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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles

Year: 2025

Type: article

Abstract: This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component analysis, Gaussian process regression, and artificial neural networks. The focus is to understand the effect of drug solubility, drug molecular weight, particle size, and pH-value of the release matrix/environment on drug release profiles. The results obtained from machine learning is then used as guidelines for designing new in vitro experiments to examine dependence of drug release profiles on those four factors. It is interesting to see that indeed the results of the new in vitro experiments are in basic agreement with the results obtained from machine learning.

Source: Scientific Reports

DOI: 10.1038/s41598-024-82728-6

Authors: Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

Institution: Colorado State University

Language: English

Topic: Nanoparticle-Based Drug Delivery

Subfield: Biomaterials

Field: Materials Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690