Development and optimization of fluoxetine orally disintegrating tablets using Box-Behnken design

Purpose: To develop and optimise some variables that influence fluoxetine orally disintegrating tablets (ODTs) formulation. Methods: Fluoxetine ODTs tablets were prepared using direct compression method. Three-factor, 3- level Box-Behnken design was used to optimize and develop fluoxetine ODT formulation. The design suggested 15 formulations of different lubricant concentration (X1), lubricant mixing time (X2), and compression force (X3) and then their effect was monitored on tablet weight (Y1), thickness (Y2), hardness (Y3), % friability (Y4), and disintegration time (Y5). Results: All powder blends showed acceptable flow properties, ranging from good to excellent. The disintegration time (Y5) was affected directly by lubricant concentration (X1). Lubricant mixing time (X2) had a direct effect on tablet thickness (Y2) and hardness (Y3), while compression force (X3) had a direct impact on tablet hardness (Y3), % friability (Y4) and disintegration time (Y5). Accordingly, Box-Behnken design suggested an optimized formula of 0.86 mg (X1), 15.3 min (X2), and 10.6 KN (X3). Finally, the prediction error percentage responses of Y1, Y2, Y3, Y4, and Y5 were 0.31, 0.52, 2.13, 3.92 and 3.75 %, respectively. Formula 4 and 8 achieved 90 % of drug release within the first 5 min of dissolution test. Conclusion: Fluoxetine ODT formulation has been developed and optimized successfully using Box- Behnken design and has also been manufactured efficiently using direct compression technique.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.