Development of pharmacophore-based classification model for activators of constitutive androstane receptor

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

Assessing the Selectivity of FXR, LXRs, CAR, and RORγ Pharmaceutical Ligands With Reporter Cell Lines

To characterize human nuclear receptor (NR) specificity of synthetic pharmaceutical chemicals we established stable cell lines expressing the ligand binding domains (LBDs) of human FXR, LXRα, LXRβ, CAR, and RORγ fused to the yeast GAL4 DNA binding domain (DBD). As we have already done for human PXR, a two-step transfection procedure was used. HeLa cells stably expressing a Gal4 responsive gene (HG5LN cell line) were transfected by Gal4-NRs expressing plasmids. At first, using these cell lines as well as the HG5LN PXR cells, we demonstrated that the basal activities varied from weak (FXR and LXRs), intermediate (PXR), to strong (CAR and RORγ), reflecting the recruitment of HeLa co-regulators in absence of ligand. Secondly, we finely characterized the activities of commercially available FXR, LXRα, LXRβ, CAR, RORγ, and PXR agonists/antagonists GW4064, feraxamine, DY268, T0901317, GW3965, WAY252623, SR9238, SR9243, GSK2033, CITCO, CINPA1, PK11195, S07662, SR1078, SR0987, SR1001, SR2211, XY018, clotrimazole, dabrafenib, SR12813, and SPA70, respectively. Among these compounds we revealed both, receptor specific agonists/antagonists, as well as less selective ligands, activating or inhibiting several nuclear receptors. FXR ligands manifested high receptor selectivity. Vice versa, LXR ligands behaved in non-selective manner, all activating at least PXR. CAR was selectively influenced by their ligands, while it also responded to several LXR ligands. Finally, although PXR was quite selectively activated or antagonized by its own ligands, it responded to several NRs ligands as well. Thus, using these reporter cell lines enabled us to precisely characterize the selectivity of pharmaceutical ligands for different nuclear receptors.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library

The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for understanding its mechanisms. Numerous studies conducted previously on CAR activation and its toxicity focused on in vivo or in vitro analyses, which are expensive, time consuming, and require many animals. We developed a computational model that predicts agonists for the CAR using the Toxicology in the 21st Century 10k library. Additionally, we evaluate the prediction performance of novel deep learning (DL)-based quantitative structure-activity relationship analysis called the DeepSnap-DL approach, which is a procedure of generating an omnidirectional snapshot portraying three-dimensional (3D) structures of chemical compounds. The CAR prediction model, which applies a 3D structure generator tool, called CORINA-generated and -optimized chemical structures, in the DeepSnap-DL demonstrated better performance than the existing methods using molecular descriptors. These results indicate that high performance in the prediction model using the DeepSnap-DL approach may be important to prepare suitable 3D chemical structures as input data and to enable the identification of modulators of the CAR.