ArticleMolecular pharmaceutics2024
A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques.
Article in Molecular pharmaceutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Hypothalamic wars: the last nanodelivery.Reviews in endocrine & metabolic disorders · 2026Review
- Plant Lipid Droplets and Derived Lipidic Nano-Assemblies: Structure, Biogenesis and Pharmaceutical Applications.Plant biotechnology journal · 2026Review
- Nanoparticles-enhanced CAR-T cell therapy: current advances and future directions.Biomarker research · 2026Review
- Utilizing nanotechnology to diagnose and treat central nervous system disorders.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Harnessing nanomedicine to target NF-κB signalling in cancer: at the intersection of inflammatory signalling, metabolic reprogramming, and therapeutic innovation.Cell communication and signaling : CCS · 2026Review
- Recent advances in tumor-activated prodrug nanoparticles for cancer immunotherapy.Frontiers in immunology · 2026Review
- Emerging neuroprotective mechanisms and therapeutic potential of natural polysaccharides in Alzheimer's disease.Frontiers in aging neuroscience · 2026Review
- Machine Learning-Guided Prediction of Formulation Performance in Inhalable Ciprofloxacin-Bile Acid Dispersions with Antimicrobial and Toxicity Evaluation.Molecular pharmaceutics · 2025Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Alectinib-Loaded Chitosan-Alginate Nanoparticles: A Novel Synthesis Method with In Vitro and In Vivo Evaluations.Pharmaceutics · 2025Article
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Innovative Microencapsulation of Polymyxin B for Enhanced Antimicrobial Efficacy via Coated Spray Drying.Molecular pharmaceutics · 2025Article
- Smart CAR-T Nanosymbionts: archetypes and proto-models.Frontiers in immunology · 2025Review
- Emerging Promise of Green Synthesized Metallic Nanoparticles for the Management of Neurological Disorders.Current pharmaceutical design · 2025Review
- Unilateral Administration of Surface-Modified G1 and G4 PAMAM Dendrimers in Healthy Mice to Assess Dendrimer Migration in the Brain.ACS applied materials & interfaces · 2024Article
- Drug Loading in Chitosan-Based Nanoparticles.Pharmaceutics · 2024Review
- Nanoparticles crossing blood-brain barrier need specific design for normal, neurodegenerative or cancerous brain conditions.Nanomedicine (London, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.