Ligand- and structure-based pregnane X receptor models.

Area of Science:

• Pharmacology and Toxicology
• Computational Chemistry
• Drug Discovery

Background:

• The human pregnane X receptor (PXR) is a key regulator of xenobiotic detoxification and apoptosis.
• PXR activation by diverse agonists, including drugs and natural compounds, can lead to adverse effects by altering drug metabolism and physiological processes.
• Accurate prediction of PXR activators is vital for pharmaceutical development to mitigate potential toxicity.

Purpose of the Study:

• To review and evaluate ligand-based and structure-based computational methods for predicting human PXR activators.
• To highlight the challenges and successes of different modeling approaches in identifying PXR-binding compounds.
• To provide insights into improving the reliability of PXR activator prediction for drug discovery.

Main Methods:

• Review of existing literature on computational modeling for PXR activation.
• Analysis of ligand-based methods: quantitative structure-activity relationship (QSAR), pharmacophore modeling, and machine learning.
• Evaluation of structure-based modeling approaches, considering the challenges posed by PXR's large and promiscuous binding site.

Main Results:

• Structure-based modeling has shown limited success in predicting PXR activators due to the receptor's complex binding site.
• Ligand-based methods, particularly those incorporating molecular shape and diverse descriptors, have demonstrated greater success.
• Combined computational approaches integrating molecular shape information show promise for enhanced PXR activator identification.

Conclusions:

• Ligand-based computational approaches offer a more promising avenue for predicting human PXR activators compared to structure-based methods.
• Further development of computational models, especially those leveraging diverse ligand properties and molecular shape, is essential for robust drug discovery.
• Improved prediction of PXR activators will aid in avoiding adverse drug effects and optimizing pharmaceutical development pipelines.