<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The World Health Organization (WHO, 2019) reports that schizophrenia affects approximately 20 million people worldwide, and the annual number of new cases is estimated at 1.5%/10,000 people. As a result, there is a demand for making the relevant medicines work better. Using a fluphenazine (FZN) drug delivery system that has been optimized using nanoparticles (NPs) technology is an important alternative treatment option for noncompliant patients with schizophrenia. Compared to the conventional delivery system, the NPs delivery system provides a controlled-release treatment, minimizes drug levels reaching the blood, and has fewer side effects as well. As a result of using the NPs delivery system, patients can obtain the benefits of reduced daily dosing and improved compliance. In this context, this study was performed to develop a mathematical model for FZN to optimize its nanocomposite delivery system using a mixture-process DoE and multiobjective optimization (MOO) approaches. The influences of NPs input fabrication parameters [i.e., FZN percentage, chitosan (CS) percentage, sodium tripolyphosphate (TPP) percentage, and pH] were investigated by mixture-designed experiments and analyzed by analysis of variance (ANOVA); subsequently, based on the results of the analysis, three regression models were built for size, zeta potential (ZP), and drug loading efficiency (LE%); and thereafter, these models were validated using 26 experiments with three replicates. The MOO approach was employed using a non-dominated sorting genetic algorithm (NSGA-II) to provide the optimal fitness value of each objective function by minimizing NPs size, maximizing ZP, and maximizing LE%. Test of hypotheses showed no statistical differences between the average observed values and the corresponding predicted values calculated by the regression models (126.6 nm, 18.7 mV, and 91.6%, respectively). As there is no benchmark available for the optimal NPs input fabrication parameters in the literature, the optimized formulation was further characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), polydispersity index (PdI), and differential scanning calorimetry (DSC). Those tests indicated that FZN was successfully encapsulated into the final nanocomposite. Furthermore, an <italic>in-vitro</italic> drug release study was carried out and showed that at least 5 days would be needed for FZN to be fully released from its nanocomposite in a sustained-release pattern. The nanocomposite could serve as a model for the controlled and extended delivery of many drugs.</p>
</abstract>

The World Health Organization (WHO, 2019) reports that schizophrenia affects approximately 20 million people worldwide, and the annual number of new cases is estimated at 1.5%/10,000 people. As a result, there is a demand for making the relevant medicines work better. Using a fluphenazine (FZN) drug delivery system that has been optimized using nanoparticles (NPs) technology is an important alternative treatment option for noncompliant patients with schizophrenia. Compared to the conventional delivery system, the NPs delivery system provides a controlled-release treatment, minimizes drug levels reaching the blood, and has fewer side effects as well. As a result of using the NPs delivery system, patients can obtain the benefits of reduced daily dosing and improved compliance. In this context, this study was performed to develop a mathematical model for FZN to optimize its nanocomposite delivery system using a mixture-process DoE and multiobjective optimization (MOO) approaches. The influences of NPs input fabrication parameters [i.e., FZN percentage, chitosan (CS) percentage, sodium tripolyphosphate (TPP) percentage, and pH] were investigated by mixture-designed experiments and analyzed by analysis of variance (ANOVA); subsequently, based on the results of the analysis, three regression models were built for size, zeta potential (ZP), and drug loading efficiency (LE%); and thereafter, these models were validated using 26 experiments with three replicates. The MOO approach was employed using a non-dominated sorting genetic algorithm (NSGA-II) to provide the optimal fitness value of each objective function by minimizing NPs size, maximizing ZP, and maximizing LE%. Test of hypotheses showed no statistical differences between the average observed values and the corresponding predicted values calculated by the regression models (126.6 nm, 18.7 mV, and 91.6%, respectively). As there is no benchmark available for the optimal NPs input fabrication parameters in the literature, the optimized formulation was further characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), polydispersity index (PdI), and differential scanning calorimetry (DSC). Those tests indicated that FZN was successfully encapsulated into the final nanocomposite. Furthermore, an in-vitro drug release study was carried out and showed that at least 5 days would be needed for FZN to be fully released from its nanocomposite in a sustained-release pattern. The nanocomposite could serve as a model for the controlled and extended delivery of many drugs.

Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Department of Industrial Engineering, School of Applied Technical Sciences

Basic Pharmaceutical Sciences, Faculty of Pharmacy

Materials Science-Poland

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The World Health Organization (WHO, 2019) reports that schizophrenia affects approximately 20 million people worldwide, and the annual number of new cases is...

Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Data publikacji: 27 mar 2022

Zakres stron: 517 - 544

Otrzymano: 02 gru 2021

Przyjęty: 26 sty 2022

DOI: https://doi.org/10.2478/msp-2021-0042

Słowa kluczowe
mixture design of experiment, multiobjective optimization, NSGA-II, nanoparticles, fluphenazine drug, regression analysis

© 2021 Ahmed Adnan Abu Sharar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

Data publikacji: 27 mar 2022

Zakres stron: 517 - 544

Otrzymano: 02 gru 2021

Przyjęty: 26 sty 2022

DOI: https://doi.org/10.2478/msp-2021-0042

Słowa kluczowemixture design of experiment, multiobjective optimization, NSGA-II, nanoparticles, fluphenazine drug, regression analysis

© 2021 Ahmed Adnan Abu Sharar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
mixture design of experiment, multiobjective optimization, NSGA-II, nanoparticles, fluphenazine drug, regression analysis