Main Article Content
Introduction: In the development of pharmaceutical products, the objective of the formulation
optimization is to design and develop the proper dosage form that results in higher efficacy, safety and
stability. The formulation efficacy and safety can be assessed during the optimization process. The
evaluations for the formulation efficacy and safety saved time more than the formulation stability study. The
stability evaluation takes a lot of time for the long term stability study, so several researchers have chosen
the accelerated stability instead. However, at least 2-3 months were spent for the accelerated stability
evaluation. The objective of this study was to minimize the time and measurement samples for determining
the accelerated stability during the optimization of ellagic acid loaded transfersomes using the computer
program. Methods: Ten model formulations of transfersomes consisting of a constant percentage of
phosphatidylcholine, ellagic acid and various molar percentages of cholesterol and oleic acid from 0-
90%mol. The transfersome formulations were experimentally prepared and investigated. To evaluate the
stability, all transfersome formulations were kept in the stability chamber at 45C for 12 h and the
refrigerator at 4C for 12 h, for3 cycles. The sample was collected and measured through the
physicochemical characteristics, e.g. vesicle size, polydispersity index (PDI), surface charge and entrapment
efficiency. The Design Expert® was applied for determining the accelerated stability during the formulation
optimization. Results: The results suggested that the computer program was a powerful methodfor
estimating the stability of the formulation during the optimization process. Moreover, the Design Expert®
obviously showed the relationship between the formulation components and the formulation stability.
Conclusion: The utilization of the computer program in this study was succeeded in showing the feasibility
of the approach for determining the accelerated stability during the formulation optimization with the
minimization of time and measurement samples.
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