IFE.PTML: Advancing Neurotherapeutic Nanoparticle Release-System Design for Enhanced Brain Delivery

Neurodegenerative diseases are a major global health concern, exacerbated by the blood-brain barrier, which limits effective drug delivery to the brain. Designing nanoparticles to cross the blood-brain barrier and deliver drugs could greatly improve the treatment of neurodegenerative diseases; 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 nanoparticles 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 nanoparticle 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 nanoparticles, with predictions identifying PMAO-coated nanoparticles as promising candidates for BBB and neuronal cell lines. This design framework offers significant advancements in the efficient identification of promising potential nanoparticle candidates for neurodegenerative diseases drug delivery, enabling predictions of desirable bioactivity profiles.