ReviewActa pharmaceutica Sinica. B2026
Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.
Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Polydatin Delivery Systems and Nanomedicine: Pharmacology, Preclinical Evidence, and Translation.Pharmaceutics · 2026Review
- Review
- Nanoparticulate and Hydrogel Vehicles for Stimuli-Responsive and Sustained Controlled Release of Active Pharmaceutical Ingredients.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Lipid Nanoparticles for Gene Therapy: Unresolved Challenges in Manufacturing, Transdermal Delivery, Machine Learning, Endosomal Escape, and the Protein Corona.Pharmaceutics · 2026Review
- Engineering the Future of Precision Medicine: A Comprehensive Guide to RNA Therapeutics.Current issues in molecular biology · 2026Review
- Engineering cuproptosis with nanomedicine: Design, combination therapy, and translation in cancer.Materials today. Bio · 2026Review
- Application of Temporally Controlled Release Systems in Periodontal Tissue Regeneration: From Material Design to Therapeutic Strategies.Pharmaceutics · 2026Review
- AI-Assisted Engineering of Glycyrrhizic Acid/Simvastatin Nanocrystals for Multifunctional Treatment of Bacterial Osteomyelitis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Smart nanoparticle vaccines integrate nanotechnology artificial intelligence and immunoengineering for precision immunization.Discover nano · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- AI-Driven Design and Comparative Evaluation of SNEDDS for the Optimized Nanoencapsulation of Phytoextracts.Nanomaterials (Basel, Switzerland) · 2026Article
- Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems.Nanomaterials (Basel, Switzerland) · 2026Review
- Co-Loaded PEGylated Nanoliposomes of Bendamustine and Rutin: Formulation, Release Kinetics, and a Hybrid Predictive Modeling Framework.Pharmaceutics · 2026Article
- The Role and Potential of Nanotechnology in Improving Solubility and Enhancing Bioavailability.Pharmaceutics · 2026Review
- Artificial intelligence-enabled cross-scale integration of traditional Chinese medicine and biomedicine for sepsis: from mechanisms to delivery.Frontiers in pharmacology · 2026Review
- Advances in Biomimetic Cell Membrane Nanoplatforms for Renal-Targeted Theranostics: From Pathophysiological Basis to Membrane-Stratified Design.International journal of nanomedicine · 2026Review
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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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.