Solubilization of drugs using beta-cyclodextrin: Experimental data and modeling

Area of Science:

• Pharmaceutical Sciences
• Medicinal Chemistry
• Computational Chemistry

Background:

• Poor physicochemical properties, particularly low solubility, are a major cause of drug candidate failure in development.
• Solubility is critical across drug discovery and development stages.
• Cyclodextrin complexation, especially with beta-cyclodextrin (βCD), is a known strategy to enhance drug solubility.

Purpose of the Study:

• To develop mechanistic quantitative structure-property relationship (QSPR) models for predicting drug solubility enhancement in the presence of beta-cyclodextrin (βCD).
• To identify key molecular descriptors influencing βCD-mediated solubilization.

Main Methods:

• Experimental determination and literature collection of drug solubility enhancement data in the presence of 10mM βCD at 25°C.
• Development of two binary logistic regression QSPR models using drug structural properties.
• Identification of predictive molecular descriptors.

Main Results:

• Two QSPR models were developed with predictive accuracy exceeding 80%.
• Polar surface area and excess molar refraction were identified as key parameters for predicting solubilization by βCD.
• Inclusion of descriptors related to hydrophobicity and hydrogen bonding capability further improved model accuracy.

Conclusions:

• Mechanistic QSPR models can accurately predict drug solubility enhancement by βCD.
• Molecular structural properties, specifically polar surface area and excess molar refraction, are crucial for βCD complexation and solubilization.
• These models offer a valuable tool for early-stage drug discovery to select candidates with favorable solubility profiles.