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Rhubarb, as a traditional Chinese medicine, has several positive therapeutic effects, such as purging and attacking accumulation, clearing heat and purging fire, cooling blood, and detoxification. Recently, Rhubarb has been used in prescriptions for the prevention and treatment of COVID-19, with good efficacy. However, the exploration of effective quantitative approach to ensure the consistency of rhubarb’s therapeutic efficacy remains a challenge. In this case, this study aims to use non-targeted and targeted data mining technologies for its exploration and has comprehensively identified 72 rhubarb-related components in human plasma for the first time. In details, the area under the time-concentration curve (AUC)-pooled method was used to quickly screen the components with high exposure, and the main components were analyzed using Pearson correlation and other statistical analyses. Interestingly, the prototype component (rhein) with high exposure could be selected out as a Q-marker, which could also reflect the metabolic status changes of rhubarb anthraquinone in human. Furthermore, after comparing the metabolism of different species, mice were selected as model animals to verify the pharmacodynamics of rhein. The  in vivo  experimental results showed that rhein has a positive therapeutic effect on pneumonia, significantly reducing the concentration of pro-inflammatory factors [interleukin (IL)-6 and IL-1β] and improving lung disease. In short, based on the perspective of human exposure, this study comprehensively used intelligent data post-processing technologies and the AUC-pooled method to establish that rhein can be chosen as a Q-marker for rhubarb, whose content needs to be monitored individually.

Exploration of Q-Marker of Rhubarb Based on Intelligent Data Processing Techniques and the AUC Pooled Method