Multiobjective optimization of fluphenazine nanocomposite formulation using NSGA-II method

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