Modeling the Cellular Uptake of Magnetofluorescent Nanoparticles in Pancreatic Cancer Cells: A Quantitative Structure Activity Relationship Study

Abstract

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.