Home J Young Pharm, Vol 9/Issue 3/2017 Utilization of Response Surface Methodology for Modeling and Optimization of Tablet Compression Process

Utilization of Response Surface Methodology for Modeling and Optimization of Tablet Compression Process

by [email protected]
Published on:July 2017
Journal of Young Pharmacists, 2017; 9(3):417-421
Original Article | doi:10.5530/jyp.2017.9.82
Authors:

Vijay Kumar Garlapati1, Lakshmishri Roy2

1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat – 173234, HP, INDIA.

2Department of Food Technology, Techno India, Kolkata, West Bengal – 700091, INDIA.

Abstract:

Rationale: Table compression process has a profound effect on different quality attributes of table manufacturing process such as appearance, content uniformity, hardness, thickness, friability, Disintegration time and Dissolution time. Among all the table compression parameters, feeder speed, precompression, main compression forces and Turret speed have a substantial effect on tablet properties. Aim: Statistical modeling and optimization approach has been utilized to model and optimize Levocetirizine tablet formulation compression process using Response Surface Methodology. Methods: A 3-level Central Composite Design has been chosen by taking turret speed, pre & main compression forces and Feeder speed as input variables and tablet characteristics (hardness, thickness and disintegration time) as output variables. Results: Non-linear regression models have obtained with respective to output variables (hardness, thickness and disintegration time) with R2 values of 99.26%, 98.01% and 99.84% for hardness, thickness and disintegration time, respectively. The effect of individual, square and interaction terms on the table hardness, thickness and disintegration has been summarized through the significance test and depicted through response surface plots. An optimized tablet characteristic of 15.3 kP hardness, 3.7 mm thickness and 226 sec disintegration time have been obtained using predicted tablet compression process variables of 68 rpm Turret speed, pre and main compression forces of 2.05 and 7.95 kN respectively and Feeder speed of 27 rpm. Conclusion: The results demonstrated the reliability of the proposed statistical approach to model and optimized the compression studies of levocetirizine tablet formulation. The present study helps in scale-up studies of tablet compression during Levocetirizine tablet formulation.

Key words: Central Composite Design, Levocetirizine, Optimization, Statistical Modeling, Tablet Compression.