Exploring the Underlying Mechanism of Shenyankangfu Tablet in the Treatment of Glomerulonephritis Through Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation

Computer-Aided Drug Design in Research on Chinese Materia Medica: Methods, Applications, Advantages, and Challenges

Chinese materia medica (CMM) refers to the medicinal substances used in traditional Chinese medicine. In recent years, CMM has become globally prevalent, and scientific research on CMM has increasingly garnered attention. Computer-aided drug design (CADD) has been employed in Western medicine research for many years, contributing significantly to its progress. However, the role of CADD in CMM research has not been systematically reviewed. This review briefly introduces CADD methods in CMM research from the perspectives of computational chemistry (including quantum chemistry, molecular mechanics, and quantum mechanics/molecular mechanics) and informatics (including cheminformatics, bioinformatics, and data mining). Then, it provides an exhaustive discussion of the applications of these CADD methods in CMM research through rich cases. Finally, the review outlines the advantages and challenges of CADD in CMM research. In conclusion, despite the current challenges, CADD still offers unique advantages over traditional experiments. With the development of the CMM industry and computer science, especially driven by artificial intelligence, CADD is poised to play an increasingly pivotal role in advancing CMM research.

Integrated Network Pharmacology, Machine Learning and Experimental Validation to Identify the Key Targets and Compounds of TiaoShenGongJian for the Treatment of Breast Cancer

Background

TiaoShenGongJian (TSGJ) decoction, a traditional Chinese medicine for breast cancer, has unknown active compounds, targets, and mechanisms. This study identifies TSGJ's key targets and compounds for breast cancer treatment through network pharmacology, machine learning, and experimental validation.

Methods

Bioactive components and targets of TSGJ were identified from the TCMSP database, and breast cancer-related targets from GeneCards, PharmGkb, and RNA-seq datasets. Intersection of these targets revealed therapeutic targets of TSGJ. PPI analysis was performed via STRING, and machine learning methods (SVM, RF, GLM, XGBoost) identified key targets, validated by GSE70905, GSE70947, GSE22820, and TCGA-BRCA datasets. Pathway analyses and molecular docking were performed. TSGJ and core compounds' effectiveness was confirmed by MTT and RT-qPCR assays.

Results

160 common targets of TSGJ were identified, with 30 hub targets from PPI analysis. Five predictive targets (HIF1A, CASP8, FOS, EGFR, PPARG) were screened via SVM. Their diagnostic, biomarker, immune, and clinical values were validated. Quercetin, luteolin, and baicalein were identified as core components. Molecular docking confirmed their strong affinities with predicted targets. These compounds modulated key targets and induced cytotoxicity in breast cancer cell lines in a similar way as TSGJ.

Conclusion

This study reveals the main active components and targets of TSGJ against breast cancer, supporting its potential for breast cancer prevention and treatment.

Effect of tacrolimus with mycophenolate mofetil or cyclophosphamide on the renal response in systemic lupus erythematosus patients

OBJECTIVE

This study aimed to determine the therapeutic efficacy of tacrolimus (TAC) with mycophenolate mofetil (MMF) or cyclophosphamide (CYC) on the renal response in systemic lupus erythematosus (SLE) patients.

METHODS

A retrospective cohort study based on medical data was conducted among SLE patients who took at least one of the following medicines in 2010-2021: TAC, MMF and CYC. The odds ratio (OR) and 95% confidence interval (CI) were calculated, and the synergistic interaction was estimated using logistic regression models.

RESULTS

Among 793 SLE patients, 27.9% patients (221 cases) achieved CR after at least 3 months. The TAC use was positively associated with CR with an adjusted OR (95% CI) of 2.82 (1.89, 4.22) overall and in subgroups of SLE patients with SLEDAI scores > 12, moderate or severe urinary protein and comorbidities. The dose-response effect on CR was also observed at TAC doses greater than 4 mg/d and more than 180 days, with adjusted ORs (95% CIs) of 5.65 (2.35, 13.55) and 3.60 (2.02, 6.41), respectively. Moreover, the combined effect of TAC with MMF or CYC was better than that of monotherapy, there was significant synergistic interactions with adjusted ORs (95% CIs) of 2.43 (1.20, 4.92) and 3.14 (1.49, 6.64), respectively, and similar results were observed for the combination of different doses of TAC with MMF or CYC.

