Conformational Effects on the Passive Membrane Permeability of Synthetic Macrocycles

Macrocyclic compounds (MCs) can have complex conformational properties that affect pharmacologically important behaviors such as membrane permeability. We measured the passive permeability of 3600 diverse nonpeptidic MCs and used machine learning to analyze the results. Incorporating selected properties based on the three-dimensional (3D) conformation gave models that predicted permeability with Q(2) = 0.81. A biased spatial distribution of polar versus nonpolar regions was particularly important for good permeability, consistent with a mechanism in which the initial insertion of nonpolar portions of a MC helps facilitate the subsequent membrane entry of more polar parts. We also examined effects on permeability of 800 substructural elements by comparing matched molecular pairs. Some substitutions were invariably beneficial or invariably deleterious to permeability, while the influence of others was highly contextual. Overall, the work provides insights into how the permeability of MCs is influenced by their 3D conformational properties and suggests design hypotheses for achieving macrocycles with high membrane permeability.

Automated summary

This study measured the passive permeability of 3600 diverse macrocyclic compounds and used machine learning to analyze the results. It found that models incorporating selected properties based on the three-dimensional conformation could predict permeability with a high accuracy. The biased spatial distribution of polar versus nonpolar regions was found to be important for good permeability. Additionally, the study examined the effects of substructural elements on permeability, providing insights for designing macrocycles with high membrane permeability.