DendPoint: a Web Resource for Dendrimer Pharmacokinetics Investigation and Prediction

Overview

Journal Sci Rep

Specialty Science

Date 2019 Oct 31

PMID 31664080

Citations 19

Authors

Lisa M Kaminskas

Douglas E V Pires

David B Ascher

Affiliations

Soon will be listed here.

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 up to 0.68. dendPoint is freely available as a user-friendly web-service and database at http://biosig.unimelb.edu.au/dendpoint . This platform is ultimately expected to be used to guide dendrimer construct design and refinement prior to embarking on more time consuming and expensive in vivo testing.

Citing Articles

Magnetic Nanoparticles and Drug Delivery Systems for Anti-Cancer Applications: A Review.

Graham W, Torbett-Dougherty M, Islam A, Soleimani S, Bruce-Tagoe T, Johnson J Nanomaterials (Basel). 2025; 15(4).

PMID: 39997849 PMC: 11858650. DOI: 10.3390/nano15040285.

piscesCSM: prediction of anticancer synergistic drug combinations.

AlJarf R, Rodrigues C, Myung Y, Pires D, Ascher D J Cheminform. 2024; 16(1):81.

PMID: 39030592 PMC: 11264925. DOI: 10.1186/s13321-024-00859-4.

Data-Driven Approaches to Predict Dendrimer Cytotoxicity.

Maity T, Balachandran A, Krishnamurthy L, Nagar K, Upadhyayula R, Sengupta S ACS Omega. 2024; 9(23):24899-24906.

PMID: 38882163 PMC: 11173563. DOI: 10.1021/acsomega.4c01775.

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications.

Paul S, Verma S, Chen Y ACS Infect Dis. 2024; 10(4):1034-1055.

PMID: 38428037 PMC: 11019562. DOI: 10.1021/acsinfecdis.3c00624.

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Serghini A, Portelli S, Ascher D Methods Mol Biol. 2023; 2714:269-294.

PMID: 37676604 DOI: 10.1007/978-1-0716-3441-7_15.

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