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

Area of Science:

• Pharmaceutical Sciences
• Physical Chemistry
• Computational Chemistry

Background:

• Partitioning tests are crucial in early pharmaceutical formulation development, especially for lipid-based drug delivery systems (LBDDS).
• Understanding active pharmaceutical ingredient (API) partitioning between LBDDS and aqueous phases is vital for formulation success.
• Current methods for assessing API partitioning can be time-consuming.

Purpose of the Study:

• To investigate API partitioning between LBDDS and water using in-silico predictions.
• To validate these predictions experimentally.
• To assess the utility of PC-SAFT for predicting API behavior in LBDDS.

Main Methods:

• Utilized Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) for in-silico API partitioning predictions.
• Investigated LBDDS composed of up to four components, including APIs like cinnarizine or ibuprofen.
• Experimentally validated the PC-SAFT predictions under various LBDDS/water ratios and excipient conditions.

Main Results:

• PC-SAFT accurately predicted API partitioning behavior in LBDDS.
• The study explored the influence of LBDDS composition and mixing ratios on API partitioning.
• PC-SAFT successfully predicted potential API crystallization and identified safe API loading limits.

Conclusions:

• PC-SAFT is a powerful tool for predicting API partitioning in LBDDS during in-vitro testing.
• This in-silico approach enables rapid screening of potential LBDDS formulations.
• It helps identify suitable API loads to prevent crystallization, optimizing formulation development.