Article Figures & Data
Figures
- Fig. 1.
(A) Classification of the inhibitors used in development of the MRP4 models. Forty-five drugs were MRP4 inhibitors only and 31 drugs were BSEP inhibitors only, whereas 26 molecules inhibited both MRP4 and BSEP. (B) Of the compounds in (A), 14 MRP4 inhibitors were cholestatic, whereas only one BSEP inhibitor was identified as a cholestatic drug. Of the 26 compounds classified as both MRP4 and BSEP inhibitors, 15 (58%) were clinically identified as cholestatic.
- Fig. 2.
(A) MRP4: PCA of the training and test set compounds (257 in total) were selected such that they occupy similar areas of the PCA plot. The PCA among the training and test set compounds was generated with the following properties: ALogP, molecular weight, molecular fractional polar surface area, number of rings, aromatic rings, rotatable bonds, hydrogen bond acceptors, and hydrogen bond donors. The first principal component explains 0.366 of total variance, and the second principal component explains 0.272 of total variance. When combined, these explain 0.638 of total variance. The principal components are linear combinations of original descriptors. The dominate descriptors in the principal components are determined by the product of the descriptor coefficient while accounting for the magnitude of the descriptor. The first principal component is dominated by molecular weight, number of hydrogen bond acceptors, and number of rotatable bonds. The second principal component is dominated by the molecular fractional polar surface area. Component 1 = −3.8514 + 0.17609 * [ALogP] + 0.0029942 * [Molecular_Weight] + 0.19241 * [Num_H_Donors] + 0.12966 * [Num_H_Acceptors] + 0.11058 * [Num_RotatableBonds] + 0.21601 * [Num_Rings] + 0.26649 * [Num_AromaticRings] − 0.91018 * [Molecular_FractionalPolarSurfaceArea]. Component 2 = −0.91763 − 0.22028 * [ALogP] + 0.00060715 * [Molecular_Weight] + 0.30038 * [Num_H_Donors] + 0.1113 * [Num_H_Acceptors] + 0.0097512 * [Num_RotatableBonds] − 0.15452 * [Num_Rings] − 0.34347 * [Num_AromaticRings] + 4.3042 * [Molecular_FractionalPolarSurfaceArea]. (B) BSEP: PCA analysis of the training and test sets. The first and second principal components accounted for 0.391 and 0.344 of total variance, respectively. Together, they explain 0.735 of total variance. The first principal component (x-axis) is governed by the number of hydrogen bond donors/acceptors and number of rings, whereas the second principal component (y-axis) is governed by lipophilicity and the number of aromatic rings. Both principal components are strongly influenced by the fractional polar surface area. Component 1 = −4.0005 + 0.035399 * [ALogP] + 0.0031256 * [Molecular_Weight] + 0.16608 * [Num_H_Donors] + 0.14138 * [Num_H_Acceptors] + 0.11881 * [Num_RotatableBonds] + 0.23456 * [Num_Rings] + 0.23836 * [Num_AromaticRings] + 0.48987 * [Molecular_FractionalPolarSurfaceArea]. Component 2 = −0.12988 + 0.2367 * [ALogP] + 0.00047919 * [Molecular_Weight] − 0.20734 * [Num_H_Donors] − 7.2971e-002 * [Num_H_Acceptors] + 0.019248 * [Num_RotatableBonds] + 0.16242 * [Num_Rings] + 0.31811 * [Num_AromaticRings] − 3.6221 * [Molecular_FractionalPolarSurfaceArea]. PCA analysis comparing the training set of MRP4 (C) and BSEP (D) to the DrugBank database of U.S. Food and Drug Administration–approved drugs.
- Fig. 3.
Favorable and unfavorable molecular features for interactions with MRP4. Each feature is a fragment-like fingerprint, up to six bond lengths in diameter, which occurs within the larger parent molecule. The squiggle and asterisks indicate that the bond extends further but does not specify the atom type. The favorable features or good features are labeled G1–G5, and the unfavorable features or bad features are labeled B1–B5. A feature is considered good if it frequently occurs within compounds that were classified as inhibitors and bad if it frequently occurs in compounds that are noninhibitors. The large integer after the colon is the unique hash identifier for the shown fingerprint. The Bayesian score is the normalized probability assigned to that feature.
- Fig. 4.
Favorable and unfavorable molecular features for interactions with BSEP.
- Fig. 5.
