Understanding the Impact of Chemical Variability and Calibration Algorithms on Prediction of Solid Fraction of Roller Compacted Ribbons Using Near-Infrared (NIR) Spectroscopy

Abstract

The study is aimed at developing a near-infrared (NIR) method for predicting solid fraction (SF) of dry granulated ribbons manufactured with formulation variability. The study investigated the impact of unmodeled chemical variability and regression approaches on method performance. The study utilized an excipient-only formulation system. Calibration compacts were created with chemical and processing variability; followed by collection of NIR spectra. Partial least squares (PLS) and spectral slope algorithms were utilized to model compact SF. Later, the models were deployed to predict SF of test ribbons and compacts containing an API at various concentrations. The risk associated with unmodeled chemical variation manifested itself through generation of new peaks and decreased baseline absorbance in the NIR spectra. The spectral slope was able to better manage this risk, as demonstrated by relatively higher robustness to the increasing load of the active pharmaceutical ingredient (API). The reduced robustness of the PLS approach was attributed to the impact of chemical variability on both spectral baseline and peak absorbance. A prediction error of approximately 5% was observed at 10% drug load using the spectral slope approach. An understanding of the risk associated with unmodeled variability will enable NIR method development as an API sparing technique for low-dose product development.