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

Abstract

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 (R 2 = 0.941) and in vivo K p,brain ratio (mdr1a/1b KO/WT) in mice (R 2 = 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.

Calculation of an Apical Efflux Ratio from P-Glycoprotein (P-gp) In Vitro Transport Experiments Shows an Improved Correlation with In Vivo Cerebrospinal Fluid Measurements in Rats: Impact on P-gp Screening and Compound Optimization

“…This difference is more pronounced for poorly permeable compounds, as they are more greatly affected by the active contribution of P-gp (equations 10 and 13). Although uni-directional measurements similar to AP-ER have been described previously (Thiel-Demby et al, 2004;Ohashi et al, 2019) and are able to differentiate strong and weak P-gp substrates, these studies also showed correlation between estimates based on uni-directional measurements and classical ER, and hence superiority of uni-directional measurements over bi-directional models was not described.…”

Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

“…It was previously reported that PCA could show the distribution of chemical space in the dataset. 21 Components 1, 2, and 3 explained 62.3, 12.0, and 8.0% of the variance, respectively. Figure 1 shows that the compounds were effectively separated into the training and test datasets.…”

Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans

“…The compounds were separated into the training and test sets for the construction and subsequent verification of the prediction models. To ensure unbiased segregation, the PCA analysis was conducted based on 11 representative molecular descriptors, which are generally considered important for synthetic expansion [19,20]. As shown in Fig.…”

“…of 2:1. To investigate applicability domain, 11 molecular parameters were used as reported previously [19] with JMP Pro PCA (Principal component analysis) [19].…”