IFE.PTML: advancing neurotherapeutic nanoparticle release-system design for enhanced brain delivery

Abstract Neurodegenerative diseases (NDDs) are a major global health concern, exacerbated by the blood–brain barrier (BBB), which limits effective drug delivery to the brain. Designing nanoparticles (NPs) to cross the BBB and deliver drugs could greatly improve the treatment of NDDs; however, current approaches to designing materials for drug delivery are generally slow. This study introduces an approach integrating information fusion, Python encoding, perturbation theory, and machine learning to predict the behaviour of NPs as drug delivery systems. Using an extensive pharmacokinetic database, random forest, extreme gradient boosting, and decision tree algorithms are trained and optimised. The random forest model achieves accuracies of 95.1% and 89.7% on training and testing data, respectively. As a proof of concept for the model’s utility, four novel Fe 3 O 4 NP systems are synthesised via thermal decomposition and functionalised for water solubility. Structural, morphological, and magnetic characterisations are performed. The ML model is then applied to these synthesised NPs, with predictions identifying PMAO-coated NPs as promising candidates for BBB and neuronal cell lines. This design framework offers significant advancements in the efficient identification of promising potential NP candidates for NDDs drug delivery, enabling predictions of desirable bioactivity profiles.