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Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CE...
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Citations
... Therefore, TCMNPAS performs adaptive binarization on weighted networks before running the BK algorithm and evaluates core formulas based on the two metrics of prescription support and confidence: (1) average confidence of a core formula (α); (2) support under a confidence α, Sα. These two metrics are described in detail elsewhere [55]. ...
... Additionally, users can choose from several options, including "Enforce the core formula containing drug number to be equal to the expected drug number during adaptive screening" and "Merge core formulas with high similarity. " The next step is to determine the method for merging highly similar core formulas, set the similarity threshold for merging core formulas, and choosing options such as "Calculate person-based statistics, " "Visualization of compatibility network, " and "Dynamic display" [25,42,55,56] (Fig. 4B). ...
... The similarity threshold for merging core formulas should be set between 0 and 1, with a recommendation to choose a value greater than 0.6. If the input prescription data includes Vid, selecting the "Calculate personbased statistics" option will facilitate patient-based core formula statistics [25,[55][56][57]. ...
The application of network formulaology and network pharmacology has significantly advanced the scientific understanding of traditional Chinese medicine (TCM) treatment mechanisms in disease. The field of herbal biology is experiencing a surge in data generation. However, researchers are encountering challenges due to the fragmented nature of the data and the reliance on programming tools for data analysis. We have developed TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. TCMNPAS incorporates multiple resources and offers a range of functions designed for automated analysis implementation, including prescription mining, molecular docking, network pharmacology analysis, and visualization. These functions enable researchers to analyze and obtain core herbs and core formulas from herbal prescription data through prescription mining. Additionally, TCMNPAS facilitates virtual screening of active compounds in TCM and its formulas through batch molecular docking, allowing for the rapid construction and analysis of networks associated with “herb-compound-target-pathway” and disease targets. Built upon the integrated analysis concept of network formulaology and network pharmacology, TCMNPAS enables quick point-and-click completion of network-based association analysis, spanning from core formula mining from clinical data to the exploration of therapeutic targets for disease treatment. TCMNPAS serves as a powerful platform for uncovering the combinatorial rules and mechanism of TCM formulas holistically. We distribute TCMNPAS within an open-source R package at GitHub (https://github.com/yangpluszhu/tcmnpas), and the project is freely available at http://54.223.75.62:3838/.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13020-024-00924-y.
... Data mining is a combination of machine learning, artificial intelligence, and other technologies [15]. It estimates correlations by analyzing large amounts of data and extracting hidden relationships and information. ...
... e confidence of the mined core prescriptions was evaluated using the confidence based on the whole network (CMBN) score and was supported by the α confidence level [15]. Both values ranged from 0 to 1, and higher values indicated higher confidence. ...
Background. “Zheng” (syndrome) is the basic unit and the basis of traditional Chinese medicine (TCM) treatment. In clinical practice, we have been able to improve the survival time and quality of life for patients with rectal cancer through the treatment of “FuZhengXiaoJi” (strengthening the Qi and reducing accumulation). Purpose. In this study, we elucidated the core prescriptions for patients with rectal cancer and Qi and blood deficiency syndrome, and we explored the potential mechanisms of the prescriptions using an integrated strategy that coupled data mining with network pharmacology. Methods. A Bron–Kerbosch (BK) algorithm was applied to find the core prescriptions. The active ingredients, targets, activated signaling pathways, and biological functions of core prescriptions were analyzed using network pharmacology and directly associated proteins were docked using molecular docking technology to elucidate the multicomponent, multitarget, and inter-related components associated with TCM systematically. Results. Data mining identified 3 core prescriptions, and most of the herbs consisted of “FuZhengXiaoJi” Fang. Network pharmacology identified 15 high-degree active ingredients among the 3 core prescriptions and 16 high-degree hub genes linked with both rectal cancer and the 3 core prescriptions. Additional Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these 16 targets showed that the most significant pathways were MAPK, interleukin-17, tumor necrosis factor (TNF), and vascular endothelial growth factor (VEGF) pathways. From the 16 genes, TGFB1, IL1B, IL10, IL6, PTGS2, and PPARG closely interacted with the tumor microenvironment, and PPARG, MYC, and ERBB2 were closely linked to survival. In molecular docking, quercetin, kaempferol, and lauric acid showed good binding energy to each target. Conclusion. Data mining, network pharmacology, and molecular docking may help identify core prescriptions, high-degree ingredients, and high-degree hub genes to apply to diseases and treatments. Furthermore, these studies may help discover hub genes that affect the tumor microenvironment and survival. The combination of these tools may help elucidate the relationship between herbs acting on “Zheng” (syndrome) and diseases, thus expanding the understanding of TCM mechanisms.
