ArticleScientific reports2025
Intelligence prediction of microfluidically prepared nanoparticles.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Data-driven and mechanistic modelling of PLGA nanoparticle formation in hydrodynamic flow focusing microfluidics.Lab on a chip · 2026Article
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
- Overcoming barriers: mechanisms and strategies of nanoparticles in overcoming the blood-brain barrier and drug resistance in glioblastomas.Journal of the Egyptian National Cancer Institute · 2026Review
- Nanostructured Lipid Carriers Enhance Ciprofloxacin Antibacterial Activity Through Diffusion-Controlled Release and Modulation of Bacterial Growth Kinetics.Pharmaceutics · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing poly(lactic-co-glycolic) acid (PLGA) nanoparticles with optimized drug encapsulation and loading is crucial for effective drug delivery. However, controlling the physicochemical properties of these nanoparticles remains challenging. In this study, we compiled a dataset of over 300 PLGA nanoparticle formulations from the literature, including 25 key features related to their preparation on microfluidic platforms. We applied various machine learning algorithms to predict encapsulation efficiency (EE) and drug loading (DL). The random forest model showed the best performance, achieving R² values of 0.93 and 0.96 for DL and EE predictions, respectively. Although EE and DL had minimal impact on each other's prediction, they provide distinct insights into nanoparticle formulation. Our results demonstrate that machine learning can effectively guide the design of drug delivery systems with desired properties, accelerating their development.
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Registered trials
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