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dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Kaminskas, Lisa M 
Pires, Douglas EV 

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 Q2 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.

Description

Keywords

Databases, Factual, Dendrimers, Drug Delivery Systems, Internet, Nanomedicine

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

9

Publisher

Springer Science and Business Media LLC

Rights

All rights reserved
Sponsorship
Medical Research Council (MR/M026302/1)
L.M.K was funded by an NHMRC Career Development Fellowship. D.B.A. and D.E.V.P were funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1, APQ-00828-15). D.E.V.P. received support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (409780/2016-2), Brazil. DBA was supported by a C. J. Martin Research Fellowship from the National Health and Medical Research Council of Australia (APP1072476) and the Jack Brockhoff Foundation (JBF 4186, 2016). This work was supported in part by the Victorian Government's OIS Program.