Journal article

dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Lisa M Kaminskas, Douglas EV Pires, David B Ascher

Scientific Reports | Nature Publishing Group | Published : 2019

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, volum..

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Grants

Awarded by Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)


Awarded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil


Awarded by C.J. Martin Research Fellowship from the National Health and Medical Research Council of Australia


Awarded by Jack Brockhoff Foundation


Funding Acknowledgements

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 Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1, APQ-00828-15). D.E.V.P. received support from Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (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.