Computational Tools to Expedite the Identification of Potential PXR Modulators in Complex Natural Product Mixtures: A Case Study with Five Closely Related Licorice Species

The genus Glycyrrhiza, comprising approximately 36 spp., possesses complex structural diversity and is documented to possess a wide spectrum of biological activities. Understanding and finding the mechanisms of efficacy or safety for a plant-based therapy is very challenging, yet it is crucial and necessary to understand the polypharmacology of traditional medicines. Licorice extract was shown to modulate the xenobiotic receptors, which might manifest as a potential route for natural product-induced drug interactions. However, different mechanisms could be involved in this phenomenon. Since the induced herb-drug interaction of licorice supplements via Pregnane X receptor (PXR) is understudied, we ventured out to analyze the potential modulators of PXR in complex mixtures such as whole extracts by applying computational mining tools. A total of 518 structures from five species of Glycyrrhiza: 183 (G. glabra), 180 (G. uralensis), 100 (G. inflata), 33 (G. echinata), and 22 (G. lepidota) were collected and post-processed to yield 387 unique compounds. Visual inspection of top candidates with favorable ligand-PXR interactions and the highest docking scores were identified. The in vitro testing revealed that glabridin (GG-14) is the most potent PXR activator among the tested compounds, followed by licoisoflavone A, licoisoflavanone, and glycycoumarin. A 200 ns molecular dynamics study with glabridin confirmed the stability of the glabridin-PXR complex, highlighting the importance of computational methods for rapid dereplication of potential xenobiotic modulators in a complex mixture instead of undertaking time-consuming classical biological testing of all compounds in a given botanical.

Automated summary

This study analyzed the effects of compounds from various species of Glycyrrhiza on the receptor PXR, and found that glabridin is the most potent activator. Computational methods can be used to quickly identify potential xenobiotic modulators, avoiding time-consuming biological testing.