SoFDA: an integrated web platform from syndrome ontology to network-based evaluation of disease-syndrome-formula associations for precision medicine

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network

Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning

OBJECTIVES

Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios.

MATERIALS AND METHODS

We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making.

RESULTS

To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline).

DISCUSSION

The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings.

CONCLUSION

Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Targeting the PANoptosis signaling pathway for myocardial protection: therapeutic potential of Xian Ling Gu Bao capsule

Introduction: Myocardial infarction (MI), the most prevalent ischemic heart disease, constitutes a primary cause of global cardiovascular disease with incidence and mortality. The pathogenesis of MI is exceedingly intricate, with PANoptosis playing a pivotal role in its pathological process. Xian Ling Gu Bao capsule (XLGB) contains various active components, including flavonoids, terpenes, and phenylpropanoids, and exhibits a wide range of pharmacological activities. However, it remains unclear whether XLGB can protect the myocardium from damage after MI. This study aimed to investigate the impact of XLGB on isoprenaline (ISO)-induced MI in mice and its potential mechanisms. Methods: This study assessed the protective effects of XLGB against ISO-induced MI through techniques such as echocardiography, HE staining, Masson staining, and enzyme-linked immunosorbent assay (ELISA). Furthermore, the potential mechanisms of XLGB's protective effects on MI were explored using bioinformatics, molecular docking, and molecular dynamics simulations. These mechanisms were further validated through immunofluorescence staining and Western blotting. Results: The results demonstrated that various doses of XLGB exhibited a significant reduction in myocardial injury induced by myocardial infarction. Intriguingly, higher dosages of XLGB displayed superior therapeutic efficacy compared to the positive control metoprolol. This protective effect is primarily achieved through the inhibition of oxidative stress and the inflammatory processes. Furthermore, we have elucidated that XLGB protected the myocardium from MI-induced damage by suppressing PANoptosis, with a critical role played by the NLRP3/Caspase3/RIP1 signaling pathway. Of particular note, the primary compounds of XLGB were found to directly interact with NLRP3/Caspase3/RIP1, a discovery further validated through molecular docking and molecular dynamics simulations. This suggests that NLRP3/Caspase3/RIP1 may be a therapeutic target for XLGB-induced myocardial protection. Conclusion: In summary, our findings reveal a novel property of XLGB: reverses myocardial damage following MI by inhibiting the NLRP3/Caspase3/RIP1-mediated PANoptosis pathway.

Osteoking promotes bone formation and bone defect repair through ZBP1-STAT1-PKR-MLKL-mediated necroptosis

BACKGROUND

Osteoking has been used for fracture therapy with a satisfying clinical efficacy. However, its therapeutic properties and the underlying mechanisms remain elusive.

METHOD

A bone defect rat model was established to evaluate the pharmacological effects of Osteoking by the dynamic observation of X-ray, micro-CT and histopathologic examination. Transcriptome profiling was performed to identify bone defect-related genes and Osteoking effective targets. Then, a "disease-related gene-drug target" interaction network was constructed and a list of key network targets were screened, which were experimentally verified.

RESULTS

Osteoking effectively promoted bone defect repair in rats by accelerating the repair of cortical bone and the growth of trabeculae. Histopathologically, the bone defect rats displayed lower histopathologic scores in cortical bone, cancellous bone and bone connection than normal controls. In contrast, Osteoking exerted a favorable effect with a dose-dependent manner. The abnormal serum levels of bone turnover markers, bone growth factors and bone metabolism-related biochemical indexes in bone defect rats were also reversed by Osteoking treatment. Following the transcriptome-based network investigation, we hypothesized that osteoking might attenuate the levels of ZBP1-STAT1-PKR-MLKL-mediated necroptosis involved into bone defect. Experimentally, the expression levels of ZBP1, STAT1, PKR and the hallmark inflammatory cytokines for the end of necroptosis were distinctly elevated in bone defect rats, but were all effectively reversed by Osteoking treatment, which were also suppressed the activities of RIPK1, RIPK3 and MLKL in bone tissue supernatants.

CONCLUSIONS

Osteoking may promote bone formation and bone defect repair by regulating ZBP1-STAT1-PKR axis, leading to inhibit RIPK1/RIPK3/MLKL activation-mediated necroptosis.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on Network target analysis

OBJECTIVE

The purpose of this study is to reveal the mechanism of action of Yi Qi Tong Qiao Pill (YQTQP) in the treatment of allergic rhinitis (AR), as well as establish a paradigm for the researches on traditional Chinese medicine (TCM) from systematic perspective.

METHODS

Based on the data collected from TCM-related and disease-related databases, target profiles of compounds in YQTQP were calculated through network-based algorithms and holistic targets of TQTQP was constructed. Network target analysis was performed to explore the potential mechanisms of YQTQP in the treatment of AR and the mechanisms were classified into different modules according to their biological functions. Besides, animal and clinical experiments were conducted to validate our findings inferred from Network target analysis.

RESULTS

Network target analysis showed that YQTQP targeted 12 main pathways or biological processes related to AR, represented by those related to IL-4, IFN-γ, TNF-α and IL-13. These results could be classified into 3 biological modules, including regulation of immune and inflammation, epithelial barrier disorder and cell adhesion. Finally, a series of experiments composed of animal and clinical experiments, proved our findings and confirmed that YQTQP could improve related symptoms of AR, like permeability of nasal mucosa epithelium.

