Comparison of Dabigatran Etexilate Self-Micro-Emulsifying Drug Delivery Systems Formulation Optimization Techniques: Design Expert Vs. MATLAB

Abstract

Background

This work aimed to formulate Dabigatran etexilate, a BCS class II medication, as self-micro-emulsifying drug delivery system (SMEDDS) to increase its rate of dissolution. By doing solubility experiments of the medication in various oils, surfactants, and cosurfactants, the three primary formulation components of a SMEDDS formulation were chosen. Formulation design and optimization were done by Box-Behnken design in Design Expert software. A comparative study was conducted with artificial neural networks (ANN) using MATLAB Software for better prediction of the selected output variables. The formulations were made and tested for transmittance and drug release percentages. The desirability function was used to create an optimal formulation, which was then made and tested for emulsification time, centrifugation, viscosity, cloud point, dilution and phase separation. Neusilin was used as an adsorbent to further solidify the optimized formulation and produce a stable product. The solidified optimized formulation was then subjected to fourier transform infrared spectroscopy and x-ray diffraction studies.

Results

The optimized SMEDDS Dabigatran etexilate formulations contained mixtures of Kollisolv MCT70 (oil), Kolliphor EL (surfactant), and PEG 400 (cosurfactant). The higher R² values and lower MSE values of percentage drug release and percentage transmittance for ANN compared to Box-Behnken design-based quadratic model indicate better predictability of ANN. In vitro release of optimized SMEDDS was 81.09 ± 1.37% within 1 h. It exhibited a significant transmittance of 89 ± 0.63%.

Conclusion

The results indicated that SMEDDS capsules could be effectively used to improve the solubility rate of Dabigatran etexilate. ANN can be successfully used as a better model for predicting characteristics of formulations.

Graphical Abstract