Formulation Optimization of Low Bioavailable Drug Loaded Alginate Microparticles Using Artificial Neural Networks

Katayoun Derakhashandeh; Zahra Hamedi; Moin Karimi; Mahmood Amiri; Farahnaz Ahmadi

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Formulation Optimization of Low Bioavailable Drug Loaded Alginate Microparticles Using Artificial Neural Networks

Author(s):

Katayoun Derakhashandeh^{1, 2,*},

Zahra Hamedi¹,

Moin Karimi¹,

Mahmood Amiri³,

Farahnaz Ahmadi⁴

1Department of Pharmaceutics, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran

2Nano Drug Delivery Research Center, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran

3Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran

4Department of Pharmacology and Toxicology, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran

Journal of Reports in Pharmaceutical Sciences:Vol. 1, issue 1; 49-59

Published online:Apr 25, 2012

Article type:Research Article

Received:Mar 26, 2012

Accepted:Apr 19, 2012

How to Cite:Katayoun DerakhashandehZahra HamediMoin KarimiMahmood AmiriFarahnaz AhmadiFormulation Optimization of Low Bioavailable Drug Loaded Alginate Microparticles Using Artificial Neural Networks.J Rep Pharm Sci.1(1):e147805.

Abstract

The adsorptive behavior of the immunosuppressive agent tacrolimus was studied by cyclic and differential-pulse voltammetry on a hanging mercury drop electrode (HMDE). The drug was accumulated at HMDE and two well-defined peak currents were obtained at -1353 and -1417 mV vs. SCE (saturated KCl) in borate buffer (pH 10.0) + 0.1 KCl solution. A voltammetric procedure was developed for the determination of tacrolimus using differential-pulse adsorptive stripping voltammetry (DPAdSV). The optimum working conditions for determination of the drug were established. The analysis of tacrolimus in pharmaceutical dosage forms was carried out satisfactorily. In the present study, sodium alginate microparticle for oral delivery of furosemide was designed whether the encapsulation into microparticles might improve the oral absorption of this potent loop diuretic. We described preparation of microspheres based on ionotropic gelation method and characterized its physicochemical properties.To acquire an optimum formulation, a Generalized Regression Neural Networks (GRNN) and a Multi-Layer Perceptron (MLP) were employed. The drug loaded formulation parameters were the input vectors of the GRNN and included the amount of polymer, cross linked agent, volume of external and internal phases. The microparticles drug loading, size and in vitro drug release constitute the output vector of each network. In this way, GRNN and MLP were trained to investigate the functional influence of input variables on the output responses. The results demonstrated that GRNN is promising in providing better solutions for optimization of drug delivery system formulation.The obtained optimum formulation showed a narrow size distribution on an average diameter of 700 ± 50 m and drug loading of more than 75%. The drug release profile illustrates a sustained released pattern and released percent of about 36% in 2 hour. In vitro drug release rate for microspheres was found to be sustained over 24 hours, obeying Higuchi order kinetic. Furthermore, the results of this paper confirmed that alginate microparticles could be a hopeful carrier for orally administration of furosemide and provides a sustained release property for this potent anti hypertension drug. In addition, the novel formulation design facilitates the optimization and successful development of microsphere formulations for enhanced safe and effective oral drug delivery.

Keywords

Alginate

Microparticles

Furosemide

Artificial Neural- Networks

Oral Drug Delivery

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