Analysis of the in-vitro nanoparticle-cell interactions via support vector regression model
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In this research a Support Vector Regression model is developed to understand the nanoparticle (NP)-cell interactions and to predict the cellular uptake rate of the nanoparticles, which is the rate of NPs adhered to the cell surface or entered into the cell. Examination of nanoparticle-cell interaction is important for developing targeted drug delivery systems and cell-level detection and treatment of diseases. Cellular uptake rate of NPs depends on NP type, size, shape, surface charge, concentration and incubation time. Conducting numerous experiments on the combinations of those variables to understand NP-cell interaction is impractical. Hence, a mathematical model of the cellular uptake rate will therefore be useful. The data for this study are obtained from in-vitro NP-healthy cell experiments conducted by a Nano-Medicine Research Center in Turkey. The proposed support vector regression model predicts the cellular uptake rate of nanoparticles with respect to incubation time given the size, charge and concentration properties of NPs.