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ORIGINAL RESEARCH
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Interpretable machine learning-driven optimization of physicochemical properties in hydrogel scaffolds to promote macrophage polarization

Zhaopu Han1,2,3# ,  Yangguang Huang4# ,  Zhibao Chen1,2,3# ,  Tingting Tan5 ,  Yucai Li1,2,3 ,  Yujie Chen1,2,3 ,  Linfeng Xu1,2,3 ,  Pengfei Xia1,2,3* ,  Xiaojian Ye1,2,3*
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1 Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
2 Center for Spinal Minimally Invasive Research, Shanghai Jiao Tong University, Shanghai, China
3 Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
4 Department of Pharmacy, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
5 Department of Critical Care Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
BMT, 00088 https://doi.org/10.12336/bmt.24.00088
Submitted: 19 December 2024 | Revised: 25 March 2025 | Accepted: 3 April 2025 | Published: 8 May 2025
Copyright © 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial–ShareAlike 4.0 License.
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Abstract

The physicochemical properties of hydrogel scaffolds, including storage modulus, loss modulus, swelling ratio, porosity, pore size, and roughness, are capable of promoting macrophage polarization into anti-inflammatory phenotype (M2) to accelerate tissue repair. However, most current studies focus on the effects of individual properties on M2 polarization, examining each in isolation. Enhancing the synergistic effects of multiple physicochemical properties is a challenge. In this work, a novel strategy called interpretable machine learning-driven optimization of physicochemical properties (IML-OPP) is proposed to address this challenge. In the IML-OPP strategy, the optimal value of each physicochemical property was sequentially determined based on its ranked importance. First, an initial value was identified for each property by maximizing the individual effect to promote M2 polarization. Then, these initial values were optimized based on their interactive effects. Once all the optimal values were determined, an optimized combination of physicochemical properties was designed to construct a hydrogel scaffold optimized for promoting M2 polarization. To assess the robustness and universality of the IML-OPP strategy, three optimized combinations of physicochemical properties were generated and evaluated. These results offer theoretical guidance for designing hydrogel scaffolds aimed at promoting M2 polarization.

Keywords
Hydrogel scaffolds
Machine learning
Macrophage polarization
Physicochemical properties
Funding
This work was supported by the National Natural Science Foundation of China (No. 52003149), the Natural Science Foundation of Shanghai (No. 24ZR1464000), the Funding of Shanghai Science and Technology Commission Project (No. 22Y11912000), the Funding of Orthopedic Priority Speciality of Changning District (No. 20231002), the Funding of Minimally Invasive Spine Surgery Research Center of Shanghai Jiao Tong University (No. 2021JCPT03), the Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery (Tongren Hospital, Shanghai Jiao Tong University School of Medicine) (No. 2024JZWC-ZDA02), and the Star-up Funds for Talent Introduction (Tongren Hospital, Shanghai Jiao Tong University School of Medicine) (No. TR2024RC17).
Conflict of interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this work.
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