1. Introduction
Rectal cancer is a common type of colorectal cancer (CRC). CRC is the second and fourth most common malignant tumor in the world and in China, respectively (approximately 37.6 per 100,000 in 2015) [1, 2], and has a mortality rate of 19.1 per 100,000 [2]. CRC has become a substantial burden in China, particularly in the more developed provinces [3]. Unfortunately, more than 50% of patients with CRC are diagnosed at an advanced stage [4]; according to previous reports, 20%–50% of patients with rectal cancer will eventually develop metastatic disease [5]. At an advanced stage, the 5-year survival rate of CRC is less than 14%.
Effective treatments for CRC include a combination of surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy therapy. However, chemotherapeutics have known limitations, such as their substantial adverse effects, including gastrointestinal reactions, mucositis, bone marrow suppression, neurotoxicity, abnormal liver or kidney function, febrile neutropenia, and fatigue [6]. The search for more effective alternative agents with lower toxicity that are able to inhibit the tumor’s metastatic potential is essential [7, 8].
Traditional Chinese medicine (TCM) has been used to treat CRC for more than 6,000 years with some degree of success [9]. Good results, including better efficiency and lower toxicity, prolonged survival periods, and improved quality of life, have also been achieved by combinations of TCM with chemotherapy, radiation therapy, targeted therapy, and immunotherapy for the treatment of advanced CRC [10]. Based on a meta-analysis, five herbs, including GanCao (glycyrrhizae radix et rhizoma), BaiZhu (atractylodis macrocephalae rhizoma), HuangQi (astragali radix), DangShen (codonopsis radix), and ChenPi (citri reticulatae pericarpium), were identified as associated with significant reductions in chemotherapy-induced gastrointestinal (CIGI) toxicity (nausea and vomiting, diarrhea, abdominal pain, abdominal bleeding, ulcerative lesions along the gastrointestinal tract, etc.) [11]. In addition, Si-Jun-Zi decoction [12], which contains GanCao, BaiZhu, FuLing (poria), and RenShen (ginseng radix et rhizoma), has alleviated CIGI toxicity, including nausea, vomiting, and diarrhea, better than chemotherapy alone.
Some herbs increase the effects of radiotherapy and chemotherapy and prevent metastasis. BaiHuaSheSheCao (Hedyotis diffusa Willd) inhibits VEGF-C-mediated lymphangiogenesis in CRC through multiple signaling pathways [13]. Dang-Gui-Bu-Xue decoction (DangGui and HuangQi) induces autophagy-associated cell death in CT26 cells and may have potential as a chemotherapy or radiotherapy sensitizer in CRC treatment [14].
Data mining is a combination of machine learning, artificial intelligence, and other technologies [15]. It estimates correlations by analyzing large amounts of data and extracting hidden relationships and information. In recent years, data mining technology has been extensively applied to understand the activities of TCM treatments better. Network pharmacology is a new technology that obtains information by systematic observation of the intervention and the influence of drugs on the disease from a holistic perspective; it has been successfully and widely applied in many research fields related to TCM [16, 17]. The network pharmacology approach has clear advantages compared with conventional methods in deepening the understanding of comprehensive mechanisms [18].
