Development of Simplified in Vitro P‑Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P‑Glycoprotein

For
efficient drug discovery and screening, it is necessary to
simplify P-glycoprotein (P-gp) substrate assays and to provide in
silico models that predict the transport potential of P-gp. In this
study, we developed a simplified in vitro screening method to evaluate
P-gp substrates by unidirectional membrane transport in P-gp-overexpressing
cells. The unidirectional flux ratio positively correlated with parameters
of the conventional bidirectional P-gp substrate assay (R2 = 0.941) and in vivo Kp,brain ratio (mdr1a/1b KO/WT) in mice (R2 =
0.800). Our in vitro P-gp substrate assay had high reproducibility
and required approximately half the labor of the conventional method.
We also constructed regression models to predict the value of P-gp-mediated
flux and three-class classification models to predict P-gp substrate
potential (low-, medium-, and high-potential) using 2397 data entries
with the largest data set collected under the same experimental conditions.
Most compounds in the test set fell within two- and three-fold errors
in the random forest regression model (71.3 and 88.5%, respectively).
Furthermore, the random forest three-class classification model showed
a high balanced accuracy of 0.821 and precision of 0.761 for the low-potential
classes in the test set. We concluded that the simplified in vitro
P-gp substrate assay was suitable for compound screening in the early
stages of drug discovery and that the in silico regression model and
three-class classification model using only chemical structure information
could identify the transport potential of compounds including P-gp-mediated
flux ratios. Our proposed method is expected to be a practical tool
to optimize effective central nervous system (CNS) drugs, to avoid
CNS side effects, and to improve intestinal absorption.