Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data

Abstract

In this work spectroscopic measurements, process data and Critical Material Attributes (CMAs) are used to predict the in vitro dissolution profile of sustained-release tablets with three machine learning methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble of Regression Trees (ERT). Beside the effect of matrix polymer content and compression force, the influence of active pharmaceutical ingredient (API) and matrix polymer particle size distribution (PSD) on the drug release rate of sustained tablets is studied. The matrix polymer PSD was found to be a significant factor, thus this factor was included in the dissolution prediction experiments. In order to evaluate the importance of the inclusion of PSD data, models without PSD data were also prepared and the results were compared. In the developed models, the API and hydroxypropyl-methylcellulose (HPMC) content is predicted from near-infrared (NIR) spectra, the compression force is measured by the tablet press and HPMC particle size is measured off-line. The predictions of ANN, SVM and ERT were compared to the measured dissolution profiles of the validation tablets, ANN yielded the most accurate results. In the presented work, data provided by Process Analytical Technology (PAT) sensors is combined with CMAs for the first time to realize the Real-Time Release Testing (RTRT) of tablet dissolution.

Keywords: Dissolution prediction; Machine learning; NIR spectroscopy; PAT; Particle size data; Sustained-release tablets.