The purpose of this study was to evaluate systematically and elucidate the composition of the TCM prescription applied at our hospital for the treatment of rectal cancer with Qi and blood deficiency “Zheng” (syndrome) using data mining to provide novel insights into the management and clinical treatment of this disease. Moreover, this study applied the methods of network pharmacology and molecular docking to explore the mechanisms of action for core prescriptions used to treat rectal cancer. The study procedure is shown in Figure 1.
... The relationship between herbs and diseases is nondominant, and which herb plays a core role in specific diseases is unclear [21]. Therefore, how to rank and evaluate herb's importance, determine the most critical herbs from these complex combinations, and discover the associations with particular symptoms, is the primary key to maximizing the use of existing clinical formulas and promote herbal drug development [22]. ...
The use of herbs to treat various human diseases has been recorded for thousands of years. In Asia's current medical system, numerous herbal formulas have been repeatedly verified to confirm their effectiveness in different periods, which is a great resource for drug innovation and discovery. Through the mining of these clinical effective formulas by network pharmacology and bioinformatics analysis, important biologically active ingredients derived from these natural products might be discovered. As modern medicine requires a combination of multiple drugs for the treatment of complex diseases, previously clinical formulas are also combinations of various herbs according to the main causes and accompanying symptoms. However, the herbs that play a major role in the treatment of diseases are always unclear. Therefore, how to rank each herb's relative importance and determine the core herbs, is the first step to assisting herb selection for active ingredients discovery. To solve this problem, we built the platform FangNet, which ranks all herbs on their relative topological importance using the PageRank algorithm, based on the constructed symptom-herb network from a collection of clinical empirical prescriptions. Three types of herb hidden knowledge, including herb importance rank, herb-herb co-occurrence, and associations to symptoms, were provided in an interactive visualization. Moreover, FangNet has designed role-based permission for teams to store, analyze, and jointly interpret their clinical formulas, in an easy and secure collaboration environment, aiming at creating a central hub for massive symptom-herb connections. FangNet can be accessed at http://fangnet.org or http://fangnet.herb.ac.cn.
... Our group has accumulated some experience about network prediction. [27][28][29] Therefore, here we adopted a network pharmacology approach to predict possible bio-active compounds, targets and mechanisms of DWYG. Then experiments in vivo and in vitro were designed according to the above results. ...
Background
Hepatitis virus infection plays a critical role in liver cancer initiation and development; so the purpose of this study was to investigate the anti-liver cancer effects of DiWuYangGan (DWYG) which was effective for hepatitis.
Methods
Network predictions were performed. Next, several tests, including HPLC, Caco-2 absorption models, MMT, protein chip, Western blotting and H22-tumor-bearing mouse, were carried out to investigate the effects and possible mechanism of DWYG.
Results
Network results showed DWYG might be involved in some processes such as STAT cascade. Some target genes may correspondingly participate in these procedures, such as IL-6, CASP3, AKT1, PPAR, and TP53. Diseases associated with DWYG formula may be liver cancer and hepatitis. Potential active compounds might be CUR and ISO. Chemical analysis results showed that ingredients in the formula, including DEO, SCHB, SOLA, SOLB, SCHA, LIQ, ISO, POT, and CHL, could be determined, indicating that DWYG samples for the following experiments were controllable and consistent. Caco-2 absorption of ingredients in DWYG, including DEO, SCHB, SOLA, SOLB, and LIQ, worked very well. In vitro experiment results showed that DWYG could inhibit the growth of cell lines and its effective ingredients might be SCHB, SOLB, SINA, SINB, SOLB, CUR, DEM, BIS, and GER. Further protein results showed that DWYG could upregulate the expressions of some proteins, including ERK1/2, AKT Ser473, BAD Ser112, PRAS40, Thr246, P38, Gsk-3β, and Ser9. In vivo experiment results showed that DWYG could shrink tumor size, recover ALT and AST, and decrease IL-6 levels. Their possible mechanism might be through the JAK/STAT3 pathway.
