Browsing by Subject "targeted drug delivery"

Now showing 1 - 3 of 3

Open Access
Analysis of the in-vitro nanoparticle-cell interactions via smoothing splines mixed effects model
(2013) Doğruöz, Elifnur
A mixed effects statistical model is developed to understand the nanoparticle(NP)- cell interactions and predict the cellular uptake rate of NPs. NP-cell interactions are crucial for targeted drug delivery systems, cell-level diagnosis, and cancer treatment. The NP cellular uptake depends on the size, charge, chemical structure, concentration of NPs, and incubation time. The vast number of combinations of those variable values disallows a comprehensive experimental study of NP-cell interactions. A mathematical model can, however, generalize the findings from some limited number of carefully designed experiments and can be used for the simulation of NP uptake rates for the alternative treatment design, planning, and comparisons. We propose a mathematical model based on the data obtained from in-vitro NPhealthy cell experiments conducted by the Nanomedicine and Advanced Technologies Research Center in Turkey. The proposed model predicts the cellular uptake rate of Silica, polymethyl methacrylate, and polylactic acid NPs given the incubation time, size, charge and concentration of NPs. This study implements the mixed model methodology in nanomedicine area for the first time and is the first mathematical model that predicts NP cellular uptake rate based on sound statistical principles. Our model provides a cost effective tool for researchers developing targeted drug delivery systems.
Open Access
Analysis of the in-vitro nanoparticle-cell interactions via support vector regression model
(2013) Akbulut, Nur Muhammed
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.
Open Access
Artificial neural networks modeling and simulation of the in-vitro nanoparticles - cell interactions
(2012) Cenk, Neslihan
In this research a prediction model for cellular uptake efficiency of nanoparticles (NPs), which is the rate of NPs adhered to the cell surface or entered into the cell, is investigated via Artificial Neural Network (ANN) method. Prediction of cellular uptake rate of NPs is an important study considering the technical limitations of volatile environment of organism and the time limitation of conducting numerous experiments for thousands of possible variations of different variables that have an impact on NP uptake rate. Moreover, this study constitutes a basis for the targeted drug delivery and cell-level detection, treatment and diagnoses of existing pathologies through simulating experimental procedure of NP-Cell interactions. Accordingly, this study will accelerate nano-medicine researches. The research focuses on constructing a proper ANN model based on multilayered feed-forward back-propagation algorithm for prediction of cellular uptake efficiency which depends on NP type, NP size, NP surface charge, concentration and time. NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA) all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing number of hidden layers, node numbers and training functions. The data sets for training and testing of the network are provided through in-vitro NP-cell interaction experiments conducted by a Nano-Medicine Research Center in Turkey. The dispersion characteristics and cell interactions of the different nanoparticles in organisms are explored through constructing and implementing an optimal prediction model using ANNs. Simulating the possible interactions of targeted nanoparticles with cells via ANN model could lead to a more rapid, more convenient and less expensive approach in comparison to numerous experimental variations.