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Pharmaceutics
Volume 17
Issue 11
10.3390/pharmaceutics17111419
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Yuchao Qiao
1,2,
Yijia Wu
1,2,
Mengchen Han
1,2,
Hao Ren
1,2,
Yu Cui
1,2,
Xuchun Wang
1,2,
Yiming Lou
1,2,
Chongqi Hao
1,2,
Quan Feng
1 and
Lixia Qiu
1,2,*
1
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2
Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Jinzhong 030600, China
*
Author to whom correspondence should be addressed.
Pharmaceutics 2025, 17(11), 1419; https://doi.org/10.3390/pharmaceutics17111419 (registering DOI)
Submission received: 23 September 2025 / Revised: 12 October 2025 / Accepted: 31 October 2025 / Published: 1 November 2025
(This article belongs to the Section Physical Pharmacy and Formulation)
Versions Notes

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 formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO4 (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations.
Keywords: sustained-release formulation; intelligent optimization algorithm; multi-objective optimization; exterior penalty function; multi-criteria decision-making sustained-release formulation; intelligent optimization algorithm; multi-objective optimization; exterior penalty function; multi-criteria decision-making

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MDPI and ACS Style

Qiao, Y.; Wu, Y.; Han, M.; Ren, H.; Cui, Y.; Wang, X.; Lou, Y.; Hao, C.; Feng, Q.; Qiu, L. A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design. Pharmaceutics 2025, 17, 1419. https://doi.org/10.3390/pharmaceutics17111419

AMA Style

Qiao Y, Wu Y, Han M, Ren H, Cui Y, Wang X, Lou Y, Hao C, Feng Q, Qiu L. A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design. Pharmaceutics. 2025; 17(11):1419. https://doi.org/10.3390/pharmaceutics17111419

Chicago/Turabian Style

Qiao, Yuchao, Yijia Wu, Mengchen Han, Hao Ren, Yu Cui, Xuchun Wang, Yiming Lou, Chongqi Hao, Quan Feng, and Lixia Qiu. 2025. "A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design" Pharmaceutics 17, no. 11: 1419. https://doi.org/10.3390/pharmaceutics17111419

APA Style

Qiao, Y., Wu, Y., Han, M., Ren, H., Cui, Y., Wang, X., Lou, Y., Hao, C., Feng, Q., & Qiu, L. (2025). A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design. Pharmaceutics, 17(11), 1419. https://doi.org/10.3390/pharmaceutics17111419

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