Mechanistic Insights of Phenobarbital-Mediated Activation of Human but Not Mouse Pregnane X Receptor.

Phenobarbital (PB), a broadly used antiseizure drug, was the first to be characterized as an inducer of cytochrome P450 by activation of the constitutive androstane receptor (CAR). Although PB is recognized as a conserved CAR activator among species via a well-documented indirect activation mechanism, conflicting results have been reported regarding PB regulation of the pregnane X receptor (PXR), a sister receptor of CAR, and the underlying mechanisms remain elusive. Here, we show that in a human CAR (hCAR)-knockout (KO) HepaRG cell line, PB significantly induces the expression of CYP2B6 and CYP3A4, two shared target genes of hCAR and human PXR (hPXR). In human primary hepatocytes and hCAR-KO HepaRG cells, PB-induced expression of CYP3A4 was markedly repressed by genetic knockdown or pharmacological inhibition of hPXR. Mechanistically, PB concentration dependently activates hPXR but not its mouse counterpart in cell-based luciferase assays. Mammalian two-hybrid assays demonstrated that PB selectively increases the functional interaction between the steroid receptor coactivator-1 and hPXR but not mouse PXR. Moreover, surface plasmon resonance binding affinity assay showed that PB directly binds to the ligand binding domain of hPXR (KD = 1.42 × 10-05). Structure-activity analysis further revealed that the amino acid tryptophan-299 within the ligand binding pocket of hPXR plays a key role in the agonistic binding of PB and mutation of tryptophan-299 disrupts PB activation of hPXR. Collectively, these data reveal that PB, a selective mouse CAR activator, activates both hCAR and hPXR, and provide novel mechanistic insights for PB-mediated activation of hPXR.

Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective.

Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small-molecule drugs. Understanding the mechanisms of drug-transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter-mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life-threatening drug-drug interactions (DDIs).5,6.

Molecular Basis of Metabolism-Mediated Conversion of PK11195 from an Antagonist to an Agonist of the Constitutive Androstane Receptor.

The constitutive androstane receptor (CAR) plays an important role in xenobiotic metabolism, energy homeostasis, and cell proliferation. Antagonism of the CAR represents a key strategy for studying its function and may have potential clinical applications. However, specific human CAR (hCAR) antagonists are limited and conflicting data on the activity of these compounds have been reported. 1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinolinecarboxamide (PK11195), a typical peripheral benzodiazepine receptor ligand, has been established as a potent hCAR deactivator in immortalized cells; whether it inhibits hCAR activity under physiologically relevant conditions remains unclear. Here, we investigated the effects of PK11195 on hCAR in metabolically competent human primary hepatocytes (HPH) and HepaRG cells. We show that although PK11195 antagonizes hCAR in HepG2 cells, it induces the expression of CYP2B6 and CYP3A4, targets of hCAR and the pregnane X receptor (PXR), in HPH, HepaRG, and PXR-knockout HepaRG cells. Utilizing a HPH-HepG2 coculture model, we demonstrate that inclusion of HPH converts PK11195 from an antagonist to an agonist of hCAR, and such conversion was attenuated by potent CYP3A4 inhibitor ketoconazole. Metabolically, we show that the N-desmethyl metabolite is responsible for PK11195-mediated hCAR activation by facilitating hCAR interaction with coactivators and enhancing hCAR nuclear translocation in HPHs. Structure-activity analysis revealed that N-demethylation alters the interaction of PK11195 with the binding pocket of hCAR to favor activation. Together, these results indicate that removal of a methyl group switches PK11195 from a potent antagonist of hCAR to an agonist in HPH and highlights the importance of physiologically relevant metabolism when attempting to define the biologic action of small molecules.

Identification of novel MRP3 inhibitors based on computational models and validation using an in vitro membrane vesicle assay.

INTRODUCTION: Multidrug resistance-associated protein 3 (MRP3), an efflux transporter on the hepatic basolateral membrane, may function as a compensatory mechanism to prevent the accumulation of anionic substrates (e.g., bile acids) in hepatocytes. Inhibition of MRP3 may disrupt bile acid homeostasis and is one hypothesized risk factor for the development of drug-induced liver injury (DILI). Therefore, identifying potential MRP3 inhibitors could help mitigate the occurrence of DILI. METHODS: Bayesian models were developed using MRP3 transporter inhibition data for 86 structurally diverse drugs. The compounds were split into training and test sets of 57 and 29 compounds, respectively, and six models were generated based on distinct inhibition thresholds and molecular fingerprint methods. The six Bayesian models were validated against the test set and the model with the highest accuracy was utilized for a virtual screen of 1470 FDA-approved drugs from DrugBank. Compounds that were predicted to be inhibitors were selected for in vitro validation. The ability of these compounds to inhibit MRP3 transport at a concentration of 100µM was measured in membrane vesicles derived from stably transfected MRP3-over-expressing HEK-293 cells with [3H]-estradiol-17ß-d-glucuronide (E217G; 10µM; 5min uptake) as the probe substrate. RESULTS: A predictive Bayesian model was developed with a sensitivity of 73% and specificity of 71% against the test set used to evaluate the six models. The area under the Receiver Operating Characteristic (ROC) curve was 0.710 against the test set. The final selected model was based on compounds that inhibited substrate transport by at least 50% compared to the negative control, and functional-class fingerprints (FCFP) with a circular diameter of six atoms, in addition to one-dimensional physicochemical properties. The in vitro screening of predicted inhibitors and non-inhibitors resulted in similar model performance with a sensitivity of 64% and specificity of 70%. The strongest inhibitors of MRP3-mediated E217G transport were fidaxomicin, suramin, and dronedarone. Kinetic assessment revealed that fidaxomicin was the most potent of these inhibitors (IC50=1.83±0.46µM). Suramin and dronedarone exhibited IC50 values of 3.33±0.41 and 47.44±4.41µM, respectively. CONCLUSION: Bayesian models are a useful screening approach to identify potential inhibitors of transport proteins. Novel MRP3 inhibitors were identified by virtual screening using the selected Bayesian model, and MRP3 inhibition was confirmed by an in vitro transporter inhibition assay. Information generated using this modeling approach may be valuable in predicting the potential for DILI and/or MRP3-mediated drug-drug interactions.

Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors.

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.