Acta pharmaceutica Sinica. BFeb 13, 2026

Using Machine Learning to Design, Improve, and Study Tiny Particle Drug Delivery Systems: Current Uses, Challenges, and Future Outlook

mRNA Technology Weekly Brief ↗PubMed ↗DOI ↗

Updated Feb 14, 2026

Abstract

Over 90 nanomedicines have been approved, showcasing the impact of nanoparticulate drug delivery systems (NDDS).

Nanoparticulate drug delivery systems improve drug targeting, bioavailability, and therapeutic efficacy.
The formulation design and optimization of NDDS remain complex and challenging.
Artificial intelligence, particularly machine learning, offers predictive capabilities for NDDS development.
Machine learning applications are summarized across various NDDS formulations, including nanoparticles and liposomes.
ML algorithms may help predict critical quality attributes such as particle size, drug encapsulation efficiency, and stability.

Simplified

Nanoparticulate drug delivery systems (NDDS) have revolutionized modern medicine by significantly improving drug targeting, bioavailability, and therapeutic efficacy. Despite the clinical success of over 90 approved nanomedicines, the development of NDDS remains challenging due to the complexity of formulation design, optimization, and characterization processes. Artificial intelligence, particularly machine learning (ML), offers powerful data analytics and predictive capabilities that can address these challenges. This review systematically summarizes recent advances in ML applications across various NDDS formulations, including polymeric nanoparticles, lipid nanoparticles, liposomes, solid lipid nanoparticles, nanostructured lipid carriers, nanoemulsions, nanosuspensions, lipid-based hybrid NDDS, self-emulsifying drug delivery systems, niosomes, and nanocrystals. We also summarize how ML algorithms could help predict critical quality attributes of NDDS, such as particle size, shape, surface properties, drug encapsulation efficiency, drug loading efficiency, drug release behavior, and stability. Furthermore, we discuss existing challenges and prospects for the formulation development empowered by ML in NDDS. In conclusion, this review provides a comprehensive overview of the transformative potential of ML in improving the formulation development of nanomedicines, ultimately accelerating their clinical translation.

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Competing interests

The authors have no conflicts of interest to declare.

PubMed

Using Machine Learning to Design, Improve, and Study Tiny Particle Drug Delivery Systems: Current Uses, Challenges, and Future Outlook

Abstract

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