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

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.

In-Silico Screening of Lipid-Based Drug Delivery Systems.

PURPOSE: This work proposes an in-silico screening method for identifying promising formulation candidates in complex lipid-based drug delivery systems (LBDDS). METHOD: The approach is based on a minimum amount of experimental data for API solubilites in single excipients. Intermolecular interactions between APIs and excipients as well as between different excipients were accounted for by the Perturbed-Chain Statistical Associating Fluid Theory. The approach was applied to the in-silico screening of lipid-based formulations for ten model APIs (fenofibrate, ibuprofen, praziquantel, carbamazepine, cinnarizine, felodipine, naproxen, indomethacin, griseofulvin and glibenclamide) in mixtures of up to three out of nine excipients (tricaprylin, Capmul MCM, caprylic acid, Capryol™ 90, Lauroglycol™ FCC, Kolliphor TPGS, polyethylene glycol, carbitol and ethanol). RESULTS: For eight out of the ten investigated model APIs, the solubilities in the final formulations could be enhanced by up to 100 times compared to the solubility in pure tricaprylin. Fenofibrate, ibuprofen, praziquantel, carbamazepine are recommended as type I formulations, whereas cinnarizine and felodipine showed a distinctive solubility gain in type II formulations. Increased solubility was found for naproxen and indomethacin in type IIIb and type IV formulations. The solubility of griseofulvin and glibenclamide could be slightly enhanced in type IIIb formulations. The experimental validation agreed very well with the screening results. CONCLUSION: The API solubility individually depends on the choice of excipients. The proposed in-silico-screening approach allows formulators to quickly determine most-appropriate types of lipid-based formulations for a given API with low experimental effort. Graphical abstract.

Solubility of Pharmaceutical Ingredients in Natural Edible Oils.

Natural edible oils (NEOs) are common excipients for lipid-based formulations. Many of them are complex mixtures comprising hundreds of different triglycerides (TGs). One major challenge in developing lipid-based formulations is the variety in NEO compositions affecting the solubility of active pharmaceutical ingredients. In this work, solubilities of indomethacin (IND), ibuprofen (IBU), and fenofibrate (FFB) in soybean oil and in coconut oil were measured via differential scanning calorimetry, high-performance liquid chromatography, and Raman spectroscopy. Furthermore, this work proposes an approach that mimics NEOs using one key TG and models the API solubilities in these NEOs based on perturbed-chain statistical associating fluid theory (PC-SAFT). Key TGs were determined using the 1,2,3-random hypothesis, and PC-SAFT parameters were estimated via a group-contribution method. Using the proposed approach, the solubility of IBU and FFB was modeled in soybean oil and coconut oil. Furthermore, the solubilities of five more APIs (IND, cinnarizine, naproxen, griseofulvin, and felodipine) were modeled in soybean oil. All modeling results were found in very good agreement with the experimental data. The influence of different NEO kinds on API solubility was examined by comparing FFB and IBU solubilities in soybean oil and refined coconut oil. PC-SAFT was thus found to allow assessing the batch-to-batch consistency of NEO batches in silico.

Solubility of pharmaceutical ingredients in triglycerides.

Lipid-based drug delivery systems (LBDDS) are highly relevant as pharmaceutical formulations significantly enhancing the bioavailability of active pharmaceutical ingredients (APIs). These formulations often are complex mixtures of APIs, various lipids, and other excipients (e.g. surfactants). In their simplest form, LBDDS contain one API being dissolved in a pure lipid, which often is a triglyceride (TG). In this work, solubilities of the APIs indomethacin, ibuprofen, and fenofibrate in pure TGs of different chain lengths (C chain 8-18) and degree of saturation were investigated. Solubilities of APIs in TGs were measured via differential scanning calorimetry, hot-stage microscopy, high-performance liquid chromatography, and Raman spectroscopy. The influence of fatty-acid chain length and degree of saturation on the API solubility in the TGs was investigated. APIs showed a higher solubility in saturated (wIBU = 10.5 wt% at 25 °C in tricaprylin) TGs compared to unsaturated ones (wIBU = 4.0 wt% at 25 °C in triolein). The fatty-acid chain length of TGs only slightly affects the solubility of ibuprofen and fenofibrate, but strongly influences the eutectic temperature of the API/TG mixtures. API solubilities in TGs and TG mixtures (mixtures of tricaprylin and tricaprin) were successfully modeled using the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) accounting for the intermolecular API/TG interactions providing a deep understanding of the energetic and structural impact of the TGs on API solubility.