ArticleACS nano2024
Machine Learning Elucidates Design Features of Plasmid Deoxyribonucleic Acid Lipid Nanoparticles for Cell Type-Preferential Transfection.
Article in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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.
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Who cites it
27 citing papers in PubMed.
- Cytokine co-presentation on targeted lipid nanoparticles enhances in vivo T cell engineering.Biomaterials · 2027Article
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Breaking Biological Barriers: Engineering Nanocarriers for Efficient Drug and Gene Delivery through Nano-Bio Interfaces and Biophysical Design.Applied physics reviews · 2026Article
- Lipid Nanoparticles for Gene Therapy: Unresolved Challenges in Manufacturing, Transdermal Delivery, Machine Learning, Endosomal Escape, and the Protein Corona.Pharmaceutics · 2026Review
- Nanoengineering Systems for Gene Therapy: Mechanisms, Modalities, and Future Directions.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Nanopharmaceutical Development: From Predictive Design to Clinical Translation.Pharmaceutics · 2026Review
- Functional Reclassification of Lipid-Based Drug Delivery Systems and Advances in Formulation Strategies and Manufacturing Challenges.AAPS PharmSciTech · 2026Review
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Tramadol use is associated with reduced 28-day mortality in ICU patients after cardiac surgery: a retrospective study based on the MIMIC-IV database.Frontiers in pharmacology · 2026Article
- Development and external validation of a composite biomarker-based machine learning model for sarcopenia risk stratification in patients with cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Advancements in Nanomedicine for Precision Management of Lymphoma: Mechanisms, Diagnostics, and Therapeutic Strategies.International journal of nanomedicine · 2026Review
- In vivo CAR-T cell therapy: New breakthroughs for cell-based tumor immunotherapy.Human vaccines & immunotherapeutics · 2025Review
- A multistep platform identifies spleen-tropic lipid nanoparticles for in vivo T cell-targeted delivery of gene-editing proteins.Science advances · 2025Article
- Designing lipid nanoparticles using a transformer-based neural network.Nature nanotechnology · 2025Article
- Article
- Emerging Approaches for the Discovery of Lipid-Based RNA Delivery Systems.Pharmaceutics · 2025Review
- Microfluidic and Turbulent Mixing for mRNA LNP Vaccines.Pharmaceutics · 2025Review
- Immunomodulatory Delivery Materials for Tissue Repair.Advanced healthcare materials · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Machine learning techniques for lipid nanoparticle formulation.Nano convergence · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
To broaden the accessibility of cell and gene therapies, it is essential to develop and optimize nonviral, cell type-preferential gene carriers such as lipid nanoparticles (LNPs). While high-throughput screening (HTS) approaches have proven effective in accelerating LNP discovery, they are often costly, labor-intensive, and do not consistently yield actionable design rules that direct screening efforts toward the most relevant chemical and formulation parameters. In this study, we employed a machine learning (ML) workflow, utilizing well-curated plasmid DNA LNP transfection data sets across six cell types, to extract compositional and chemical insights from HTS studies. Our approach achieved prediction errors averaging between 5 and 10%, depending on the cell type. By applying SHapley Additive exPlanations to our ML models, we uncovered key composition-function relationships that govern cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance
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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.