Browsing by Subject "Targeted drug delivery"

Now showing 1 - 3 of 3

Open Access
Analysis of the in vitro nanoparticle–cell interactions via a smoothing-splines mixed-effects model
(Taylor and Francis, 2016) Dogruoz, E.; Dayanik, S.; Budak, G.; Sabuncuoglu, I.
A mixed-effects statistical model has been developed to understand the nanoparticle (NP)–cell interactions and predict the rate of cellular uptake of NPs. NP–cell interactions are crucial for targeted drug delivery systems, cell-level diagnosis, and cancer treatment. The cellular uptake of NPs depends on the size, charge, chemical structure, and concentration of NPs, and the incubation time. The vast number of combinations of these variable values disallows a comprehensive experimental study of NP–cell interactions. A mathematical model can, however, generalize the findings from a limited number of carefully designed experiments and can be used for the simulation of NP uptake rates, to design, plan, and compare alternative treatment options. We propose a mathematical model based on the data obtained from in vitro interactions of NP–healthy cells, through experiments conducted at 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 the field of nanomedicine for the first time, and is the first mathematical model that predicts the rate of cellular uptake of NPs based on sound statistical principles. Our model provides a cost-effective tool for researchers developing targeted drug delivery systems.
Open Access
Artificial neural network modeling and simulation of in-vitro nanoparticle-cell interactions
(American Scientific Publishers, 2014) Cenk, N.; Budak, G.; Dayanik, S.; Sabuncuoglu, I.
In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The 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 a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.
Open Access
Study of encapsulated microbubble cluster based on association schemes perspective
(Elsevier, 2018) Behnia, S.; Yahyavi, Mohammad; Habibpourbisafar, R.; Mottaghi, F.
Ultrasound contrast agents have been recently utilized in therapeutical implementations for targeted delivery of pharmaceutical substances. Radial pulsations of a cluster of encapsulated microbubbles under the action of an ultrasound field are complex and highly nonlinear, particularly for drug and gene delivery applications with high acoustic pressure amplitudes. In this paper, based on Qin-Ferrara’s model (Qin and Ferrara, 2010), the complete synchronization and cluster formation in targeted microbubbles network are studied. Also, association schemes as a novel approach are suggested for finding a relationship between coupled microbubbles elements which are immersed in blood or surrounding soft tissue. A significant advantage of this method is that the stability of the synchronized state (or symmetric eigenmode of mutual bubble oscillation) with respect to another state (another eigenmode) can now predict. More interestingly, we find a significant relationship between an isolated and multiple microbubbles. The results show that the problem of studying the dynamics of encapsulated microbubble cluster at synchronization state is dependent on the dynamical characteristics of isolated cases, shell thickness, density. Also, the distance between microbubbles has an important role in their synchronous modes.