Computer-aided optimization of carbidopa/levodopa orally disintegrating tablets

Abstract

Objective

This study aimed to optimize the formulation of carbidopa/levodopa orally disintegrating tablets (ODTs) in order to improve their disintegration performance, and facilitate easier medication intake for Parkinson’s patients.

Method

The response surface methodology (RSM) was used to optimize the formulation, with the content of cross-linked polyvinylpyrrolidone (PVPP), microcrystalline cellulose (MCC), and mannitol (MNT) as independent variables, and disintegration time as the response parameter. Python was utilized to model Carr Indices and mixing time to determine the suitable mixing time. Direct compression (DC) was used for the preparation of ODTs.

Result

The optimization process resulted in the following values for the independent variables: 7.04% PVPP, 22.02% MCC, and 16.21% MNT. By optimizing the mixing time using Python, it was reduced to 14.19 min. The ODTs prepared using the optimized formulation and a mixing time of 14.19 min exhibited disintegration times of 16.74 s in vitro and 17.63 s in vivo. The content uniformity of levodopa and carbidopa was found to be 100.83% and 99.48%, respectively.

Conclusion

The ODTs optimized using RSM and Python demonstrated excellent disintegration performance, leading to a decrease in the time the drug exists in solid form in the oral cavity. This improvement in disintegration time reduced the difficulty of swallowing for patients and enhanced medication compliance, while still ensuring that ODTs prepared by DC had sufficient mechanical strength to meet storage and transportation requirements.

Keywords:

• Levodopa
• carbidopa
• orally disintegration tablets
• design-expert
• python

Acknowledgements

This work was supported by the Department of Pharmaceutical Engineering, College of Chemical Engineering, Qingdao University of Science and Technology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [21546004] and the Natural Science Foundation of Shandong Province [ZR2022MB119].