Impact of Hydrogen Bonding on P‑Glycoprotein Efflux Transport as Revealed by Evaluation of a De Novo Prediction Model

Drug efflux transport by the permeability glycoprotein
(P-gp) frequently
diminishes drug efficacy, making the prediction of the P-gp efflux
ratio critically significant. Most contemporary computational methods
rely on binary predictions through machine learning. However, the
accuracy and applicability of these predictions are heavily influenced
by the training data sets. In contrast, we evaluated the validity
of a de novo prediction model, which employs solvation free energies
both within and outside the P-gp-binding pocket. Utilizing our in-house
data set of 397 compounds, we discovered that this model struggles
to predict a certain class of compounds accurately and that such outliers
often exhibit favorable solvation free energy, with an increased number
of hydrogen bond donors compared to other compounds investigated.
Further examination of the functional groups in these compounds highlighted
the significance of their specific interactions with P-gp. Considering
these specific drug-P-gp interactions could enhance the accuracy of
mechanism-based de novo prediction methods.