The application of spectroscopic process analytical technology (PAT) for in-line data collection offers advantages to modern pharmaceutical manufacturing. Partial least squares (PLS) models are the preferred approach for predicting API potency from PAT data, particularly near-infrared (NIR) spectra. However, the calibration burden of PLS models is sometimes considered prohibitive. Pure component approaches, such as iterative optimization technology (IOT), have a reduced calibration burden for PAT applications. The IOT algorithm is dependent on several assumptions, including the harmonization of spectral collection conditions for pure component and mixture spectra. Collecting pure components under identical conditions to mixture spectra does not guarantee accurate predictions, and not all pure components are suitable for individual processing. This IOT assumption must be addressed to facilitate IOT application in PAT systems. In this work, IOT predicted API potency from in-line NIR spectra using combinations of stagnant and dynamic pure component spectra. A small number of mixture samples called a development set guided the selection of representative pure component spectral sets. Several model performance metrics from the development set predictions identified optimal pure component spectral sets for prediction of test sets. The combination of IOT and a development set generated accurate API potency predictions and potentiates the application of IOT in challenging pharmaceutical manufacturing settings. The IOT assumption of similar collection conditions should not be regarded as an assumption, but rather a consideration that the pure component spectral collection conditions should be representative of the mixture spectra to ensure appropriate predictions.
Keywords: Iterative Optimization Technology (IOT); Minimal Calibration; NIR; PAT; Pure Component Models; Small-Scale Simulator.
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