ArticleScientific reports2019
dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Progress in the Application of Nanomaterials in Tumor Treatment.Biomedicines · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Magnetic Nanoparticles and Drug Delivery Systems for Anti-Cancer Applications: A Review.Nanomaterials (Basel, Switzerland) · 2025Review
- piscesCSM: prediction of anticancer synergistic drug combinations.Journal of cheminformatics · 2024Article
- Data-Driven Approaches to Predict Dendrimer Cytotoxicity.ACS omega · 2024Article
- Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications.ACS infectious diseases · 2024Review
- AI-Driven Enhancements in Drug Screening and Optimization.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence.Biomedicines · 2023Review
- Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023Article
- kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors.Protein science : a publication of the Protein Society · 2022Article
- Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives.Nanomaterials (Basel, Switzerland) · 2022Review
- Investigation of the Associations between a Nanomaterial's Microrheology and Toxicology.ACS omega · 2022Article
- cropCSM: designing safe and potent herbicides with graph-based signatures.Briefings in bioinformatics · 2022Article
- Nanomaterial-based drug delivery systems as promising carriers for patients with COVID-19.RSC advances · 2021Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.Journal of chemical information and modeling · 2021Article
- mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.Nucleic acids research · 2021Article
- pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.Bioinformatics advances · 2021Article
- DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations.Protein science : a publication of the Protein Society · 2021Article
- The Impact of Polymer Size and Cleavability on the Intravenous Pharmacokinetics of PEG-Based Hyperbranched Polymers in Rats.Nanomaterials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Nanomedicine development currently suffers from a lack of efficient tools to predict pharmacokinetic behavior without relying upon testing in large numbers of animals, impacting success rates and development costs. This work presents dendPoint, the first in silico model to predict the intravenous pharmacokinetics of dendrimers, a commonly explored drug vector, based on physicochemical properties. We have manually curated the largest relational database of dendrimer pharmacokinetic parameters and their structural/physicochemical properties. This was used to develop a machine learning-based model capable of accurately predicting pharmacokinetic parameters, including half-life, clearance, volume of distribution and dose recovered in the liver and urine. dendPoint successfully predicts dendrimer pharmacokinetic properties, achieving correlations of up to r = 0.83 and Q
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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.