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.
