Predicting the API partitioning between lipid-based drug delivery systems and water

Abstract

Partitioning tests in water are early-stage standard experiments during the development of pharmaceutical formulations, e.g. of lipid-based drug delivery system (LBDDS). The partitioning behavior of the active pharmaceutical ingredient (API) between the fatty phase and the aqueous phase is a key property, which is supposed to be determined by those tests. In this work, we investigated the API partitioning between LBDDS and water by in-silico predictions applying the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) and validated these predictions experimentally. The API partitioning was investigated for LBDDS comprising up to four components (cinnarizine or ibuprofen with tricaprylin, caprylic acid, and ethanol). The influence of LBDDS/water mixing ratios from 1/1 up to 1/200 (w/w) as well as the influence of excipients on the API partitioning was studied. Moreover, possible API crystallization upon mixing the LBDDS with water was predicted.This work showed that PC-SAFT is a strong tool for predicting the API partitioning behavior during in-vitro tests. Thus, it allows rapidly assessing whether or not a specific LBDDS might be a promising candidate for further in-vitro tests and identifying the API load up to which API crystallization can be avoided.