Updating a near-infrared multivariate calibration model formed with lab-prepared pharmaceutical tablet types to new tablet types in full production

Determining active pharmaceutical ingredient (API) tablet concentrations rapidly and efficiently is of great importance to the pharmaceutical industry in order to assure quality control. Using near-infrared (NIR) spectra measured on tablets in conjunction with multivariate calibration has been shown to meet these objectives. However, the calibration is typically developed under one set of conditions (primary conditions) and new tablets are produced under different measurement conditions (secondary conditions). Hence, the accuracy of multivariate calibration is limited due to differences between primary and secondary conditions such as tablet variances (composition, dosage, and production processes and precision), different instruments, and/or new environmental conditions. This study evaluates application of Tikhonov regularization (TR) to update NIR calibration models developed in a controlled primary laboratory setting to predict API tablet concentrations manufactured in full production where conditions and tablets are significantly different than in the laboratory. With just a few new tablets from full production, it is found that TR provides reduced prediction errors by as much as 64% in one situation compared to no model-updating. TR prediction errors are reduced by as much as 51% compared to local centering, another calibration maintenance method. The TR updated primary models are also found to predict as well as a full calibration model formed in the secondary conditions.