cereal designed to incorporate the functionality and high yield of wheat (Triticum spp. Linnaeus 1753) and durability of rye (Secale cereale Linnaeus 1753). The potential of triticale has remained largely unrealised, and in the 135 years since A. Stephen Wilson first crossed wheat and rye, triticale has mostly been used as animal feed. Growing demand for food resources has led to an increased interest in triticale development. Efforts to breed cultivars appropriate for baking have met with difficulty, although relatively new approaches to triticale end-use propose greater applicability for human consumption. Further, environmental awareness has generated interest in the use of triticale within biofuel production. We review environmental and genetic effects on triticale yield with a view towards increased demand on a hardy and useful cereal crop. We find triticale could satisfy many of the hopes originally placed upon it, and may be useful in foodstuffs and fuel, but only when growth environment is carefully considered.

Review: The application of near infrared spectroscopy to the measurement of bioactive compounds in food commodities

The perceived benefit of functional foods in the prevention or mitigation of degenerative diseases has stimulated the growth of the functional food market. This perception is based on the presence in these foods of specific molecules which have a positive pharmacological effect when consumed in sufficient quantities (bioactive compounds). The increasing market and consumer desire for quality food products with positive health benefits has created a need for efficient and accurate analytical methods for the quantification of bioactive compounds in raw materials and finished products. Near infrared (NIR) spectroscopy is a fast, non-destructive and accurate method of analysis that has been extensively utilised for the study of foods. NIR spectroscopy has been used to quantify carotenoids, polyphenols, fatty acids and glucosinolates in a wide range of food commodities, for example, wine, dairy products, tea, fruit, vegetables, herbs, spices and cereals. Often, these quantifications are based on data from both the NIR and visible spectral regions; several bioactive compounds are also considered pigments, hence the utility of the visible spectral region. Major classes of other bioactive compounds, including pre- and probiotics, have yet to be analysed using NIR spectroscopy. The use of NIR spectroscopy for analysis of bioactive compounds is expected to match the growth of the functional food and bioactive ingredients markets.

Quantification of Process Induced Disorder in Milled Samples Using Different Analytical Techniques

The aim of this study was to compare three different analytical methods to detect and quantify the amount of crystalline disorder/ amorphousness in two milled model drugs. X-ray powder diffraction (XRPD), differential scanning calorimetry (DSC) and Raman spectroscopy were used as analytical methods and indomethacin and simvastatin were chosen as the model compounds. These compounds partly converted from crystalline to disordered forms by milling. Partial least squares regression (PLS) was used to create calibration models for the XRPD and Raman data, which were subsequently used to quantify the milling-induced crystalline disorder/ amorphousness under different process conditions. In the DSC measurements the change in heat capacity at the glass transition was used for quantification. Differently prepared amorphous indomethacin standards (prepared by either melt quench cooling or cryo milling) were compared by principal component analysis (PCA) to account for the fact that the choice of standard ultimately influences the quantification outcome. Finally, the calibration models were built using binary mixtures of crystalline and quench cooled amorphous drug materials. The results imply that the outcome with respect to crystalline disorder for milled drugs depends on the analytical method used and the calibration standard chosen as well as on the drug itself. From the data presented here, it appears that XRPD tends to give a higher percentage of crystalline disorder than Raman spectroscopy and DSC for the same samples. For the samples milled under the harshest milling conditions applied (60 min, sixty 4 mm balls, 25 Hz) a crystalline disorder / amorphous content of 44.0% (XRPD), 10.8% (Raman spectroscopy) and 17.8% (DSC) were detected for indomethacin. For simvastatin 18.3% (XRPD), 15.5% (Raman spectroscopy) and 0% (DSC, no glass transition) crystalline disorder/ amorphousness were detected.

Investigating the relationship between drug distribution in solid lipid matrices and dissolution behaviour using raman spectroscopy and mapping

In this study, in situ and mapping Raman spectroscopic measurements were used to investigate the physical structure of solid lipid extrudates and relate the structure to dissolution behaviour. Theophylline anhydrate was extruded with tripalmitin, with and without the water-soluble polymer, polyethylene glycol 10000. Raman mapping of the extrudate cores revealed that drug particles of diverse size were dispersed in a continuous lipid phase with or without polyethylene glycol. At the surface, there was evidence of more mixing between the components. Previous characterisation by other methods suggested that the extrudate surface is covered predominantly by lipid, and the Raman mapping suggested that such a layer is in general less than a few micrometres thick. Nevertheless, the lipid layer dramatically reduced the drug dissolution rate. The extrudate cores were also mapped after a period of dissolution testing, and there was no evidence of a uniformly receding drug boundary in the extrudates during drug release. In situ Raman spectroscopy analysis during dissolution testing revealed that the drug distribution in the extrudate affected the formation of theophylline monohydrate. However, the drug release rate was primarily determined directly by drug distribution, with the solid-state behaviour of the drug having a smaller influence.

