Comparison of Surrogate Models in Tablet Dissolution Prediction: Addressing the Limitations of F₂ and Introducing Sum of Ranking Differences for Model Evaluation.

Abstract

As process analytical technology (PAT) and real-time release testing (RTRT) are gaining momentum in the pharmaceutical industry, there is an increasing need for developing methods for the non-destructive and real-time characterization of the in vitro dissolution of pharmaceuticals. In recent years, several surrogate models relying on PAT measurements and advanced chemometric techniques have been published addressing this task. Nevertheless, methodologies for the fair comparison of the model performance and setting relevant acceptance criteria are still not well established. Therefore, this study aims to draw attention to appropriate model comparison when developing and applying surrogate dissolution models and highlight the limitations of the widely used dissolution curve comparison metrics, including the f₂ similarity value. A set of 10 different artificial neural network (ANN) models were developed for the prediction of the dissolution profiles of clopidogrel tablets produced through hot-melt granulation and tableting. Models were fitted with diverse input data, including granulation nominal experiment settings and real recorded process parameters (e.g., air and material temperature, humidity, granulation and lubrication time, tableting pressure) and near-infrared spectra. The models' goodness was compared using the f₂ factor, coefficient of determination (R²) and root mean square error (RMSE). The results demonstrated that these measures do not sufficiently reflect the discriminating ability of the models. We proposed for the first time the use of the sum of ranking differences (SRD) method for the comparison of the prediction models, which proved to be an effective tool to assess the discriminatory power of surrogate dissolution models during model development.