Synthetic Calibration for Efficient Method Development: Analysis of Tablet API Concentration by Near-Infrared Spectroscopy

Abstract

Near-infrared (NIR) spectroscopy is an important process analytical technology (PAT) tool for rapid characterization of pharmaceutical tablet quality. The time and expense required for calibration development has been a detriment to implementation of PAT sensors. While methods based on generalized least-squares and net analyte signal pure-component projection (PCP) have been demonstrated to be useful tools for efficient spectroscopic calibration, PCP methods are relatively difficult to deploy and maintain in industrial settings. Synthetic calibration based on augmentation of parallel-testing data with artificial interference spectra generated in silico is introduced as a method to achieve efficient NIR calibration using off-the-shelf chemometric algorithms. A method for estimating a slope correction factor using parallel test data is shown. The results of this work demonstrate that, by using efficient calibration methods, accurate quantitative NIR calibration models for characterization of drug tablet quality can be created using only pure-component spectra and production-scale tablet samples.