[Artificial neural network parameters optimization software and its application in the design of sustained release...

Area of Science:

• Pharmaceutical Sciences
• Computational Chemistry
• Biotechnology

Background:

• Artificial neural networks (ANN) are powerful multi-objective optimization tools.
• Their application in pharmaceutical research is limited by complex mathematical and statistical requirements.
• A need exists for simplified ANN application in drug formulation.

Purpose of the Study:

• To develop user-friendly software for optimizing artificial neural network parameters.
• To facilitate the application of ANNs in pharmaceutical research, specifically in sustained release formulation design.
• To reduce the complexity and difficulty associated with ANN implementation.

Main Methods:

• Developed artificial neural network parameters optimization software (ANNPOS) using Visual Basic.
• Utilized ANNPOS to estimate suitable ANN parameters for sustained release formulations.
• Employed Matlab 5.0 Neural Network Toolbox to determine optimal formulations based on ANNPOS estimations.

Main Results:

• The developed ANNPOS effectively simplified the application of ANNs.
• ANNPOS successfully estimated parameters for sustained release formulation design.
• The integration of ANNPOS with Matlab facilitated the determination of optimal drug formulations.

Conclusions:

• ANNPOS significantly reduces the complexity and difficulty of applying ANNs in pharmaceutical research.
• The software enhances the accessibility of advanced optimization techniques for formulation development.
• This approach holds promise for accelerating drug discovery and development processes.