Generalized linear models for in-vitro nanoparticle-cell interactions
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Nanomedicine techniques are quite promising in terms of treatment and detection of cancerous cells. Targeted drug delivery plays an important role in this field of cancer nanotechnology. A lot of studies have been conducted so far concerning nanoparticle (NP)-cell interaction. Most of them fail to propose a mathematical model for a quantitative prediction of cellular uptake rate with measurable accuracy. In this thesis, we investigate cell-NP interactions and propose statistical models to predict cellular uptake rate. Size, surface charge, chemical structure (type), concentration of NPs and incubation time are known to affect the cellular uptake rate. Generalized linear models are employed to explain the change in uptake rate with the consideration of those effects and their interactions. The data set was obtained from in-vitro NP-healthy cell experiments conducted by the Nanomedicine & Advanced Technologies Research Center in Turkey. Statistical models predicting cellular uptake rate are proposed for sphere-shaped Silica, polymethyl methacrylate (PMMA), and polylactic acid (PLA) NPs.
generalized linear models