Predicting Nanoparticle Drug Delivery to the Brain with Machine Learning
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.
In our study published in Molecular Pharmaceutics (American Chemical Society), “A comprehensive study on nanoparticle drug delivery to the brain: application of machine learning techniques”, we addressed the limitations of previous research by using various predictive models based on the collection of large data sets of 403 data points incorporating both numerical and categorical features.
1. Literature Data Analysis & Physicochemical Properties
- Predictive Modeling: Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain.
- Pharmacodynamic Analysis: The physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve ($\text{AUC}_{\text{plasma}}$).
- Administration Routes: Evaluated delivery kinetics across both the intranasal (IN) and intravenous (IV) routes.
2. Linear Mixed-Effects Models (LMEMs)
The analysis employed various linear models, with a particular emphasis on Linear Mixed-Effects Models (LMEMs):
- Exceptional Accuracy: LMEMs demonstrated exceptional accuracy and exhibited superior performance in capturing underlying patterns among the various modeling approaches.
- Accounting for Variations: Effectively separated global physicochemical parameters from study-specific variations.
3. Key Findings & Experimental Validation
- Negative Impact of Release Rate: Factors such as the release rate had a negative impact on brain targeting.
- Negative Impact of Molecular Weight: Factors such as molecular weight had a negative impact on brain targeting.
- P-gp Substrate Effect: The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.
- Experimental in Vivo Validation: The model was validated via the laboratory preparation and administration of two distinct NP formulations via the intranasal and intravenous routes.
4. Paper & Code Repository
- 📄 Published Paper: ACS Molecular Pharmaceutics (DOI: 10.1021/acs.molpharmaceut.3c00880)
- 💻 Code Repository: github.com/Introvertuoso/BrainTargeting
@article{Yousfan_2023,
author = {Yousfan, Amal and Al Rahwanji, Mhd Jawad and Hanano, Abdulsamie and Al-Obaidi, Hisham},
title = {A comprehensive study on nanoparticle drug delivery to the brain: application of machine learning techniques},
journal = {Molecular Pharmaceutics},
volume = {21},
number = {1},
pages = {333--345},
year = {2023},
publisher = {American Chemical Society},
doi = {10.1021/acs.molpharmaceut.3c00880}
}