Conclusion
Besides the known pharmacological function of anti-hepatitis, DWYG extract expressed anti-liver cancer effects and the results were consistent partly with network predictions.
... These attempts give us a good inspiration. Thus our team has also tried to search some databases and use computational tools 19 , which were successfully applied to predict p-Glycoprotein inhibitors 20 , discovery core effective formulae in TCM 21 , mine the tumor clinical data of TCM 22 , identify serum lipid alteration 23 and so on. These experiences provide a basis for us to study RYP formula. ...
Ruyiping (RYP), a Chinese herbal formula, can remove toxin and clear nodular, showing ability of preventing postoperative recurrence of breast cancer. In this study, network was performed to predict possible targets, genes and pathways associated with RYP and breast cancer. Thin Layer Chromatography (TLC) and High Performance Liquid Chromatography (HPLC) were used to quantitatively study RYP formula and its single herbs. MTT methods, Luciferase reporter systems, zebrafish model and western blotting were respectively adopted to verify network prediction. Results showed that the quality of RYP could be controlled and icariin could be selected as mark ingredient; RYP expressed anti-breast tumor effects, which could be associated with inhibiting expression of Transforming Growth Factor β (TGFβ), promoting cells apoptosis and anti-angiogenesis. Parts of these results were consistent with network predictions in some degree, but not all. Network can help us narrow areas, focus on crucial factors, save money as well as time, but the results predicted by network should be confirmed by further experiments.
... A given synergistic effect can be tested by comparing the pharmacological effects of the monosubstances versus the combination of substances by analyzing isobole curves based on data from several dose combinations (60). This analysis enables one to discriminate effects between simple additive, antagonistic interactions or real synergism with potentiated or over-additive effects (56). ...
The efforts to understand the nature of how the consumption of cannabis affects the human body are ongoing, complex, and multifaceted. Documentation on the use of cannabis dates back thousands of years; however, it is only now with the recent softening of legal restrictions that modern research approaches have been able to initiate an appropriate level of detailed investigations For clinicians, researchers and policy makers, this paper reviews the general structure of cannabinoids, the current understanding of cannabinoids on cellular systems, the deference of inhalation and oral consumption on cannabinoid bioavailability, the variance among purified cannabinoids versus whole plant extract, and the potential activities of another prominent family of secondary metabolites found in cannabis, the terpenes.
... The existing prescription patterns vary from methods to methods. By clustering algorithms, the patterns are in the form of specific groups of herbs for stroke [8] or for gout and hyperuricemia [9], in the form of latent tree for "disharmony between liver and spleen" which is a TCM defined symptom [10], in the form of several flat groups of herbs [4,11,12] with different treatment functions. By genetic algorithms, 2 Evidence-Based Complementary and Alternative Medicine the patterns are core groups of herbs for lung cancer [12]. ...
... By clustering algorithms, the patterns are in the form of specific groups of herbs for stroke [8] or for gout and hyperuricemia [9], in the form of latent tree for "disharmony between liver and spleen" which is a TCM defined symptom [10], in the form of several flat groups of herbs [4,11,12] with different treatment functions. By genetic algorithms, 2 Evidence-Based Complementary and Alternative Medicine the patterns are core groups of herbs for lung cancer [12]. By factor analysis, the patterns are 7 groups of herbs for insomnia [13]. ...