CONCLUSION

A combination of Network target analysis and the experimental validation indicated that YQTQP was effective in the treatment of AR and might provide a new insight on revealing the mechanism of TCM against diseases. Trial registration Name of the registry: Chinese Clinical Trial Registry: Trial registration number: ChiCTR-TRC-13,003,137: Date of registration: Registered 29 March 2013 - Retrospectively registered: URL of trial registry record: https://www.chictr.org.cn/showproj.html?proj=6422 .

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

Ontology characterization, enrichment analysis, and similarity calculation-based evaluation of disease-syndrome-formula associations by applying SoFDA

Clinical symptom-based diagnosis and therapy play a crucial role in personalized medicine and drug discovery. The syndromes, distinctive groups of clinical symptoms summarized by traditional Chinese medicine (TCM) theories and clinical experiences, are used as the core diagnostic criteria and therapeutic guidance in TCM. However, there is still a lack of standardized data, information, and intrinsic molecular basis to help TCM syndromes better classify diseases and guide tailored medications. To address this problem, we built the first integrated web platform, SoFDA (http://www.tcmip.cn/Syndrome/front/), with a curated ontology of 319 TCM syndromes, 8045 diseases, and 1359 TCM herbal formulas and their relationships with genes, diseases, and formulas. This platform proposed an association measurement by calculating Jaccard/Cosine similarities between TCM syndromes and their related biomedical entities with case and control validations. On this basis, the SoFDA platform enables biomedical and pharmaceutical scientists to rank and filter the most promising associations for disease diagnosis and tailored interventions. Conversely, the targeted gene sets and symptom sets can also be associated with TCM syndromes, formulas, and diseases for function illustration. Notably, SoFDA explores the multi-way associations among diseases, TCM syndromes, symptom genes, herbal formulas, drug targets, and pathways in heterogeneous biomedical networks with lots of customization. The protocol here implements all the analyses above using the SoFDA platform. Collectively, SoFDA may provide insights into the biological basis of disease-specific TCM syndromes and the underlying molecular mechanisms, as well as a tailored treatment for single or multiple symptoms within a syndrome.

Machine learning in TCM with natural products and molecules: current status and future perspectives

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

Efficacy and safety of LongShengZhi capsule on functional recovery after acute ischemic stroke (LONGAN): Protocol and statistical analysis plan for a randomized, double-blind, placebo-controlled trial

Background: Due to limited time windows and technical requirements, only a small percentage of patients can receive reperfusion therapy for acute ischemic stroke (AIS). Previous studies have shown that LongShengZhi (LSZ) capsule can improve neurological outcomes in patients after AIS, yet those results have not been finally verified through rigorous randomized controlled trials. Thus, this trial was designed to further clarify the efficacy and safety of LSZ capsule for patients with AIS. Methods: LSZ capsule on Functional Recovery after Acute Ischemic Stroke (LONGAN) trial is a prospective, multicenter, randomized, placebo-controlled, double-blind, parallel-group, superiority trial that enrolls patients from stroke and rehabilitation units in China. We will enroll 1,376 patients aged 18 years or older with AIS within 7 days of symptom onset and a National Institute of Health Stroke Scale (NIHSS) score of 4-15. Eligible patients will be randomized to receive either 2 g LSZ capsules three times a day or placebo LSZ capsules for 90 days. The primary outcome is the proportion of patients with favorable outcomes, as measured by the modified Rankin Scale (mRS) 90 days after randomization. The main safety outcome is the proportion of severe adverse events. Conclusion: This study will be the first randomized, double-blind trial to evaluate the efficacy and safety of LSZ capsule in patients with AIS. In order to improve the transparency and reproducibility of the trial, the data will be analyzed in accordance with this pre-specified plan for statistical analysis to reduce bias due to selective analysis and reporting. This trial aims to provide high-quality evidence for the efficacy and safety of LSZ capsule for AIS.

TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms

In China, traditional Chinese medicine (TCM) has been widely used for coronavirus infectious disease 2019 (COVID-19) prevention, treatment, and recovery and has played a part in the battle against the disease. A variety of TCM treatments have been recommended for different stages of COVID-19. But, to the best of our knowledge, a comprehensive database for storing and organizing anti-COVID TCM treatments is still lacking. Herein, we developed TCM2COVID, a manually curated resource of anti-COVID TCM formulas, natural products (NPs), and herbs. The current version of TCM2COVID (1) documents over 280 TCM formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information; (2) records over 80 NPs with detailed potential therapeutic mechanisms; and (3) launches a useful web server for querying, analyzing and visualizing documented formulas similar to those supplied by the user (formula similarity analysis). In summary, TCM2COVD provides a user-friendly and practical platform for documenting, querying, and browsing anti-COVID TCM treatments, and will help in the development and elucidation of the mechanisms of action of new anti-COVID TCM therapies to support the fight against the COVID-19 epidemic. TCM2COVID is freely available at http://zhangy-lab.cn/tcm2covid/.