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

Abstract

Mesoporous silica has emerged as a promising component in bio-enabling formulation strategy. However, there is currently a lack of predictive tools for assessing drug-silica interactions in a preformulation phase, when formulators only have minimal material to guide them. This study proposes a solution: a chromatographic method to rank apparent drug-silica affinity for mesoporous formulations. Using a dataset of 52 drugs, a hydrophilic liquid interaction chromatography (HILIC) screening method was developed, with a stationary silica phase to simulate the drug carrier. Molecular descriptors were calculated for various compounds to analyze HILIC retention times using a tree-based machine learning algorithm. For silica affinity, the distribution coefficient (LogD), the molecular shape descriptor Kappa1, and the number of conjugated bonds (NCB) were identified as possible critical parameters. Additionally, an amine-modified HILIC column was evaluated to simulate a surface-modified silica carrier. The classification tree analysis revealed that Abraham's hydrogen bonding acidity, the NCB and the pKa were determinants for a qualitative assessment of drug affinity to the modified silica. The classification into low, moderate, and high affinity to the stationary phase appeared to be useful in understanding drug release from mesoporous silica formulations, highlighting its potential for future research.