Selecting Excipients Forming Therapeutic Deep Eutectic Systems-A Mechanistic Approach

Area of Science:

• Pharmaceutical Science
• Physical Chemistry
• Materials Science

Background:

• Many new active pharmaceutical ingredients (APIs) exhibit poor water solubility, limiting their bioavailability.
• Therapeutic deep eutectic systems (THEDES) offer a promising formulation strategy to enhance API solubility and bioavailability.
• Current methods for selecting excipients for THEDES rely heavily on time-consuming and costly trial-and-error approaches due to a lack of mechanistic understanding.

Purpose of the Study:

• To develop a predictive thermodynamic model for identifying suitable excipients for THEDES.
• To reduce the experimental effort required for API/excipient selection by focusing on melting properties and intermolecular interactions.
• To enable more efficient and tailor-made drug formulations.

Main Methods:

• Utilized a predictive thermodynamic model (UNIFAC(Do)) incorporating intermolecular interactions.
• Assessed API and excipient melting properties (melting temperature and enthalpy).
• Experimentally validated predictions using differential scanning calorimetry (DSC) with model APIs (lidocaine, ibuprofen, phenylacetic acid) and excipients (thymol, vanillin, lauric acid, para-toluic acid, benzoic acid, cinnamic acid).

Main Results:

• The thermodynamic model successfully predicted the formation of THEDES for various API/excipient combinations.
• Experimental validation using DSC confirmed the model's predictions.
• The study demonstrated a significant reduction in experimental effort for identifying suitable API/excipient pairings.

Conclusions:

• Thermodynamic modeling provides a powerful tool to accelerate the identification of excipients for THEDES.
• This approach can lead to more efficient development of customized drug formulations with improved solubility and bioavailability.
• The findings pave the way for rational design of drug delivery systems, moving beyond heuristic methods.