Attenuating pregnane X receptor (PXR) activation: A molecular modelling approach

Discovery of 4,4-Disubstituted Quinazolin-2-ones as T-Type Calcium Channel Antagonists

A novel series of quinazolinone T-type calcium channel antagonists have been prepared and evaluated using in vitro and in vivo assays. Optimization of the screening hit 3 by modifications of the 3- and 4-positions of the quinazolinone ring afforded potent and selective antagonists that displayed in vivo central nervous system efficacy in epilepsy and tremor models, as well as significant effects on rat active wake as measured by electrocorticogram.

SAR of PXR transactivation in benzimidazole-based IGF-1R kinase inhibitors

The SAR of PXR transactivation by 3-(benzimidazol-2-yl)-pyridine-2-one based ATP competitive inhibitors of Insulin-like Growth Factor 1 Receptor kinase (IGF-1R) is discussed. Compounds without PXR transactivation, with in vivo antitumor activity, reduced protein binding and improved oral exposure are presented.

Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

Promiscuous proteins generally bind a large array of diverse ligand structures. This may be facilitated by a very large binding site, multiple binding sites, or a flexible binding site that can adjust to the size of the ligand. These aspects also increase the complexity of predicting whether a molecule will bind or not to such proteins which frequently function as exogenous compound sensors to respond to toxic stress. For example, transporters may prevent absorption of some molecules, and enzymes may convert them to more readily excretable compounds (or alternatively activate them prior to further clearance by other detoxification enzymes). Nuclear hormone receptors may respond to ligands and then affect downstream gene expression to upregulate both enzymes and transporters to increase the clearance for the same or different molecules. We have assessed the ability of many different ligand-based and structure-based computational approaches to model and predict the activation of human PXR by steroidal compounds. We find the most effective computational approach to identify potential steroidal PXR agonists which are clinically relevant due to their widespread use in clinical medicine and the presence of mimics in the environment.

Understanding nuclear receptors using computational methods

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

Preferential physical and functional interaction of pregnane X receptor with the SMRTalpha isoform

The silencing mediator for retinoid and thyroid hormone receptors (SMRT) serves as a platform for transcriptional repression elicited by several steroid/nuclear receptors and transcription factors. SMRT exists in two major splicing isoforms, alpha and tau, with SMRTalpha containing only an extra 46-amino acid sequence inserted immediately downstream from the C-terminal corepressor motif. Little is known about potential functional differences between these two isoforms. Here we show that the pregnane X receptor (PXR) interacts more strongly with SMRTalpha than with SMRTtau both in vitro and in vivo. It is interesting that the PXR-SMRTalpha interaction is also resistant to PXR ligand-induced dissociation, in contrast to the PXR-SMRTtau interaction. SMRTalpha consistently inhibits PXR activity more efficiently than does SMRTtau in transfection assays, although they possess comparable intrinsic repression activity and association with histone deacetylase. We further show that the mechanism for the enhanced PXR-SMRTalpha interaction involves both the 46-amino acid insert and the C-terminal corepressor motif. In particular, the first five amino acids of the SMRTalpha insert are essential and sufficient for the enhanced binding of SMRTalpha to PXR. Furthermore, we demonstrate that Tyr2354 and Asp2355 residues of the SMRTalpha insert are most critical for the enhanced interaction. In addition, expression data show that SMRTalpha is more abundantly expressed in most human tissues and cancer cell lines, and together these data suggest that SMRTalpha may play a more important role than SMRTtau in the negative regulation of PXR.

Discovery and evaluation of 4-(2-(4-chloro-1H-pyrazol-1-yl)ethylamino)-3-(6-(1-(3-fluoropropyl)piperidin-4-yl)-4-methyl-1H-benzo[d]imidazol-2-yl)pyridin-2(1H)-one (BMS-695735), an orally efficacious inhibitor of insulin-like growth factor-1 receptor kinase with broad spectrum in vivo antitumor activity

We previously reported that 1 (BMS-536924), a benzimidazole inhibitor of the insulin-like growth factor-1 receptor, had demonstrated in vivo antitumor activity. This lead compound was found to have potent CYP3A4 inhibition, CYP3A4 induction mediated by PXR transactivation, poor aqueous solubility, and high plasma protein binding. Herein we disclose the evolution of this chemotype to address these issues. This effort led to 10 (BMS-695735), which exhibits improved ADME properties, a low risk for drug-drug interactions, and in vivo efficacy in multiple xenograft models.

Machine learning methods and docking for predicting human pregnane X receptor activation

The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.

