ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
High-Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Overcoming the Druggability Hurdles of Celastrol: A Critical Review of Advanced Drug Delivery Strategies.Biomolecules · 2026Review
- The in vivo revolution in CAR-T therapy medicinal products: challenges and regulatory prospects.Signal transduction and targeted therapy · 2026Review
- Trojan Horse Strategy: How Biomimetic Nanomedicine Remodels the Tumor Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Resveratrol Nanoformulations for Cancer Management: A Comprehensive Review of Disease-Specific Strategies and Clinical Translational Barriers.International journal of nanomedicine · 2026Review
- Engineering design of platelet-mimicking therapeutic systems: multilevel biomimicry, gating strategies, and translational boundaries.Frontiers in bioengineering and biotechnology · 2026Review
- High-Throughput Strategies for Streamlining Lipid Nanoparticle Development Pipeline.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lipid nanoparticles (LNPs) have become clinically validated nanocarriers for nucleic acid delivery, enabling applications in mRNA vaccines and therapies for cancer, ocular, and infectious diseases. Identifying LNPs formulations with optimal physicochemical and pharmacokinetic properties using traditional low-throughput methods is resource-intensive and impractical for evaluating large libraries. Recent advances in automation, high-throughput platforms for lipid synthesis, characterization, and screening tools are transforming the landscape of LNP formulation. These strategies enable rapid multi-parametric generation and evaluation of hundreds to thousands of formulations across key properties such as size, charge, stability, biodistribution, cellular uptake, and intracellular trafficking. In parallel, advanced biomimetic models and in vivo multiplexed barcoding screening strategies provide deeper insights into tissue targeting and therapeutic delivery outcomes. This review provides an integrated framework that combines automation with high-throughput combinatorial synthesis, characterization, and in vitro/in vivo screening tools. In this development pipeline, performance benchmarks applied at each step systematically exclude suboptimal candidates, ensuring that only clinically viable LNP candidates advance. Future directions, including automation, high-throughput, and closed-loop machine learning guided design strategies, are further discussed to advance the development of next-generation LNP therapeutics and accelerate their translation from bench to bedside.
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.