The pregnane X receptor (PXR) is an important transcriptional regulator of the expression of xenobiotic metabolism and transporter genes. The receptor is promiscuous, binding many structural classes of molecules that act as agonists at the ligand-binding domain, triggering up-regulation of genes, increasing the metabolism and excretion of therapeutic agents, and causing drug-drug interactions. It has been suggested that human PXR antagonists represent a means to counteract such interactions. Several azoles have been hypothesized to bind the activation function-2 (AF-2) surface on the exterior of PXR when agonists are concurrently bound in the ligand-binding domain. In the present study, we have derived novel computational models for PXR agonists using different series of imidazoles, steroids, and a set of diverse molecules with experimental PXR agonist binding data. We have additionally defined a novel pharmacophore for the steroidal agonist site. All agonist pharmacophores showed that hydrophobic features are predominant. In contrast, a qualitative comparison with the corresponding PXR antagonist pharmacophore models using azoles and biphenyls showed that they are smaller and hydrophobic with increased emphasis on hydrogen bonding features. Azole antagonists were docked into a proposed hydrophobic binding pocket on the outer surface at the AF-2 site and fitted comfortably, making interactions with key amino acids involved in charge clamping. Combining computational and experimental data for different classes of molecules provided strong evidence for agonists and antagonists binding distinct regions on PXR. These observations bear significant implications for future discovery of molecules that are more selective and potent antagonists.

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.

BackgroundLaboratory tests for routine drug of abuse and toxicology (DOA/Tox) screening, often used in emergency medicine, generally utilize antibody-based tests (immunoassays) to detect classes of drugs such as amphetamines, barbiturates, benzodiazepines, opiates, and tricyclic antidepressants, or individual drugs such as cocaine, methadone, and phencyclidine. A key factor in assay sensitivity and specificity is the drugs or drug metabolites that were used as antigenic targets to generate the assay antibodies. All DOA/Tox screening immunoassays can be limited by false positives caused by cross-reactivity from structurally related compounds. For immunoassays targeted at a particular class of drugs, there can also be false negatives if there is failure to detect some drugs or their metabolites within that class.MethodsMolecular similarity analysis, a computational method commonly used in drug discovery, was used to calculate structural similarity of a wide range of clinically relevant compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target ('antigenic') molecules of DOA/Tox screening tests. These results were compared with cross-reactivity data in the package inserts of immunoassays marketed for clinical testing. The causes for false positives for phencyclidine and tricyclic antidepressant screening immunoassays were investigated at the authors' medical center using gas chromatography/mass spectrometry as a confirmatory method.ResultsThe results illustrate three major challenges for routine DOA/Tox screening immunoassays used in emergency medicine. First, for some classes of drugs, the structural diversity of common drugs within each class has been increasing, thereby making it difficult for a single assay to detect all compounds without compromising specificity. Second, for some screening assays, common 'out-of-class' drugs may be structurally similar to the target compound so that they account for a high frequency of false positives. Illustrating this point, at the authors' medical center, the majority of positive screening results for phencyclidine and tricyclic antidepressants assays were explained by out-of-class drugs. Third, different manufacturers have adopted varying approaches to marketed immunoassays, leading to substantial inter-assay variability.ConclusionThe expanding structural diversity of drugs presents a difficult challenge for routine DOA/Tox screening that limit the clinical utility of these tests in the emergency medicine setting.

The MI-QSAR models indicate that the blood-brain barrier partitioning process can be reliably described for structurally diverse molecules provided interactions of the molecule with the phospholipids-rich regions of cellular membranes are explicitly considered.

BACKGROUND:Immunoassays used for routine drug of abuse (DOA) and toxicology screening may be limited by cross-reacting compounds able to bind to the antibodies in a manner similar to the target molecule(s). To date, there has been little systematic investigation using computational tools to predict cross-reactive compounds.