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Modeling the cellular uptake of magnetofluorescent nanoparticles in pancreatic cancer cells: a quantitative structure activity relationship study
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2012 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 51, no 32, 10712-10718 p.Article in journal (Refereed) Published
Abstract [en]

An artificial neural network was employed to predict the cellular uptake of 109 magnetofluorescent nanoparticles (NPs) in pancreatic cancer cells on the basis of quantitative structure activity relationship method. Six descriptors chosen by combining self organizing map and stepwise multiple linear regression (MLR) techniques were used to correlate the nanostructure of the studied particles with their bioactivity using MLR and multilayered perceptron neural network (MLP-NN) modeling techniques. For the MLR and MLP-NN models, the correlation coefficient was 0.769 and 0.934, and the root-mean-square error was 0.364 and 0.150, respectively. The results obtained after a leave-many-out cross-validation test revealed the credibility of MLP-NN for the prediction of cellular uptake of NPs. In addition, sensitivity analysis of MLP-NN model indicated that the number of hydrogen-bond donor sites in the organic coating of a NP is the predominant factor responsible for cellular uptake.

Place, publisher, year, edition, pages
2012. Vol. 51, no 32, 10712-10718 p.
National Category
Chemical Engineering
Identifiers
URN: urn:nbn:se:umu:diva-59522DOI: 10.1021/ie3006947ISI: 000307488400021OAI: oai:DiVA.org:umu-59522DiVA: diva2:552958
Available from: 2012-09-17 Created: 2012-09-17 Last updated: 2017-12-07Bibliographically approved

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Ghorbanzadeh, MehdiKarimpour, Masoumeh
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