Aims: Clinically significant interactions with food occur for more than half of antiretroviral drugs. Different physiochemical properties deriving from the chemical structures of antiretroviral drugs may contribute to the variable food effect. Chemometric methods allow analysing a large number of interrelated variables concomitantly and visualizing correlations between them. We used a chemometric approach to determine the types of correlations among different features of antiretroviral drugs and food that may influence interactions.
Methods: Thirty-three antiretroviral drugs were analysed: ten nucleoside reverse transcriptase inhibitors, six non-nucleoside reverse transcriptase inhibitors, five integrase strand transfer inhibitors, ten protease inhibitors, one fusion inhibitor and one HIV maturation inhibitor. Input data for the analysis were collected from already published clinical studies, chemical records and calculations. We constructed a hierarchical partial least squares (PLS) model with three response parameters: postprandial change of time to reach maximum drug concentration (ΔTmax ), albumin binding (%) and logarithm of partition coefficient (logP). Predictor parameters were the first two principal components of principal component analysis (PCA) models for six groups of molecular descriptors.
Results: PCA models explained 64.4% to 83.4% of the variance of the original parameters (average: 76.9%), whereas the PLS model had four significant components and explained 86.2% and 71.4% of the variance in the sets of predictor and response parameters, respectively. We observed 58 significant correlations between ΔTmax , albumin binding (%), logP and constitutional, topological, hydrogen bonding and charge-based molecular descriptors.
Conclusions: Chemometrics is a useful and valuable tool for analysing interactions between antiretroviral drugs and food.
Keywords: antiretroviral; chemometrics; food; interaction; partial least squares; principal component analysis.
© 2023 British Pharmacological Society.