DESIGN AND OPTIMIZATION OF DOXORUBICIN HCL PRONIOSOMES BY-DESIGN OF EXPERIMENT

Objective: The present research work was designed to formulate and optimize doxorubicin HCl proniosomes by design of experiment (DoE). Methods: A 4-factor, 3-level Box-Behnken design was used to explain multiple linear regression analysis and contour 3D plot responses. The independent variables selected were tween 20, cholesterol, hydration volume and sonication time; dependent variables percentage entrapment efficiency (PEE), mean vesicle size (MVS). Based on the Box-Behnken design 29 trial runs were studied and optimized for PEE and MVS. Further "Model F-Value" was calculated to confirm the omission of insignificant terms from the full-model equation to derive a multiple linear regression analysis to predict the PEE and MVS of niosomes derived from proniosomes. 3D plots were constructed to show the influence of independent variables on dependent variables. Results: PEE of doxorubicin HCl proniosomes was found to be in the range of 40.21-87.5%. The polynomial equation for PEE exhibited a good correlation coefficient (0.5524) and the "Model F-Value" of 7.41 implies the model is significant. P-values less than 0.0500 indicate model terms are significant. The MVS of doxorubicin HCl proniosomes was found to be in the range of 325.2 nm to 420.25 nm. The mathematical model generated for MVS (R2) was found to be significant with model F-value of 54.22. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise (P<0.0500) and R2 value of 0.9004. Conclusion: The DoE of Box-Behnken design demonstrated the role of the derived equation, 3D plot in predicting the values of dependent variables for the preparation and optimization of doxorubicin HCl proniosomes. The results suggest that doxorubicin HCl proniosomes can act as a promising carrier.