Design and Optimization of Scored Tablets with Concave Surface and Application of Bayesian Estimation for Solving
Hiroki Katayama1, Yoshiharu Maeda2, Tsubasa Sato3
1Department of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Josai University.
Developing patient-friendly concavely curved scored tablets (CCST) ensures uniform division for self-medication. This study optimized CCST preparation and used Bayesian estimation to address scale-up challenges, confirming their dividing uniformity advantage.
Area of Science:
- Pharmaceutical Technology
- Materials Science
Background:
- Patient-friendly pharmaceutical formulations are crucial for self-medication adherence.
- Scored tablets require precise dividing uniformity to ensure accurate dosing.
Purpose of the Study:
- To optimize the preparation conditions for concavely curved scored tablets (CCST).
- To evaluate the scale-up potential and dividing uniformity of CCST.
Main Methods:
- Design of experiments (DOE) and response surface methodology (RSM) with thin-plate spline interpolation.
- Bootstrap resampling for robust optimization.
- Bayesian estimation for scale-up prediction.
- Finite element method (FEM) for stress analysis.
Main Results:
- Optimal preparation conditions for CCST were successfully developed.
- Bayesian estimation suggested a viable approach for solving scale-up challenges in large-scale manufacturing.
- FEM simulations indicated tensile stresses at the score line tip under applied force.
- The unique CCST shape demonstrated an advantage in dividing uniformity.
Conclusions:
- The study successfully optimized CCST preparation and demonstrated its potential for scale-up.
- CCSTs offer improved dividing uniformity, enhancing patient-friendly medication.
- Bayesian estimation is a valuable tool for predicting large-scale manufacturing outcomes.
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