SynthesisACS nano2023
Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.
Synthesis in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed.
- Decoding miRNA‑146a: Mechanisms of action in cardiovascular diseases and endocrine metabolic disorders (Review).International journal of molecular medicine · 2026Review
- Surface-Engineered Phytochemical Nanomedicines for Postmenopausal Osteoporosis: Skeletal Targeting, Imaging-Supported Evaluation, and Therapeutic Performance.Pharmaceutics · 2026Review
- Paclitaxel Nanomedicines: Molecular Mechanisms of Drug Resistance, Tumor Microenvironment-Responsive Delivery, and Translational Challenges.International journal of molecular sciences · 2026Review
- Analysis of Pharmacokinetic-Pharmacodynamic Relationships of Nanoparticles against Tumors.ACS nano · 2026Article
- IntratumoralACS omega · 2026Article
- Systematic evaluation of magnetic particle imaging quantification strategies in biologically relevant scenarios using anatomically correct 3D printed mouse phantoms.Physics in medicine and biology · 2026Article
- Phase change nanodroplets: Challenges and clinical potential.Acta pharmaceutica Sinica. B · 2026Review
- Article
- Are similar nanoforms comparable in biodistribution? Data-driven approaches for optimizing nanomedicine design.Materials today. Nano · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Nanotechnology in Cancer Therapy: How Nanoparticles Are Shaping the Future of Personalized Treatment.ACS nano medicine · 2026Review
- Engineering Bilosomal Nanocarriers for Targeted Delivery of Resveratrol: Rational Design, Radiotracking Insights and Cytotoxic Assessment.AAPS PharmSciTech · 2026Article
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Smart Nanoparticles Are Not Smart Enough (Yet): A Cell-Aware View of Cancer Nanomedicine.Cells · 2026Article
- Bienzyme-powered nanorobots with ultrasensitive chemotaxis for precision cancer therapy.National science review · 2026Article
- Cancer-associated fibroblasts: enablers of tumor drug resistance.Journal of translational medicine · 2026Review
- Impeding the NHEJ Pathway for Overcoming Radioresistance in the Context of Precision Radiotherapy of Cancer.Pharmaceutics · 2026Review
- Tumor agnostic drug delivery with dynamic nanohydrogels.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.
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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.