Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

Evaluation of FXR Activity in Pollutants Identified in Sewage Sludge and Subsequent in Vitro and in Vivo Characterization of Metabolic Effects of Triphenyl Phosphate

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease worldwide, and increasing evidence suggests that exposure to environmental pollutants is associated with the increased incidence of MASLD. The farnesoid X receptor (FXR) plays an important role in the development of MASLD by regulating bile acids (BAs) and lipid metabolism. However, whether FXR-active pollutants are the environmental drivers of MASLD remains unclear.

OBJECTIVES

This study aimed to determine whether FXR-active pollutants exist in the environment and evaluate their ability to trigger MASLD development in mice.

METHODS

An FXR protein affinity pull-down assay and nontargeted mass spectrometry (MS) analysis were used to identify environmental FXR ligands in sewage sludge. A homogeneous time-resolved fluorescence coactivator recruitment assay and cell-based dual-luciferase reporter assay were used to determine the FXR activities of the identified pollutants. Targeted analysis of BAs, MS imaging, lipidomic analysis, 16S rRNA sequencing, and quantitative polymerase chain reaction were conducted to assess the ability of FXR-active pollutants to induce metabolic disorders of BAs and lipids and to contribute to MASLD development in C57BL/6N mice.

RESULTS

We identified 19 compounds in the sewage sludge that had FXR-antagonistic activity, and triphenyl phosphate (TPHP) was the FXR antagonist with the highest efficacy. Mice exposed to either 10 or 50 mg / kg TPHP for 30 d had higher levels of conjugated primary BAs in enterohepatic circulation, and the BA pool showed FXR antagonistic activities. The exposed mice also had greater lipogenesis (more Oil Red O staining and high triglyceride levels) in liver.

CONCLUSIONS

Nineteen FXR-antagonistic pollutants were identified in sewage sludge. FXR inhibition by the strongest antagonist TPHP may have a role in promoting MASLD development in mice by inducing a positive feedback loop between the FXR and BAs. https://doi.org/10.1289/EHP15435.

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.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.