A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design.

ABSTRACT: Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms-NSGA-III, MOGWO, and NSWOA-were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal for

SUBMITTER: Qiao Y 

PROVIDER: S-EPMC12654971 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature