[Optimize the preparation process of Erigeron breviscapus sustained-release pellets based on artificial neural...

Area of Science:

• Pharmaceutical Technology
• Computational Chemistry

Background:

• Erigeron breviscapus is a valuable medicinal herb.
• Developing sustained-release formulations is crucial for effective drug delivery.
• Optimizing complex preparation processes requires advanced methodologies.

Purpose of the Study:

• To optimize the preparation process of Erigeron breviscapus sustained-release pellets.
• To establish a predictive model for pellet preparation.
• To achieve enhanced drug release profiles.

Main Methods:

• Utilized back-propagation (BP) artificial neural networks (ANN) to model the relationship between preparation variables and pellet properties.
• Employed particle swarm optimization (PSO) algorithm to optimize the identified preparation parameters.
• Prepared Erigeron breviscapus sustained-release pellets using the optimized parameters.

Main Results:

• The optimized preparation process yielded pellets with significant sustained-release effects.
• Drug release mechanism was identified as a combination of diffusion and matrix corrosion.
• The developed model accurately predicted the outcomes of the preparation process.

Conclusions:

• The combination of BP ANN modeling and PSO algorithm offers an effective solution for multi-dimensional optimization problems in pharmaceutical technology.
• This integrated approach can be applied to optimize complex nonlinear systems in drug formulation.
• The study demonstrates a powerful computational strategy for enhancing pharmaceutical manufacturing processes.