Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles.

ABSTRACT: Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained an

SUBMITTER: Rezvantalab S 

PROVIDER: S-EPMC10784499 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature