Table IX: Dissolution studies of marketed sustained-release formulations versus optimized ibuprofen formulation.
|
Data analysis for drug-release kinetic.
After applying the different release model to the dissolution release profile of the optimized formulation, the value of correlation-coefficient
obtained by zero order, first order, Higuchi release, and Korsmeyer–Peppas model were 0.909, 0.774, 0.990, and 0.64, respectively.
The optimized formulation thus follows the Higuchi release.
Figure 3: Dissolution studies of a marketed sustained-release formulation and an optimized ibuprofen formulation.
|
Dissolution studies of a marketed sustained-release formulation and the optimized ibuprofen formulation.
Dissolution studies were also performed for a marketed sustained-release formulation (Ibugesic SR-300, Cipla, Mumbai) by using
the same dissolution conditions. The comparative release data of both formulations are represented in Table IX and in Figure
3. The release data clearly demonstrate that the marketed sustained-release tablet releases 90.12% of drug in 12 h, while
the optimized tablet releases 94.7% of drug in 24 h.
Conclusion
All three formulation variables evaluated in this study affected the release profiles and maintained the floating of the formulations.
However, only the sodium-bicarbonate loading level was important within the selected range for floating properties because
HPMC alone could not maintain the floating for more than 1 h. HPMC provided the sustained release, but the ratio of the grades
was not important. Carbopol provided the binding capacity to the tablet and maintained the integrity of the tablet. When ibuprofen
is administered as a conventional dosage form, it displays a short half life (i.e., 2 to 4 hours). By applying the direct
tablet-compression method optimized by 23 full factorial design, the authors successfully developed a controlled-release intragastric floating system for ibuprofen
that floated and provided sustained release for as long as 24 h. The statistical approach to formulation optimization was
a useful tool, particularly when several variables needed to be evaluated.
A. Nanda is professor of pharmaceutics and dean at the Faculty of Pharmaceutical Sciences, M.D. University, Rohatak, Haryana, M. Ola* is a lecturer at the Goenka College of Pharmacy, Lachhmangarh, Sikar, India, R. Bhaskar is a lecturer at Seth G.L. Bihani S.D. College of Technical Education, Sriganganagar, India, and C.K. Sharma and S. Nayak are lecturers, both at the Goenka College of Pharmacy, tel. +91 97992 99708, monika.ola@rediffmail.com
*To whom all correspondence should be addressed.
Submitted: Aug. 14, 2009. Accepted: Feb. 18, 2010.
|
