A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques

The delivery of drugs to specific target tissues and
cells in the
brain poses a significant challenge in brain therapeutics, primarily
due to limited understanding of how nanoparticle (NP) properties influence
drug biodistribution and off-target organ accumulation. This study
addresses the limitations of previous research by using various predictive
models based on collection of large data sets of 403 data points incorporating
both numerical and categorical features. Machine learning techniques
and comprehensive literature data analysis were used to develop models
for predicting NP delivery to the brain. Furthermore, the physicochemical
properties of loaded drugs and NPs were analyzed through a systematic
analysis of pharmacodynamic parameters such as plasma area under the
curve. The analysis employed various linear models, with a particular
emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional
accuracy. The model was validated via the preparation and administration
of two distinct NP formulations via the intranasal and intravenous
routes. Among the various modeling approaches, LMEMs exhibited superior
performance in capturing underlying patterns. Factors such as the
release rate and molecular weight had a negative impact on brain targeting.
The model also suggests a slightly positive impact on brain targeting
when the drug is a P-glycoprotein substrate.