An herbal drug combination identified by knowledge graph alleviates the clinical symptoms of plasma cell mastitis patients: a nonrandomized controlled trial
- Shengjing Hospital of China Medical University, China
- Dalian University of Technology, China
- China Resources Sanjiu Medical and Pharmaceutical Co, Ltd, China
- Peking University, China
- Shanghai BeautMed Corporation, China
- University of Copenhagen, Denmark
Abstract
Background: Plasma cell mastitis (PCM) is a nonbacterial breast inflammation with severe and intense clinical manifestation yet treatment methods for PCM are still rather limited. Although the mechanism of PCM remains unclear, mounting evidences suggest that the dysregulation of immune system is closely associated with the pathogenesis of PCM. Drug combinations or combination therapy could exert improved efficacy and reduced toxicity through hitting multiple discrete cellular targets.
Methods: We have developed a knowledge graph architecture towards immunotherapy and systematic immunity that consists of herbal drug-target interactions with a novel scoring system to select drug combinations based on target-hitting rates and phenotype relativeness. To this end, we employed this knowledge graph to identify an herbal drug combination for PCM and we subsequently evaluated the efficacy of the herbal drug combination in clinical trial.
Results: Our clinical data suggests that the herbal drug combination could significantly reduce the serum level of various inflammatory cytokines, downregulate serum IgA and IgG level, reduce the recurrence rate and reverse the clinical symptoms of PCM patients with improvements of general health status.
Conclusions: In summary, we reported that an herbal drug combination identified by knowledge graph can alleviate the clinical symptoms of plasma cell mastitis patients. We demonstrated that the herbal drug combination holds great promise as an effective remedy for PCM, acting through the regulation of immunoinflammatory pathways and improvement of systematic immune level. In particular, the herbal drug combination could significantly reduce the recurrence rate of PCM, a major obstacle for PCM treatment. Our data suggests that the herbal drug combination is expected to feature prominently in future PCM treatment.
Funding: Liu's lab was supported by grants from the Public Health Science and Technology Project of Shenyang (Grant: 22-321-32-18), Y. Yang's laboratory was supported by the National Natural Science Foundation of China (Grant: 81874301); the Fundamental Research Funds for Central University (Grant: DUT22YG122) and the Key Research project of 'be Recruited and be in Command' in Liaoning Province (2021JH1/10400050).
Clinical trial number: ClinicalTrials.gov: NCT05530226.
Data availability
Figure 1-3 are computational study and therefore no data have been generated for the manuscript. In addition, Figure 4 - Source Data, Figure 5 - Source Data, Figure 6 - Source Data 1, Figure 6 - Source Data 2 and Figure 6 - Source Data 3 contain the numerical data used to generate the figures have been included in the manuscript.
Article and author information
Author details
Funding
National Natural Science Foundation of China (81874301)
- Yongliang Yang
National Natural Science Foundation of China (81572609)
- Caigang Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The protocol was approved by the Institutional Review Board (IRB) of the China Medical University (approval number: 2021PS024T). This study was registered with ClinicalTrials.gov: NCT05530226. All patients provided written informed consent.
Copyright
© 2023, Liu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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An herbal drug combination identified by knowledge graph alleviates the clinical symptoms of plasma cell mastitis patients: a nonrandomized controlled trial
eLife 12:e84414.
https://doi.org/10.7554/eLife.84414
Further reading
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Drugs that induce reversible slowing of metabolic and physiological processes would have great value for organ preservation, especially for organs with high susceptibility to hypoxia-reperfusion injury, such as the heart. Using whole-organism screening of metabolism, mobility, and development in Xenopus, we identified an existing drug, SNC80, that rapidly and reversibly slows biochemical and metabolic activities while preserving cell and tissue viability. Although SNC80 was developed as a delta opioid receptor activator, we discovered that its ability to slow metabolism is independent of its opioid modulating activity as a novel SNC80 analog (WB3) with almost 1000 times less delta opioid receptor binding activity is equally active. Metabolic suppression was also achieved using SNC80 in microfluidic human organs-on-chips, as well as in explanted whole porcine hearts and limbs, demonstrating the cross-species relevance of this approach and potential clinical relevance for surgical transplantation. Pharmacological induction of physiological slowing in combination with organ perfusion transport systems may offer a new therapeutic approach for tissue and organ preservation for transplantation, trauma management, and enhancing patient survival in remote and low-resource locations.
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Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