Pharmacophore model of inhibitors of MRP4-mediated transport of DHEAS. (A) The pharmacophore model with the measured distances between the three features. (B) The pharmacophore model aligned with chemical groups of two drugs from the training set: clobetasol propionate (orange) and finasteride (lavender). Yellow spheres represent hydrophobic features, and the red sphere represents a hydrogen bond acceptor. On the stick model, red represents oxygen atoms, blue represents nitrogen atoms, green represents halogen atoms, and the rest are carbons. Both hydrophobic features align with methyl groups, and the hydrogen bond acceptor aligns with a ketone group. Hydrogen atoms are not displayed for clarity.
- Fig. 6.
ROC curve of pharmacophore model of MRP4 inhibitors from virtually screening the test set (N = 77 compounds).
- Fig. 7.
Structural alignment of glucocorticoids clobetasol propionate (orange) and dexamethasone (gray). Clobetasol propionate, a potent MRP4 inhibitor, inhibits MRP4-mediated transport of DHEAS by 101 ± 23%. In contrast, dexamethasone exhibits no significant inhibitory effect (5 ± 34% inhibition). The orange circles indicate identical chemical groups in proximity with each other. On the stick model, red represents oxygen atoms, green represents halogen atoms, and the rest represents carbon atoms.
- Fig. 8.
MRP4: comparison of calculated Log P of compounds classified as inactive (< 21% MRP4 inhibitory activity; n = 37) compared with those classified as active (≥ 21% MRP4 inhibitory activity; n = 50). The mean and median log P values of the inactives are 0.38 and 0.69, respectively, and 3.64 and 3.84, respectively, for the actives.
- Fig. 9.
DHEAS, an MRP4 substrate, and felbinac, an MRP4 inhibitor, aligned with the MRP4 inhibitor pharmacophore. Both compounds are also depicted with their individual pharmacophore, which shows all possible intermolecular interactions. (A) DHEAS aligned to the MRP4 pharmacophore. (B) DHEAS pharmacophore showing all possible intermolecular interactions. (C) Felbinac aligned to the MRP4 pharmacophore. (D) Felbinac pharmacophore showing all possible interactions. Yellow spheres represent hydrophobic features, red spheres represent hydrogen bond acceptor features, the red star represents a negatively ionizable feature, and the purple torus represents an aromatic ring feature. On the stick models, red represents oxygen atoms, yellow represents phosphorus atoms, and the rest represents carbon atoms. Hydrogen atoms are not displayed for clarity.
Tables
Bayesian models MRP4inhib-ECFP_6 MRP4inhib-FCFP_6 BSEPinhib-ECFP_6 BSEPinhib-FCFP_6 Two-dimensional fingerprints ECFP_6 FCFP_6 ECFP_6 FCFP_6 10-fold XV ROC AUCa 0.816 0.793 0.750 0.759 TP/FN/FP/TNa 33/1/1/22 33/1/1/22 43/0/3/125 43/0/5/123 External validationb 0.819 0.838 0.845 0.871 TP/FN/FP/TNb 8/9/1/11 10/7/2/10 18/4/15/49 17/5/10/54 SE (%)b 47.1 58.8 81.8 77.3 SP (%)b 91.7 83.3 76.7 84.4 Q (%)b 65.5 69.0 77.9 82.6 MCCb 0.4123 0.4216 0.5238 0.5796 FN, false negative; FP, false positive; Q, overall prediction accuracy; SE, sensitivity; SP, specificity; TN; true negative; TP, true positive.
↵a XV ROC AUC based on training set compounds (green shaded region).
↵b Predictive performance validation by test set compounds (blue shaded region) (Ung et al., 2007; Khandelwal et al., 2008). SE = TP/(TP + FN); SP = TN/(TN + FP); Q = (TP + TN)/(TP + TN + FP + FN); MCC = [(TP * TN) – (FN * FP)]/[(TP + FP)(TP + FN)(TN +FN)(TN+FP)]1/2.
Data Supplement
Files in this Data Supplement:
- Supplemental Data -
Supplementary Table 1 - These two tables contain the names of the drugs in the MRP4 dataset and BSEP dataset, PubChem ID or CHEMBL ID if available, the inhibition data, and the compound's classification for the model
Supplemental Figure 1 - The BSEP pharmacophore and the assay substrate aligned to the BSEP pharmacophore
Supplemental Figure 2 - Favorable and unfavorable molecular features for interactions with MRP4 and BSEP
- Supplemental Data -
In this issue
Computational Modeling of MRP4 and BSEP to Predict DILI
