Optimization and evaluation of time-dependent tablets comprising an immediate and sustained release profile using...

Area of Science:

• Pharmaceutical Technology
• Computational Pharmaceutics
• Drug Delivery Systems

Background:

• Time-dependent drug delivery systems require precise control over drug release kinetics.
• Optimizing complex formulations with multiple interacting variables presents a significant challenge in pharmaceutical development.
• Isosorbide-5-mononitrate (5-ISMN) is a key therapeutic agent requiring advanced delivery systems for sustained efficacy.

Purpose of the Study:

• To optimize the formulation of time-dependent tablets containing isosorbide-5-mononitrate (5-ISMN).
• To utilize artificial neural network (ANN) modeling for predicting and optimizing drug release profiles.
• To investigate the suitability of ANN in managing complex, non-linear relationships within pharmaceutical formulations.

Main Methods:

• Development of time-dependent tablets with a core of sustained and immediate release 5-ISMN, superdisintegrant (sodium carboxymethylstarch, CMS-Na), and an ethylcellulose/channeling agent coating.
• Application of a three-factor, three-level Box-Behnken design to optimize independent variables: tablet coating level, pellet coating level, and CMS-Na level.
• Analysis of release data using Artificial Neural Network (ANN) modeling and response surface plots to establish relationships between variables and predict release profiles.

Main Results:

• ANN successfully modeled and optimized the complex release profile of the time-dependent tablets.
• Optimized formulation parameters were determined: tablet coating level (4.1%), pellet coating level (14.1%), and CMS-Na level (29.8%).
• Observed drug release data closely matched the release patterns predicted by the ANN model for optimized formulations.

Conclusions:

• Artificial neural network (ANN) technique is highly suitable for optimizing complex pharmaceutical formulations, particularly time-dependent dosage forms.
• ANN effectively handles the non-linear relationships inherent in such systems, leading to accurate predictions and optimized drug release.
• The study demonstrates a powerful computational approach for advancing pharmaceutical technology and developing sophisticated drug delivery systems.