Application of chemometric methods to differential scanning calorimeter (DSC) to estimate nimodipine polymorphs from cosolvent system

Abstract

The focus of this study was to evaluate the applicability of chemometrics to differential scanning calorimetry data (DSC) to evaluate nimodipine polymorphs. Multivariate calibration models were built using DSC data from known mixtures of the nimodipine modification. The linear baseline correction treatment of data was used to reduce dispersion in thermograms. Principal component analysis of the treated and untreated data explained 96% and 89% of the data variability, respectively. Score and loading plots correlated variability between samples with change in proportion of nimodipine modifications. The R² for principal component regression (PCR) and partial lease square regression (PLS) were found to be 0.91 and 0.92. The root mean square of standard error of the treated samples for calibration and validation in PCR and PLS was found to be lower than the untreated sample. These models were applied to samples recrystallized from a cosolvent system, which indicated different proportion of modifications in the mixtures than those obtained by placing samples under different storage conditions. The model was able to predict the nimodipine modifications with known margin of error. Therefore, these models can be used as a quality control tool to expediently determine the nimodipine modification in an unknown mixture.

• Chemometrics
• DSC
• nimodipine
• PCS
• PLS
• polymorphs

Acknowledgements

The authors would like to thank Medical Counter Measure Initiative (MCMi) for infrastructure support and Oak Ridge Institute for Science and Education (ORISE) for supporting post-doctoral research program.

Declaration of interest

The findings and conclusions in this article have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any Agency determination or policy.