Analysis of matrix dosage forms during dissolution testing using raman microscopy

Matrix dosage forms are widely used for sustained drug release. As both the distribution of the matrix components and physical changes during dissolution can impact drug release behavior, a comprehensive investigation of these phenomena is required during matrix development. In this study, Raman microscopy was used to investigate different extrudate formulations in terms of component distribution and structural changes during dissolution testing. Two systems containing the model drug theophylline anhydrate were investigated: a binary system, based on a tripalmitin matrix, and a ternary system, containing tripalmitin and polyethylene glycol. The distribution of the drug and the soluble and insoluble matrix components were mapped during dissolution testing. Although a receding drug boundary was observed, it was not uniformly distant from the matrix edge. The lipid structure remained intact, whereas the water-soluble polymer rapidly dissolved and diffused from the matrix leaving a more extensive network of channels through which the dissolution medium could penetrate and the drug could diffuse. Raman mapping can be considered a useful aid in the direct analysis of multiple matrix components during drug release, and therefore a deeper understanding of factors affecting drug release can be obtained during the development of sustained-release matrices.

Classification of Maize Kernel Hardness Using near Infrared Hyperspectral Imaging

Maize is an internationally important food crop that is usually milled before use. Milling yield is strongly influenced by maize hardness which, in turn, is controlled by the relative proportions of vitreous and floury endosperm within a single kernel. Current conventional near infrared (NIR) spectroscopic methods for determining maize hardness require reference data, which is obtained by destroying multiple kernels. In contrast, NIR hyperspectral imaging (NIR-HSI) has shown promise for determining the hardness of individual maize kernels without sample destruction or the need for reference data. This is possible due to the spatial dimension, in addition to the spectral dimension, offered by NIR-HSI. To illustrate this, NIR-HSI was used to characterise regions of germ, vitreous endosperm and floury endosperm from both the endosperm-rich (germ-down; GD) and endosperm-poor (germ-up; GU) surfaces of 155 single maize kernels. The proportions (expressed as % of whole image) of germ, vitreous and floury endosperm were determined from these images after principal component analysis was applied. Subsequent manual kernel dissection confirmed that the ratio of vitreous to floury endosperm was higher in kernels determined to be harder by NIR-HSI. The correlation coefficients between the manually obtained proportions (i.e. dissected) and proportions determined from NIR hyperspectral images (i.e. GU and GD images averaged) were 0.61, 0.59 and 0.11 for vitreous endosperm, floury endosperm and germ, respectively. The incongruence between the two types of determination reflects the surface-bias of reflectance spectroscopy. Irrespectively, NIR-HSI reflectance models could be developed without a reference method and applied to rapidly determine very hard from very soft kernels.

Influence of grain topography on near infrared hyperspectral images

Near infrared hyperspectral imaging (NIR-HSI) allows spatially resolved spectral information to be collected without sample destruction. Although NIR-HSI is suitable for a broad range of samples, sizes and shapes, topography of a sample affects the quality of near infrared (NIR) measurements. Single whole kernels of three cereals (barley, wheat and sorghum), with varying topographic complexity, were examined using NIR-HSI. The influence of topography (sample shape and texture) on spectral variation was examined using principal component analysis (PCA) and classification gradients. The greatest source of variation for all three grain types, despite spectral preprocessing with standard normal variate (SNV) transformation, was kernel curvature. Only 1.29% (PC5), 0.59% (PC6) and 1.36% (PC5) of the spectral variation within the respective barley, wheat and sorghum image datasets was explained within the principal component (PC) associated with the chemical change of interest (loss of kernel viability). The prior PCs explained an accumulated total of 91.18%, 89.43% and 84.39% of spectral variance, and all were influenced by kernel topography. Variation in sample shape and texture relative to the chemical change of interest is an important consideration prior to the analysis of NIR-HSI data for non-flat objects.