Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based...

Area of Science:

• Materials Science
• Pharmaceutical Sciences
• Analytical Chemistry

Background:

• Mesoporous silica is a key material in bio-enabling formulations.
• Predictive tools for drug-silica interactions in preformulation are lacking.
• Formulators need efficient methods to assess drug-silica affinity with minimal material.

Purpose of the Study:

• To develop a chromatographic method for ranking drug-silica affinity.
• To establish a predictive tool for drug interactions with mesoporous silica carriers.
• To guide preformulation strategies for mesoporous silica-based drug delivery systems.

Main Methods:

• Developed a hydrophilic liquid interaction chromatography (HILIC) screening method using a silica stationary phase.
• Calculated molecular descriptors for 52 drugs to analyze retention times.
• Employed a tree-based machine learning algorithm to identify critical parameters for drug-silica affinity.

Main Results:

• Identified distribution coefficient (LogD), Kappa1 shape descriptor, and number of conjugated bonds (NCB) as key parameters for silica affinity.
• Evaluated an amine-modified HILIC column to simulate surface-modified silica.
• Classification tree analysis revealed Abraham's hydrogen bonding acidity, NCB, and pKa as determinants for modified silica affinity.

Conclusions:

• The developed chromatographic method provides a useful classification of drug affinity (low, moderate, high) to mesoporous silica.
• This approach aids in understanding drug release from mesoporous silica formulations.
• The study highlights the potential of this method for future research in drug formulation development.