QbD application for a fixed-dose combination with biowaiver potential: Evaluations of In vitro and In vivo applications
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CitationYaşın, D.S., Uslu, A., Uyar, E., Erdinç, M. ve Teksin, Z.Ş. (2022). QbD application for a fixed-dose combination with biowaiver potential: Evaluations of In vitro and In vivo applications. Journal of Pharmaceutical Innovation, Early Access
Purpose The purpose of this study was to use the quality by design (QbD) approach to design a directly compressed fixed-dose combination (FDC) tablet comprising amlodipine besylate and enalapril maleate with biowaiver potential in alignment with the Biopharmaceutical Classification System (BCS). Methods As a result of the risk assessment, the amounts of the formulation components such as disintegrant, binder, and lubricant were selected as critical material attributes, and the processes of blending and lubrication were accepted as critical process parameters in a screening design of Plackett-Burman. These factors were evaluated based on the statistical significance of their impact on the drug product's content uniformity, assay, friability, disintegration, and dissolution. The most significant factors determined with the use of Pareto charts and half-normal graphs were the amount of lubricant and disintegrant and blending time, all of which were subsequently optimized using Box-Behnken Design. The optimum formulation was evaluated with in vitro quality tests and in vivo blood pressure-lowering efficacy tests, the results of which were compared to the individual references in rats. Results As a result of the optimization process, a design space was established for the critical factors. FDC product showed quality and dissolution profiles similar to those of the references. Combination therapy was superior to individual drugs in rats (p < 0.05). Conclusion It was concluded that an FDC product eligible for BCS-based biowaiver can be developed systematically by using the QbD concept. It was demonstrated that using scanning designs prior to optimization can reduce the number of unnecessary experiments and yield more reliable results in less time.