Tools for the Calculation of Dissolution Experiments and their Predictive Properties

Dissolution testing, which establishes the rate and extent of the drug release from pharmaceutical products intended for oral administration, has been recognized as a crucial method for drug development and quality control of dosage form. Dissolution studies also help in establishing the in vitro and in vivo correlative studies, i.e., they can predict drug release and absorption without performing the study inside living things. The calculation and interpretation of dissolution data is a very typical task but it has been made simple by using various software and mathematical tools that easily analyze and illustrate the drug release data with their interpretation. Currently, most pharmaceutical companies believe in real-time prediction of dissolution profiles, which they have done due to their market position and increasing demand. Because of their competitiveness and rising demand, the majority of pharmaceutical businesses now support real-time prediction of dissolution profiles. As a result, alternative methods have been added to acquire a rapid response, such as spectroscopic approaches, particularly near-infrared spectroscopy (NIRS), which gathers the data based on the physicochemical features of the dosage form. Advanced multivariate analytic approaches, such as principal component analysis (PCA), principal component regression, and classical least squares regression, are widely employed to extract such data for use in quantitative modelling. There is still a dearth of research into the combined impact of numerous critical factors and their interactions on dissolution, despite several studies showing that drug product dissolution profiles can potentially be predicted from material, formulation, and process information using advanced mathematical approaches.

Keywords: Dissolution studies, Mathematical model, NIRS, PCA, Quantitative modelling.