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

Abstract

The focus of the present investigation was to explore the use of solid-state nuclear magnetic resonance (13C 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 13C 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 13C ssNMR were better than the XRPD as indicated by statistical parameters such as correlation coefficient, R2, root mean square error, and standard error. On the other hand, the XRPD multivariate models were better than the 13C 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 13C ssNMR and XRPD can be used to quantitate nimodipine polymorphs.