Novel Biphasic Lipolysis Method To Predict in Vivo Performance of Lipid-Based Formulations
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The absence of an intestinal absorption sink is a significant weakness of standard in vitro lipolysis methods, potentially leading to poor prediction of in vivo performance and an overestimation of drug precipitation. In addition, the majority of the described lipolysis methods only attempt to simulate intestinal conditions, thus overlooking any supersaturation or precipitation of ionizable drugs as they transition from the acidic gastric environment to the more neutral conditions of the intestine. The aim of this study was to develop a novel lipolysis method incorporating a two-stage gastric-to-intestinal transition and an absorptive compartment to reliably predict in vivo performance of lipid-based formulations (LBFs). Drug absorption was mimicked by in situ quantification of drug partitioning into a decanol layer. The method was used to characterize LBFs from four studies described in the literature, involving three model drugs (i.e., nilotinib, fenofibrate, and danazol) where in vivo bioavailability data have previously been reported. The results from the novel biphasic lipolysis method were compared to those of the standard pH-stat method in terms of reliability for predicting the in vivo performance. For three of the studies, the novel biphasic lipolysis method more reliably predicted the in vivo bioavailability compared to the standard pH-stat method. In contrast, the standard pH-stat method was found to produce more predictive results for one study involving a series of LBFs composed of the soybean oil, glyceryl monolinoleate (Maisine CC), Kolliphor EL, and ethanol. This result was surprising and could reflect that increasing concentrations of ethanol (as a cosolvent) in the formulations may have resulted in greater partitioning of the drug into the decanol absorptive compartment. In addition to the improved predictivity for most of the investigated systems, this biphasic lipolysis method also uses in situ analysis and avoids time- and resource-intensive sample analysis steps, thereby facilitating a higher throughput capacity and biorelevant approach for characterization of LBFs.