Determining particle size and water content by near-infrared spectroscopy in the granulation of naproxen sodium

Polyvinyl alcohol-based capsule shells manufactured by injection molding as ready-to-use moisture barriers for the development of delivery systems

In this work, feasibility of injection molding was demonstrated for manufacturing capsule shells. 600 µm-thick prototypes were successfully molded with pharmaceutical-grade low-viscosity polyvinyl alcohols (PVAs), possibly added with a range of different fillers. They showed reproducible weight and thickness (CV < 2 and 5, respectively), compliant behavior upon piercing (holes diameter analogous to the reference), tunable release performance (immediate and pulsatile), and moisture protection capability. To assess the latter, an on-line method relying on near infrared spectroscopy measurements was set-up and validated. Based on the data collected and considering the versatility IM would provide for product shape/thickness/composition, PVA-based molded shells could help widening the portfolio of ready-to-use capsules, representing an interesting alternative to those commercially available. Indeed, these capsules could be filled with various formulations, even those with stability issues, and intended either for oral administration or for pulmonary delivery via single-dose dry powder inhalers.

Rapid and Non-Destructive Estimation of Moisture Content in Caragana Korshinskii Pellet Feed Using Hyperspectral Imaging

Moisture content is an important parameter for estimating the quality of pellet feed, which is vital in nutrition, storage, and taste. The ranges of moisture content serve as an index for factors such as safe storage and nutrition stability. A rapid and non-destructive model for the measurement of moisture content in pellet feed was developed. To achieve this, 144 samples of Caragana korshinskii pellet feed from various regions in Inner Mongolia Autonomous Region underwent separate moisture content control, measurement using standard methods, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 935.5-2539 nm. The Monte Carlo cross validation (MCCV) was used to eliminate abnormal sample data from the spectral data for better model accuracy, and a global model of moisture content was built by using partial least squares regression (PLSR) with seven preprocessing techniques and two spectral feature extraction techniques. The results showed that the regression model developed by PLSR based on second derivative (SD) and competitive adaptive reweighted sampling (CARS) resulted in better performance for moisture content. The model showed predictive abilities for moisture content with a coefficient of determination of 0.9075 and a root mean square error (RMSE) of 0.4828 for the training set; and a coefficient of determination of 0.907 and a root mean square error (RMSE) of 0.5267 for the test set; and a relative prediction error of 3.3 and the standard error of 0.307.

Spectroscopic evaluation on pseudopolymorphs of sodium naproxen

This paper demonstrates the assessment of vibrational spectroscopy methods such as middle infrared, near infrared and Raman spectroscopy (FTIR, FT-NIR, Raman) for the identification of pseudopolymorphic forms of a model active pharmaceutical ingredient (API) - sodium naproxen (NpxNa). NpxNa, in the form of three different pseudopolymorphs, was investigated by methods dedicated for solid state characterization: DSC (differential scanning calorimetry), XPRD (powder X-ray diffraction), SEM (scanning electron microscopy) and Karl Fischer titration. Novelty in the results sourced in the usage of the method not applied so far to identify pseudopolymorphic forms of NpxNa, that is, FTIR and FT-NIR. Based on the obtained reproduceable results, various pseudopolymorphic forms were successfully evaluated. Spectroscopic data were correlated with DSC and XPRD results. It was concluded that the combination of band's variations visible on the spectra of pseudopolymorphic forms will allow to interpretate the results unequivocally in case of crucial stability tests of medicinal substance or during on-line pharmaceutical process development by FTIR, FT-NIR and Raman spectroscopy.

A Novel Method for Rapid Particle Size Analysis of Ibuprofen Using Near-infrared Spectroscopy

Particle size distribution (PSD) is often considered as critical material attribute for active pharmaceutical ingredients (APIs), and the need for regular evaluation stands as an important quality control parameter in the pharmaceutical industry. Near-infrared (NIR) spectroscopy, used routinely for API identification, was introduced as analytical tool for simultaneous determination of particle size of ibuprofen. The demonstrated potential was highlighted by the development of rapid, robust, and noninvasive method coupled with multivariate data analysis (MVA), which can be easily transferred in QC laboratories for routine analysis. Principal component analysis (PCA) and partial least squares (PLS) regression analyses were performed on a calibration set of 61 ibuprofen samples, which differed in their median particle size Dv(50). The score scatterplots revealed evident clustering of ibuprofen samples according to their particle size, as well as occurrence of a distinctive outlying group of ibuprofen samples originating from one manufacturer. Further testing by means of mid-infrared spectroscopy, X-ray powder diffraction, and particle morphology analysis pinpointed particle morphology being responsible for the observed outlying group. Consequently, PLS class modeling based on particle morphology was introduced, which delivered two separate PLS regression models: one for blade-like ibuprofen crystals and another for irregular plate-like ibuprofen crystals. The former regression model exhibited high correlation coefficients and satisfactory predictive power (R²X = 0.999, R²Y = 0.917, Q² = 0.901), whereas the latter demonstrated lower statistical indicators (R²X = 0.99, R²Y = 0.72, Q² = 0.55). Additionally, the study underlines the importance of particle shape evaluation and sample classification according to particle morphology similarity prior to building NIRS-based regression models for PSD determination.

Investigation of Preprocessing and Validation Methodologies for PAT: Case Study of the Granulation and Coating Steps for the Manufacturing of Ethenzamide Tablets

After the Food and Drug Association in the USA published guidelines on the enhanced use of process analytical technology (PAT) and continuous manufacturing, many studies regarding PAT and continuous manufacturing have been published. This paper describes a case study involving granulation and coating steps with ethenzamide to investigate interference for PAT model construction and model management. We investigated what factors should be considered and addressed when PAT is implemented for continuous manufacturing and how predictive models should be constructed. The product qualities that were monitored were moisture content and particle size in the granulation step and tablet weight and moisture content in the coating step. We have constructed models for the granulation step and validated the predictive capability of the models against an external dataset. A partial least squares (PLS) model with manual wavelength selection had the best predictive accuracy for loss on drying against the external validation set. We found that the prediction of loss on drying was accurate, but the prediction of particle size was not sufficiently accurate. In the coating step, because of the small amount of data, we performed three-fold cross-validation and y-scrambling 10 times, to select the optimal hyper-parameters and to check if the models were fitted to chance correlations. We confirmed that the coating agent weights, tablet weights, and water content could be accurately predicted based on the mean of the R² score for cross-validation. Addition of other variables, as well as the absorbance, slightly improved the predictive accuracy.