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Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug–drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.78 using high throughput percentage inhibition data [J. Med. Chem.2013, 56(3), 781–795] which we aimed to improve upon here using a combined fingerprint and physics-based approach. To this end, we collected 225 publicly available compounds with pIC50 values against MATE1. Subsequently, on the one hand, we performed a physics-based approach using an Alpha-Fold protein structure, from which we obtained MM–GB/SA scores for those compounds. On the other hand, we built Random Forest (RF) and message passing neural network models using extended-connectivity fingerprints with radius 4 (ECFP4) and chemical structures as graphs, respectively, which also included MM–GB/SA scores as input variables. In a five-fold cross-validation with a separate test set, we found that the best predictivity for the hold-out test was observed in the RF model (including ECFP4 and MM–GB/SA data) with an ROC-AUC of 0.833 ± 0.036; while that of the MM–GB/SA regression model was 0.742. However, the MM–GB/SA model did not show a dependency of the performance on the particular chemical space being predicted. Additionally, via structural interaction fingerprint analysis, we identified interacting residues with inhibitor as identical for those with noninhibitors, including substrates, such as Gln49, Trp274, Tyr277, Tyr299, Ile303, and Tyr306. The similar binding modes are consistent with the observed similar IC50 value inhibitor when using different substrates experimentally, and practically, this can release the experimental scientists from bothering of selecting substrates for MATE1. Hence, we were able to build highly predictive classification models for MATE1 inhibitory activity with both ECFP4 and MM–GB/SA score as input features, which is fit-for-purpose for use in the drug discovery process.

Drug absorption from the gastrointestinal tract is often restricted by efflux transport by P-glycoprotein (P-gp) and metabolism by CYP3A4. Both localize in the epithelial cells, and thus, their activities are directly affected by the intracellular drug concentration, which should be regulated by the ratio of permeability between apical (A) and basal (B) membranes. In this study, using Caco-2 cells with forced expression of CYP3A4, we assessed the transcellular permeation of A-to-B and B-to-A directions and the efflux from the preloaded cells to both sides of 12 representative P-gp or CYP3A4 substrate drugs and obtained the parameters for permeabilities, transport, metabolism, and unbound fraction in the enterocytes (fent) using simultaneous and dynamic model analysis. The membrane permeability ratios for B to A (RBA) and fentvaried by 8.8-fold and by more than 3000-fold, respectively, among the drugs. The RBAvalues for digoxin, repaglinide, fexofenadine, and atorvastatin were greater than 1.0 (3.44, 2.39, 2.27, and 1.90, respectively) in the presence of a P-gp inhibitor, thus suggesting the potential involvement of transporters in the B membrane. The Michaelis constant for quinidine for P-gp transport was 0.077 µM for the intracellular unbound concentration. These parameters were used to predict overall intestinal availability (FAFG) by applying an intestinal pharmacokinetic model, advanced translocation model (ATOM), in which permeability of A and B membranes accounted separately. The model predicted changes in the absorption location for P-gp substrates according to its inhibition, and FAFGvalues of 10 of 12 drugs, including quinidine at varying doses, were explained appropriately.SIGNIFICANCE STATEMENTPharmacokinetics has improved predictability by identifying the molecular entities of metabolism and transport and by using mathematical models to appropriately describe drug concentrations at the locations where they act. However, analyses of intestinal absorption so far have not been able to accurately consider the concentrations in the epithelial cells where P-glycoprotein and CYP3A4 exert effects. In this study, the limitation was removed by measuring the apical and basal membrane permeability separately and then analyzing these values using new appropriate models.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood–brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration–time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration–time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silicoprediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration–time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration–time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration–time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration–time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration–time profiles.