A Review on Optimization of Drug Delivery System With Experimental Designs

Abstract

The present review article aims at determining the various possible techniques available to enhance the quality, safety and efficacy of pharmaceutical formulations by exploring most suitable and practically applicable experimental designs and optimization techniques. As we know that pharmaceutical industries are constantly in search of novel ideas to improve quality by various optimization techniques, hence in present review article we shall discuss latest optimization techniques and experimental designs to achieve the best combination of product and process characteristics under the given set of conditions. Experimental designs and optimization techniques are the tools that are simultaneously and systematically used to identify various types of problems that may influence research, development and production of pharmaceutical formulations. These are organized an approach to determine the relationship between the factors affecting a process and the output of that process. The screening methods discussed here include factorial design, fractional factorial designs, full factorial design, mixture designs etc. Recently, different software has been used in implementing optimization techniques in pharmaceutical products to enhance product quality by using most suitable available facilities.

A multivariate quantification of Box‐Behnken design assisted method development and validation of dextromethorphan hydrobromide and desloratadine simultaneously by reverse‐phase HPLC in in‐house syrup formulation

“…The developed method was optimized using response surface methodology by applying BBD and implementing the software known as Design Expert 12.0.7.0 software. The applied design matrix yield 27 experimental runs based on the conditions of parameters suggested according to Table 1, and then the obtained data are presented in Table 2, representing experimental design and its corresponding response variables (Y1, Y2, and Y3) [12,13] . Finally, analysis of variance (ANOVA) was applied to obtain the significant difference in design matrix, and the data obtained after implementation of ANOVA are presented in Table 3.…”

A 32 Full Factorial Design for Topical Controlled Releasetazarotene Microsponge Using HPMC Gel

“…The coefficients b1, b2 and b11 were found to be significant at P<0.05; hence they were retained in the reduced model. The reduced model for time required to 80% drug release [28,29] = 409.444+ (14.167…”

Formulation and Evaluation of Statistically Designed Ibuprofen Fastdissolving Tablets Employing Starch Glutamate as a Novel Superdisintegrant

“…A statistical analysis was conducted to know the optimum formulation with suitable superdisintegrant having the highest percentage of drug A polynomial regression algorithm equation was drawn by correlating the independent variables like concentration of superdisintegrants, i.e.,, starch glutamate (A), croscarmellose sodium (B), and crospovidone (C) and response variables such as dissolution efficiency in 5 min and percent dissolved in 5 min. Contour plots and surface response plots were plotted with the help of Design Expert 7.11 version software to know the interaction effects of the level of each factor in the dissolution efficiency in 5 min and percent dissolved in 5 min [4].…”