Designing of Mathematical Models For Drug Release Similarity

Abstract

Drug dissolution testing is crucial for drug development, providing insights into the release of Active Pharmaceutical Ingredients (APIs) from dosage forms. Drug dissolution helps with process monitoring, formulation enhancement, bioequivalence confirmation, and ensuring product quality. Comparing drug release profiles of different pharmaceutical products is essential.The similarity factor f2, which was proposed by Moore and Flanner in 1996, has been endorsed by both the European Medicines Agency (EMA) and the US Food and Drug Administration (USFDA) for comparing drug release profiles. Although f newline2x is easy to manipulate; the results do newlinenot change if the test and reference interchange. However, it lacks scientific justification and has the following disadvantages: Since it does not rely on a hypothesis testing process, the error (Type I or Type II errors) is not measured The method will not work if the time points selected for two drug products are different. In the FDA guideline, no time point selection is specified. Only point estimates of the model parameters for similarity assessment based on mean standard deviation (MSD) are recommended by FDA guidance.Till date no universal agreement is proved for the best approach for comparing dissolution profile data in pharmaceutical research and development. The goal of this work is to develop a mathematical model that can be used to investigate the impact of additional intervening timepoints on the similarity factor f 2xi for all drug release. Cubic newlinespline interpolation and the linear regression model were used to calculate the additional intervening time points. An artificial neural network (ANN) has been found useful in assisting several formulation variables and process variables in drug formulation. Also, ANN helps in optimization and evaluation of immediate and modified dosage form to check drug release similarity.