Desirability Function in analytical method development for determination of glitazones and metabolites employing HF-LPME

Abstract

Thiazolidinedione, often shortened to TZD or glitazone, helps lower insulin resistance, which is the underlying problem for many people with type 2 diabetes. The two most known glitazones are pioglitazone (PGZ), with the brand name medicine Actos^®, and rosiglitazone (RSG), which is Avandia^®. This study presented a multivariate optimization in the microextraction procedure employing Fractional Factorial Design (FFD) combined with Desirability Function (DF) to determine TZD and metabolites in biological samples. Microextraction requires several parameters to be optimized; however, most of them still use univariate optimization. Finding optimum conditions by simple response is relatively simple, but the problems, in case of microextractions, are often more complex when it has more responses. For example, changing one factor that promotes one response may suppress the effect of the others. Thus, this multivariate optimization was applied for two bioanalytical methods for determination of TZD and metabolites, one by HPLC and other by CE, both using Hollow Fiber Liquid-Phase Microextraction (HF-LPME). The results establish the optimal values and elucidate how the factors that affect HF-LPME procedure perform in extraction efficiency for TZDs. Additionally, this study demonstrates that DF can be an important tool to optimize microextraction procedures.