Machine learning-assisted microfluidic approach for broad-spectrum liposome size control.
10.1016/j.jpha.2025.101221
- Author:
Yujie JIA
1
;
Xiao LIANG
1
;
Li ZHANG
2
;
Jun ZHANG
1
;
Hajra ZAFAR
1
;
Shan HUANG
1
;
Yi SHI
3
;
Jian CHEN
1
;
Qi SHEN
1
Author Information
1. Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
2. Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai, 200240, China.
3. Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Publication Type:Journal Article
- Keywords:
Liposomal size;
Liposomes;
Machine learning;
Microfluidics;
SHM
- From:
Journal of Pharmaceutical Analysis
2025;15(6):101221-101221
- CountryChina
- Language:English
-
Abstract:
Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.
