Chemometric Evaluation of Near Infrared, Fourier Transform Infrared, and Raman Spectroscopic Models for the Prediction of Nimodipine Polymorphs

Advances in powder bed fusion 3D printing in drug delivery and healthcare

Powder bed fusion (PBF) is a 3D printing method that selectively consolidates powders into 3D objects using a power source. PBF has various derivatives; selective laser sintering/melting, direct metal laser sintering, electron beam melting and multi-jet fusion. These technologies provide a multitude of benefits that make them well suited for the fabrication of bespoke drug-laden formulations, devices and implants. This includes their superior printing resolution and speed, and ability to produce objects without the need for secondary supports, enabling them to precisely create complex products. Herein, this review article outlines the unique applications of PBF 3D printing, including the main principles underpinning its technologies and highlighting their novel pharmaceutical and biomedical applications. The challenges and shortcomings are also considered, emphasising on their effects on the 3D printed products, whilst providing a forward-thinking view.

DRIFTS-based multivariate calibration and prediction of low-concentration polymorphic impurities in multiple lots of an active pharmaceutical ingredient, and outlier criteria

Mixtures of two polymorphic impurities with one lot of the desired form of an Active Pharmaceutical Ingredient (API), mostly binary mixtures, with up to 2% wt/wt of an impurity, were used for multivariate modeling via Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectra. The two obtained cross-validated models, significantly differing in accuracy, were used to predict the concentrations of these impurities in independent API lots. The predictions were found to be biased and with outliers, as revealed by the Q residuals criterion but not the other two outlier criteria. Updating the models with the spectra from the mixtures using multiple API lots produced very different calibration results: the model of the impurity with the strong IR response became noticeably worse, while the model of the impurity with less responsive IR signal changed only marginally. The updated models performed much better in the prediction as the bias for both polymorphs was reduced and the outlier-related issues mostly disappeared.

Recent progress of structural study of polymorphic pharmaceutical drugs

This review considers advances in the understanding of active pharmaceutical ingredient polymorphism since around 2010 mainly from a structural view point, with a focus on twelve model drugs. New polymorphs of most of these drugs have been identified despite that the polymorphism of these old drugs has been extensively studied so far. In addition to the conventional modifications of preparative solvents, temperatures, and pressure, more strategic structure-based methods have successfully yielded new polymorphs. The development of analytical techniques, including X-ray analyses, spectroscopy, and microscopy has facilitated the identification of unknown crystal structures and also the discovery of new polymorphs. Computational simulations have played an important role in explaining and predicting the stability order of polymorphs. Furthermore, these make significant contributions to the design of new polymorphs by considering structure and energy. The new technologies and insights discussed in this review will contribute to the control of polymorphic forms, both during manufacture and in the drug formulation.

Comparison of Univariate and Multivariate Models of ¹³C SSNMR and XRPD Techniques for Quantification of Nimodipine Polymorphs

The focus of the present investigation was to explore the use of solid-state nuclear magnetic resonance ((13)C ssNMR) and X-ray powder diffraction (XRPD) for quantification of nimodipine polymorphs (form I and form II) crystallized in a cosolvent formulation. The cosolvent formulation composed of polyethylene glycol 400, glycerin, water, and 2.5% drug, and was stored at 5°C for the drug crystallization. The (13)C ssNMR and XRPD data of the sample matrices containing varying percentages of nimodipine form I and form II were collected. Univariate and multivariate models were developed using the data. Least square method was used for the univariate model generation. Partial least square and principle component regressions were used for the multivariate models development. The univariate models of the (13)C ssNMR were better than the XRPD as indicated by statistical parameters such as correlation coefficient, R (2), root mean square error, and standard error. On the other hand, the XRPD multivariate models were better than the (13)C ssNMR as indicated by precision and accuracy parameters. Similar values were predicted by the univariate and multivariate models for independent samples. In conclusion, the univariate and multivariate models of (13)C ssNMR and XRPD can be used to quantitate nimodipine polymorphs.