ReviewAAPS PharmSciTech2014
Nanoparticle ligand presentation for targeting solid tumors.
Review in AAPS PharmSciTech, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Polymer Drug Conjugate: A Revolution in Drug Delivery.AAPS PharmSciTech · 2026Review
- Mechanism and applications of magnetoelectric nanoparticles in cancer therapy.Nanomedicine (London, England) · 2025Review
- Breaking barriers: Smart vaccine platforms for cancer immunomodulation.Cancer communications (London, England) · 2025Review
- Exploring Advanced CRISPR Delivery Technologies for Therapeutic Genome Editing.Small science · 2024Article
- Design of Chitosan-Coated, Quercetin-Loaded PLGA Nanoparticles for Enhanced PSMA-Specific Activity on LnCap Prostate Cancer Cells.Biomedicines · 2023Article
- Matrices and Affinity Ligands for Antibody Purification and Corresponding Applications in Radiotherapy.Biomolecules · 2022Review
- Review
- Solid-phase supported design of carriers for therapeutic nucleic acid delivery.Bioscience reports · 2017Review
- The Weak Link: Optimization of the Ligand-Nanoparticle Interface To Enhance Cancer Cell Targeting by Polymer Micelles.Nano letters · 2017Article
- Clinical cancer research : an official journal of the American Association for Cancer Research · 2017Article
- Translational application of nano delivery systems: emerging cancer therapy.AAPS PharmSciTech · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among the many scientific advances to come from the study of nanoscience, the development of ligand-targeted nanoparticles to eliminate solid tumors is predicted to have a major impact on human health. There are many reports describing novel designs and testing of targeted nanoparticles to treat cancer. While the principles of the technology are well demonstrated in controlled lab experiments, there are still many hurdles to overcome for the science to mature into truly efficacious targeted nanoparticles that join the arsenal of agents currently used to treat cancer in humans. One of these hurdles is overcoming unwanted biodistribution to the liver while maximizing delivery to the tumor. This almost certainly requires advances in both nanoparticle stealth technology and targeting. Currently, it continues to be a challenge to control the loading of ligands onto polyethylene glycol (PEG) to achieve maximal targeting. Nanoparticle cellular uptake and subcellular targeting of genes and siRNA also remain a challenge. This review examines the types of ligands that have been most often used to target nanoparticles to solid tumors. As the science matures over the coming decade, careful control over ligand presentation on nanoparticles of precise size, shape, and charge will likely play a major role in achieving success.
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.