CONCLUSION

TAC can effectively alleviate the condition of patients with SLE and may interact with MMF or CYC, which suggests that the combination therapy of TAC with MMF or CYC may produce greater benefits for patients with SLE.

TRIAL REGISTRATION

This is a purely observational study that does not require registration.

Dynamic changes in marker components during the stir-frying of Pharbitidis Semen, and network analysis of its potential effects on nephritis

Introduction: Pharbitidis Semen (PS) has been widely used in traditional Chinese medicine to treat several diseases such as nephritis. PS is usually stir-fried to enhance its therapeutic efficacy before use in clinical practice. However, the changes in phenolic acids during stir-frying and the mechanisms of their therapeutic effects on nephritis are still unclear.

Methods: Here, we studied the processing-induced chemical changes and elucidated the mechanism of PS in the treatment of nephritis. We determined the levels of the 7 phenolic acids in raw PS (RPS) and stir-fried PS (SPS) using high-performance liquid chromatography, analyzed the dynamic compositional changes during stir-frying, and used network analysis and molecular docking to predict and verify compound targets and pathways corresponding to nephritis.

Results: The dynamic changes in the 7 phenolic acids in PS during stir-frying are suggestive of a transesterification reaction. Pathway analysis revealed that the targets of nephritis were mainly enriched in the AGE-RAGE, hypoxia-inducible factor-1, interleukin-17, and tumor necrosis factor signaling pathways among others. Molecular docking results showed that the 7 phenolic acids had good binding ability with the key nephritic targets.

Discussion: The potential pharmaceutical basis, targets, and mechanisms of PS in treating nephritis were explored. Our findings provide a scientific basis for the clinical use of PS in treating nephritis.

Meta-analysis of the effectiveness and safety of Shenyankangfu tablets combined with losartan potassium in the treatment of chronic glomerulonephritis

OBJECTIVE

To conduct a systematic review of the efficacy and safety of Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis.

METHOD

We searched PubMed, Embase, Cochrane Library, CNKI, WanFang Data, and WeiPu for comparative studies on Shenyankangfu tablets in combination with losartan potassium in the treatment of chronic glomerulonephritis. The search period runs from the establishment of the database until September 2021. Data extraction and quality evaluation were carried out on the documents that met the inclusion criteria, and a meta-analysis of the included literature was conducted using the RevMan5.3 software.

RESULTS

A total of 17 randomized controlled trials that met the inclusion criteria were included, with a total sample size of 1680 patients (841 patients in the study group and 839 in the control group). The effective rate was significantly higher in the study group than in the control group [RR = 1.22, 95% CI (1.16, 1.27), P < 0.00001]. In addition, 24-hour urine protein levels [SMD = -1.11, 95% CI (-1.40, -0.83), P < 0.00001], urine NAG enzyme [SMD = -0.99, 95% CI (-1.27, -0.72), P < 0.00001], leukotactin-1 [SMD = -2.43, 95% CI (-3.50, -1.35), P < 0.00001], and the incidence of adverse reactions [RR = 0.43, 95% CI (0.28, 0.66), P < 0.00001] were lower in the study group when compared to the control group.

CONCLUSION

It is safer to treat chronic glomerulonephritis with Shyenyankangfu tablets in combination with losartan potassium. At the same time, it alleviates disease-related symptoms, reduces the influence of cytokine levels, and has fewer adverse reactions, making it more conducive to disease recovery. However, additional multi-center, randomized, control trials with large sample sizes must be conducted to confirm the findings.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.