Prescription patterns are rules or regularities used to generate, recognize, or judge a prescription. Most of existing studies focused on the specific prescription patterns for diverse diseases or syndromes, while little attention was paid to the common patterns, which reflect the global view of the regularities of prescriptions. In this paper, we designed a method CPPM to find the common prescription patterns. The CPPM is based on the hierarchical clustering of herb-pair efficacies (HPEs). Firstly, HPEs were hierarchically clustered; secondly, the individual herbs are labeled by the HPE
C
(the clusters of HPEs); and then the prescription patterns were extracted from the combinations of HPE
C
; finally the common patterns are recognized statistically. The results showed that HPEs have hierarchical clustering structure. When the clustering level is 2 and the HPEs were classified into two clusters, the common prescription patterns are obvious. Among 332 candidate prescriptions, 319 prescriptions follow the common patterns. The description of the patterns is that if a prescription contains the herbs of the cluster (
C
1
), it is very likely to have other herbs of another cluster (
C
2
); while a prescription has the herbs of
C
2
, it may have no herbs of
C
1
. Finally, we discussed that the common patterns are mathematically coincident with the Blood-Qi theory.
... Fang et al. [134] developed a highly complicated database called TCM-GeneDIT to discover the relationships among medicines, genes, diseases, TCM effects, and TCM ingredients from a large amount of biomedical literatures. A herb-herb network was built in [135] to find the core effective formula by using genetic algorithm from a lung cancer dataset. All the results manifested the proposed network that is effective and agreed with the TCM theory. ...
As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs
examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of
disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on
patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic
data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.
... In this context, this study attempts to explore a strategic method which allows comprehensive analysis of a massive amount of complex data to determine any underlying regularities and valuable information in TCM studies for drug development. According to the literature, the methods of information discovery in database have been suggested as promising approaches [11]. Among these approaches is network analysis [12][13][14][15][16][17][18][19], which has been widely applied to TCM for screening synergistic drug combination [20], establishing network pharmacology of TCM [21], uncovering formulae combination rules [22][23], and predicting drug targets [24]. ...
As a complex system, the complicated interactions between chemical ingredients, as well as the potential rules of interactive associations among chemical ingredients of traditional Chinese herbal formulae are not yet fully understood by modern science. On the other hand, network analysis is emerging as a powerful approach focusing on processing complex interactive data. By employing network approach in selected Chinese herbal formulae for the treatment of coronary heart disease (CHD), this article aims to construct and analyze chemical ingredients network of herbal formulae, and provide candidate herbs, chemical constituents, and ingredient groups for further investigation. As a result, chemical ingredients network composed of 1588 ingredients from 36 herbs used in 8 core formulae for the treatment of CHD was produced based on combination associations in herbal formulae. In this network, 9 communities with relative dense internal connections are significantly associated with 14 kinds of chemical structures with P<0.001. Moreover, chemical structural fingerprints of network communities were detected, while specific centralities of chemical ingredients indicating different levels of importance in the network were also measured. Finally, several distinct herbs, chemical ingredients, and ingredient groups with essential position in the network or high centrality value are recommended for further pharmacology study in the context of new drug development.
... On the basis of the compatibility network of drugs, three BFs for psoriasis and four BFs for eczema were found after parameter optimization. A genetic algorithm was applied to find the core and effective formula (i.e., BF) from a lung cancer dataset with 595 records of 161 patients [Yang et al., 2013b]. Nine BFs with positive fitness values consisted of 15 distinct herbs. ...
Preclinical Research
Network pharmacology, based on the theory of systems biology, is a new discipline that analyzes the biological network and screens out the nodes of particular interest, with the aim of designing poly‐target drug molecule. It emphasizes maximizing drug efficacy and minimizing adverse effect via the multiple regulation of the signaling pathway. Coincidentally, almost all traditional C hinese medicine ( TCM ) and worldwide ethnomedicine exert therapeutic effect by targeting multiple molecules of the human body. In this overview, we offer a critique on the present perception of TCM and network pharmacology; illustrate the utility of network pharmacology in the study of single herb, medicine pair, and TCM formula; and summarize the recent progress of TCM ‐based drug discovery inspired by network pharmacology. Network pharmacology could be of great help in decreasing drug attrition rate and thus is essential in rational and cost‐effective drug development. We also pinpoint the current TCM issues that could be tackled by the flexible combined use of network pharmacology and relevant disciplines.
