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FABAD
J. Pharm. Sci.
ISSN 1300-4182
Copyright Ó 2005
FABAD. All rights reserved
FABAD
J. Pharm. Sci., 29(1), 21-25, 2004. PDF
(109 KB)
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Research
Articles
ABSTRACT
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SOLUBILITY AND DISSOLUTION PROPERTIES OF GLICLAZIDE
Esra DEMİRTÜRK * , Levent ÖNER*,o
*Hacettepe University, Faculty of Pharmacy, Department of
Pharmaceutical Technology, 06100 Sıhhiye, Ankara, TURKEY.
oCorresponding Author
Summary
Gliclazide is a second generation sulfonylurea drug, characterized
by poor solubility and, hence, by a low dissolution rate
in water. This property causes inter-individual variations
of its bioavailability. The major drawback in the therapeutic
application and efficacy of gliclazide as oral dosage
forms is its very low aqueous solubility because of its
hydrophobic nature. Statistical experimental design is
the methodology of how to conduct and plan experiments
in order to extract the maximum amount of information
in the fewest number of runs. With the rapidly rising
cost of conducting experiments, it is essential that optimization
be achieved with as few experiments as possible. This
is one important reason why statistical experimental design
is needed. Most experimentation today is done by changing
levels of one factor (variable) at a time in a non-systematic
way in order to try and find the optimum conditions of
a complex system. It is not a good strategy because of
its low efficacy, non-rationality and expense. A key concept
often employed in optimization methodology is the response
surface graphic. The response surface graphic is a geometrical
representation of the response and the factor levels similar
to a contour graphic. The formulation or a region in which
its response has optimal characteristics based on the
experimenter‘s specifications can be chosen in this way.
Results of this study indicate the low aqueous solubility
and low dissolution rate of gliclazide. A factorial design
study was used to investigate the results, and the polynomial
equations were constructed for further investigations.
Key Words :
Gliclazide, factorial design, optimization, dissolution.
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