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. 2007 Feb;6(1):49-56.
doi: 10.1177/153303460700600107.

Modeling and sensitivity analysis of acoustic release of Doxorubicin from unstabilized pluronic P105 using an artificial neural network model

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Modeling and sensitivity analysis of acoustic release of Doxorubicin from unstabilized pluronic P105 using an artificial neural network model

Ghaleb A Husseini et al. Technol Cancer Res Treat. 2007 Feb.

Abstract

This paper models steady state acoustic release of Doxorubicin (Dox) from Pluronic P105 micelles using Artificial Neural Networks (ANN). Previously collected release data were compiled and used to train, validate, and test an ANN model. Sensitivity analysis was then performed on the following operating conditions: ultrasonic frequency, power density, Pluronic P105 concentration, and temperature. The model showed that drug release was most efficient at lower frequencies. The analysis also demonstrated that release increases as the power density increases. Sensitivity plots of ultrasound intensity revealed a drug release threshold of 0.015 W/cm2 and 0.38 W/cm2 at 20 and 70 kHz, respectively. The presence of a power density threshold provides strong evidence that cavitation plays an important role in acoustically activated drug release from polymeric micelles. Based on the developed model, Dox release is not a strong function of temperature, suggesting that thermal effects do not play a major role in the physical mechanism involved. Finally, sensitivity plots of P105 concentration indicated that higher release was observed at lower copolymer concentrations.

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Figures

Figure 1
Figure 1
General structure of a three layers feedforward ANN.
Figure 2
Figure 2
The SIMULINK interface for the ANN drug release model. Four input variables are used (frequency, power density, pluronic concentration, and temperature). The output variable is drug release percent.
Figure 3
Figure 3
A plot of Neural Network (NN) predictions versus experimental data. The plot shows a linear fit with an R 2 = 0.957.
Figure 4
Figure 4
Comparison of NN drug delivery model predictions with experimental data. The plot shows the model simulation output (−) and the experimental data (+).
Figure 5
Figure 5
Variation of Standard Square of Error with Neural Network training Epochs.
Figure 6
Figure 6
The drug release sensitivity plots of frequency. (Power densities = 0.2, 0.3, 0.4, and 0.5 W/cm2, Temp = 37 °C, Concentration = 10%).
Figure 7
Figure 7
The drug release sensitivity plot of power density at 20 kHz. (Temp = 37 °C, Concentration = 10%).
Figure 8
Figure 8
The drug release sensitivity plot of power density at 70 kHz. (Temp = 37 °C, Concentration = 10 %).
Figure 9
Figure 9
The drug release sensitivity plot of temperature. (Power densities = 0.8, 0.9, and 1 W/cm2, Concentration = 10%, Frequency = 70 kHz).
Figure 10
Figure 10
The drug release sensitivity plots of Pluronic P105 concentration. (Power density = 1 W/cm2, Temp = 37 °C, Frequency = 20 kHz and 90 kHz).

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References

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