AUTHOR=Wang Xiuhuan , Sun Youyi , Ling Ling , Ren Xueyang , Liu Xiaoyun , Wang Yu , Dong Ying , Ma Jiamu , Song Ruolan , Yu Axiang , Wei Jing , Fan Qiqi , Guo Miaoxian , Zhao Tiantian , Dao Rina , She Gaimei 

TITLE=Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.704040

DOI=10.3389/fphar.2021.704040

ISSN=1663-9812

ABSTRACT=Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a Traditional Chinese/Ethnic Medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti-rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities, and it is mainly composed of methyl salicylate glycosides, flavonoids, organic acids and the others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.
Purpose: The aim of present study is to give insight on the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of ADME properties, the analysis of network pharmacology, and the validation of in vivo and in vitro.
Study design and methods: The IL-1β-induced HFLS-RA cell model and adjuvant induced arthritis rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by UHPLC-LTQ-Orbitrap-MSn. The quantitative structure-activity relationship (QSAR) models were developed by 5 machine learning algorithms alone or combined with genetic algorithm for predicting ADME properties of ARF. The molecular networks and pathways presumably referred to the therapy of ARF on RA were yielded by common databases and visible software, and the experimental validations of key targets were conducted in vitro. 
Results: ARF effectively relieved RA in vivo and in vitro. The developed five optimized QSAR models have robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β-induced expression of EGFR, MMP 9, IL2, MAPK14 and KDR in HFLS-RA cells.
Conclusions: ARF has good druggability and high exploitation. The methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in inflammation-immune system. It provides valuable information to rationalize for ARF and other TC/EMs in treatment on RA.