Current industrial practices in assessing CYP450 enzyme induction: preclinical and clinical

Induction of drug metabolizing enzymes, such as the cytochromes P450 (CYP) is known to cause drug-drug interactions due to increased elimination of co-administered drugs. This increased elimination may lead to significant reduction or complete loss of efficacy of the co-administered drug. Due to the significance of such drug interactions, many pharmaceutical companies employ screening and characterization models which predict CYP enzyme induction to avoid or attenuate the potential for drug interactions with new drug candidates. The most common mechanism of CYP induction is transcriptional gene activation. Activation is mediated by nuclear receptors, such as AhR, CAR, and PXR that function as transcription factors. Early high throughput screening models utilize these nuclear hormone receptors in ligand binding or cell-based transactivation/reporter assays. In addition, immortalized hepatocyte cell lines can be used to assess enzyme induction of specific drug metabolizing enzymes. Cultured primary human hepatocytes, the best established in vitro model for predicting enzyme induction and most accepted by regulatory agencies, is the predominant assay used to evaluate induction of a wide variety of drug metabolizing enzymes. These in vitro models are able to appropriately predict enzyme induction in patients when compared to clinical drug-drug interactions. Finally, transgenic animal models and the cynomolgus monkey have also been shown to recapitulate human enzyme induction and may be appropriate in vivo animal models for predicting human drug interactions.

A comprehensive in vitro and in silico analysis of antibiotics that activate pregnane X receptor and induce CYP3A4 in liver and intestine

We have investigated several in silico and in vitro methods to improve our ability to predict potential drug interactions of antibiotics. Our focus was to identify those antibiotics that activate pregnane X receptor (PXR) and induce CYP3A4 in human hepatocytes and intestinal cells. Human PXR activation was screened using reporter assays in HepG2 cells, kinetic measurements of PXR activation were made in DPX-2 cells, and induction of CYP3A4 expression and activity was verified by quantitative polymerase chain reaction, immunoblotting, and testosterone 6beta-hydroxylation in primary human hepatocytes and LS180 cells. We found that in HepG2 cells CYP3A4 transcription was activated strongly (> 10-fold) by rifampin and troleandomycin; moderately (> or = 7-fold) by dicloxacillin, tetracycline, clindamycin, griseofulvin, and (> or = 4-fold) erythromycin; and weakly (> 2.4-fold) by nafcillin, cefaclor, sulfisoxazole, and (> 2-fold) cefadroxil and penicillin V. Similar although not identical results were obtained in DPX-2 cells. CYP3A4 mRNA and protein expression were induced by these antibiotics to differing extents in both liver and intestinal cells. CYP3A4 activity was significantly increased by rifampin (9.7-fold), nafcillin and dicloxacillin (5.9-fold), and weakly induced (2-fold) by tetracycline, sufisoxazole, troleandomycin, and clindamycin. Multiple pharmacophore models and docking indicated a good fit for dicloxacillin and nafcillin in PXR. These results suggest that in vitro and in silico methods can help to prioritize and identify antibiotics that are most likely to reduce exposures of medications (such as oral contraceptive agents) which interact with enzymes and transporters regulated by PXR. In summary, nafcillin, dicloxacillin, cephradine, tetracycline, sulfixoxazole, erythromycin, clindamycin, and griseofulvin exhibit a clear propensity to induce CYP3A4 and warrant further clinical investigation.

4-Methyl-5-phenyl triazoles as selective inhibitors of 11beta-hydroxysteroid dehydrogenase type I

4-Methyl-5-phenyl-(1,2,4)-triazoles were identified as selective inhibitors of 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1). They were active in vitro and in an in vivo mouse pharmacodynamic (PD) model. The synthesis and structure activity relationships are presented.

A human immunodeficiency virus protease inhibitor is a novel functional inhibitor of human pregnane X receptor

Drug-drug interactions involving induction of cytochrome P450 enzymes (P450s) can lead to loss of drug efficacy. Certain drugs, particularly those used to treat mycobacterial and human immunodeficiency virus (HIV) infections, are especially prone to induce P450s. During studies to examine drug-interaction potential of compounds in cultured human hepatocytes, exposure with (S)-1-[(1S,3S,4S)-4-[(S)-2-(3-benzyl-2-oxo-imidazolidin-1-yl)-3,3-dimethyl-butyrylamino]-3-hydroxy-5-phenyl-1-(4-pyridin-2-yl-benzyl)-pentylcarbamoyl]-2,2-dimethyl-propyl-carbamic acid methyl ester (A-792611), a novel HIV protease inhibitor (PI) previously under investigation for the treatment of HIV infection, resulted in significant down-regulation of constitutive CYP3A4 expression. Furthermore, coadministration of A-792611 was found to attenuate CYP3A4 induction mediated by known inducers rifampin and efavirenz. A-792611 also attenuated the rifampin and ritonavir-mediated activation of the human pregnane X receptor (PXR) in luciferase reporter assays. Microarray analysis on cultured human hepatocytes revealed that A-792611 treatment down-regulated the expression of PXR target genes CYP3A4, CYP2B6, CYP2C8, and CYP2C9, whereas there was a lack of inductive effect observed in treated rat hepatocytes. A-792611 did not interact with other ligand-activated nuclear receptors that regulate P450 expression such as constitutive androstane receptor, farnesoid X receptor, vitamin D receptor, and peroxisome proliferator-activated receptor alpha. These data suggest that A-792611 is a functional and effective human PXR inhibitor. Among the class of HIV-PIs, which are typically PXR activators, A-792611 seems to have a unique property for PXR antagonism and could be a useful tool for studying nuclear receptor pathway regulation.