A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells

Abstract

Nanotechnology has led to the development of new nanomaterials with unique properties and a wide variety of applications. In the present study, we focused on the cellular uptake of a group of nanoparticles with a single metal core by pancreatic cancer cells, which has been studied by Yap et al. (Rsc Advances, 2012, 2 (2):8489-8496) using classification models. In this work, the development of a further Quantitative Nanostructure-Activity Relationship (QNAR) model was performed by linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN) techniques to accurately predict the cellular uptake values of these compounds by dividing them into three groups. Judging from the attained statistical results, our derived QNAR models have an acceptable overall accuracy and robustness, as well as good predictivity on the external data sets. Moreover, the results of this study provide some insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying predictive computational models for biological responses caused by exposure to nanomaterials.

Keywords: Multiple linear regression method (MLR); Nanomaterials; Quantitative Nanostructure–Activity relationship (QNAR); Radial basis function neural network (RBFNN).