Citation

Owida, Hamza Abu and Arabiat, Areen and Alwidian, Jaber A. and Mashagba, Hamza A. and Vasudevan, Asokan and Abd. Aziz, Azlan and Ahmmad Hunitie, Mohammad Faleh and Mohammad, Suleiman Ibrahim (2026) Machine Learning-Based Prediction of Particle Size in PLGA Nanoparticle Formulations. Applied Mathematics & Information Sciences, 20 (5). pp. 1345-1354. ISSN 1935-0090

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Abstract

Poly lactic-co-glycolic acid (PLGA) nanoparticles’ particle size is a crucial quality parameter, but precise pre-experimental prediction is still challenging since it depends on coupled polymer, drug, and formulation variables across heterogeneous investigations. Hydrodynamic particle size was modeled in this study using a carefully selected literature-derived dataset that included 430 formulations from 59 literature references and 15 predictors. Nevertheless, the suggested model shows very little cross-study generality. Drug hydrogen-bond acceptor count (mol Hacceptors), LA/GA ratio, surfactant concentration, drug/polymer ratio, drug lipophilicity, and polymer molecular weight were consistently found to be the main predictors of particle size by interpretable machine learning (ML) analyses, with distinct non-linear and interaction-dependent behavior. A grouped external hold-out split at the literature-reference level and random 5-fold cross-validation were used to assess the performance of the proposed model. While performance declined on the external reference-level test set (RMSE = 61.56 nm, MAE = 49.63 nm, R2 = 0.18), random 5-fold cross-validation demonstrated good apparent in-distribution performance (RMSE = 49.75 nm, MAE = 26.63 nm, R2 = 0.76). These results demonstrate that study-aware external validation is necessary prior to widespread predictive implementation, but they also support the use of interpretable machine learning for formulation screening and hypothesis creation.

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Cross-Validation, poly lactic-co-glycolic acid, formulation, biomedical
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 Oct 2026 03:55
Last Modified:	28 Oct 2026 03:55
URII:	http://shdl.mmu.edu.my/id/eprint/16931

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