Prediction of entire tablet formulations from pure powder components' spectra via a two-step non-linear optimization methodology

Abstract

Process analytical technology in the pharmaceutical industry requires the monitoring of critical quality attributes (CQA) through calibrated models. However, the development, implementation, and maintenance of these quantitative models are both resource and time-intensive. This study proposes the implementation of a non-linear iterative optimization technology (IOT) to study the magnitude of analytical errors when the calibration tablet used to extract the λ vector deviates physically and chemically from the test samples. IOT is based on mathematical optimization of excess spectral absorbance. It requires minimum calibration effort and allows simultaneous prediction of the entire formulation instead of only the active pharmaceutical ingredient (API), with just one standard and pure component spectral data. Unlike Partial Least Squares (PLS), which requires the development of standards to incorporate variations in the process, this non-destructive methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. The method described in this study allows a fast re-calculation to include factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet preparation. The robustness of the proposed approach for variation in compaction (physical changes) and variation in composition (chemical changes) was evaluated for correlated and uncorrelated formulations. For uncorrelated formulation a PLS model was also constructed to compare the robustness of the proposed methodology. The RMSEP of API in target formulation predicted using non-linear IOT method was varied from 0.17 to 1.50 depends on compaction of tablet chosen to compute λ vector. On the other hand, the RMSEP of API in target formulation predicted using PLS-based model was varied from 0.13 to 0.57 depending on compaction of tablet. The additional accuracy achieved in PLS based model required significant calibration effort of preparing 84 tablets compared to just one in proposed non-linear IOT method.

Keywords: CQA; Calibration-effort; NIR; Optimization; PAT; PLS.