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Multi-Objective Spray Drying Process Optimization via BBD-ANN-AGWO Framework: Case of Andrographolide Amorphous Solid

Guangpu Fang1,2, Changhao Jia1,2, Zhiqi Guan1

  • 1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.

AAPS Pharmscitech
|February 25, 2026
PubMed
Summary

This study introduces an intelligent framework for optimizing spray drying processes using artificial neural networks and an adaptive grey wolf optimizer. The method enhances drug formulation development by improving energy efficiency, yield, and drug release.

Keywords:
adaptive grey wolf optimizeramorphous solid dispersionandrographolideartificial neural networkmulti-objective

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Area of Science:

  • Pharmaceutical Sciences
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Spray drying is a critical process in pharmaceutical formulation for creating amorphous solid dispersions.
  • Optimizing multi-objective spray drying parameters (e.g., energy consumption, yield, drug release) is complex.
  • Andrographolide (ADG) amorphous solid dispersion serves as a model system for process optimization.

Purpose of the Study:

  • To establish a multi-objective spray drying process optimization framework.
  • To integrate Box-Behnken design (BBD), artificial neural networks (ANN), and an adaptive grey wolf optimizer (AGWO).
  • To optimize the formulation of andrographolide (ADG) amorphous solid dispersion.

Main Methods:

  • Molecular docking screened potential polymeric carriers for optimal solubilizing performance.
  • In vitro dissolution experiments identified the best-performing carrier.
  • Single-factor and BBD trials evaluated process parameters' effects on key outcomes.
  • A multi-response ANN model was developed and augmented with virtual samples.
  • AGWO performed inverse optimization to determine optimal process parameters.

Main Results:

  • Experimental validation confirmed the model's predictive accuracy.
  • X-ray diffraction (XRD) and Fourier-transform infrared (FT-IR) analyses verified ADG's amorphous transformation and stability.
  • The optimized process demonstrated satisfactory short-term physical stability.
  • The framework successfully balanced multiple optimization objectives.

Conclusions:

  • The proposed framework offers a feasible and intelligent strategy for pharmaceutical formulation development.
  • The integration of BBD, ANN, and AGWO enables efficient multi-objective optimization.
  • This approach can be applied to optimize various pharmaceutical processes for enhanced drug delivery.