Applied Chemical Engineering

Formulation and Statistical Optimisation of Liposome-Loaded Glimepiride Using Design-Expert® Software

Wafaa Nahi

Department of Pharmaceutics, Faculty of Pharmacy, University of Al-Qadisiya, Al-Diwaniyah, Iraq

Sarmad Al-Edresi

Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, University of Kufa, Najaf, Iraq, 52001.

Rafid Mohammed Ali Hassan wasfi

Clinical Laboratory Sciences, Faculty of Pharmacy, University of Kufa, Najaf, Iraq.

DOI: https://doi.org/10.59429/ace.v9i1.5794

Keywords: Liposome; Glimepiride; Preformulation; Design-Expert® software

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Abstract

Background: Glimepiride is a popular third-generation sulfonylurea used to treat type 2 diabetes. Its therapeutic effectiveness is, however, limited by its fluctuating bioavailability and poor water solubility. Liposomal drug delivery techniques offer an attractive means of improving the stability, solubility, and bioavailability of poorly soluble drugs like Glimepiride.

Objective: This study seeks to develop and statistically optimise Glimepiride-loaded liposomes utilising Design-expert® software as part of an extensive pre-formulation analysis.

Method: Liposomes were synthesised utilising the thin-film hydration method, adjusting critical formulation variables like phospholipid content and cholesterol proportion. Utilizing a statistical experimental design methodology, the impact of these factors on key quality measures, including loading capacity (DL%), entrapment efficacy (EE%), zeta potential, particle size, and polydispersity index (PDI), was assessed. The Design-expert ® software facilitated model generation, optimisation, and response surface analysis.

Results: A statistical investigation revealed that the formulation factors had a substantial effect on the liposomes' properties. Zeta potential of -28.85 mV, polydispersity index (PDI) of 0.025, particle size of 71 nm, drug loading (DL%) of 7.53%, and entrapment efficiency (EE%) of 35% were all characteristics of the optimized liposomal formulation. The regression models demonstrated robust predictive capability, and the design space was effectively delineated for subsequent formulation development.

Conclusion: This preformulation study illustrated the successful application of statistical design in optimising Glimepiride-loaded liposomes. These findings establish a foundation for subsequent studies concerning in vitro release and stability testing in the forthcoming phase of formulation development.

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