SynthesisInternational journal of pharmaceutics2023
Meta-analysis of material properties influencing nanoparticle plasma pharmacokinetics.
Synthesis in International journal of pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- From bench to bedside: Overcoming translational hurdles in nanoparticle research with pharmacokinetic modeling.Bioengineering & translational medicine · 2026Review
- Ionizable Polymeric Micelles Targeting Transferrin Receptor 1 Enhance Systemic mRNA Delivery to the Brain.Molecular pharmaceutics · 2026Article
- Cancer-associated fibroblasts: enablers of tumor drug resistance.Journal of translational medicine · 2026Review
- Whole-body distribution of threeAntimicrobial agents and chemotherapy · 2026Article
- Development of a predictive algorithm for the efficacy of half-life extension strategies.International journal of pharmaceutics · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Thorough characterization of the plasma pharmacokinetics (PK) is a critical step in clinical development of novel therapeutics and is routinely performed for small molecules and biologics. However, there is a paucity of even basic characterization of PK for nanoparticle-based drug delivery systems. This has led to untested generalizations about how nanoparticle properties govern PK. Here, we present a meta-analysis of 100 nanoparticle formulations following IV administration in mice to identify any correlations between four PK parameters derived by non-compartmental analysis (NCA) and four cardinal properties of nanoparticles: PEGylation, zeta potential, size, and material. There was a statistically significant difference between the PK of particles stratified by nanoparticle properties. However, single linear regression between these properties and PK parameters showed poor predictability (r
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