Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis

Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs under meridians context. Several studies have determined the Chinese herbal medicine (CHM) phytochemicals for intrinsic attribute or meridians classification based on artificial intelligence (AI) tools. However, it is challenging to represent the complex molecular structures with large heterogeneity through the current technologies. In addition, the multiple correspondence between herbs and meridians has not been paid much attention.

AIM OF THE STUDY

We aim to develop an AI framework to classify multi-target meridians through the topological structure of phytochemicals.

MATERIALS AND METHODS

A total of 354 anti-cancer herbs, their corresponding TCM meridians and 5471 ingredient compounds were collected from public databases of CancerHSP, ETCM, and Hit 2.0. The statistical analysis of herbal and compound datasets, clustering analysis of the associated cancers, and correlational analysis of meridian tropism were preliminary conducted. Then a deep learning (DL) hybrid model named GRMC consisting of graph convolutional network (GCN) and recurrent neural network (RNN) was employed to generate the meridian multi-label sequences based on molecular graph.

RESULTS

The curing herbs against tumors have tight relationships to lung, liver, stomach, and spleen meridians. These herbs behave different properties in curing certain cancer. Certain cancer types have co-occurrence such as ovarian, bladder and cervical cancer. Compounds have multitarget meridians with characteristics of higher-order correlations. Compared with the other state-of-the-art algorithms on the datasets and previous methods dealing with conventional fixed fingerprints of herbal compounds, the proposed GRMC has superior overall performance on testing dataset with the one error of 0.183, hamming loss of 0.112, mean averaged accuracy (MAA) of 0.855, mean averaged precision (MAP) of 0.891, mean averaged recall (MAR) of 0.812, and mean averaged F1 score (MAF) of 0.849.

CONCLUSIONS

The proposed method can predict multi-targeted meridians through neural graph features in herbal compounds and outperforms several comparison methods. It could provide a basis for understanding the molecular scientific evidence of TCM meridians.

Reveal the Patterns of Prescriptions for Recurrent Respiratory Tract Infections' Treatment Based on Multiple Illustrious Senior Traditional Chinese Medicine Practitioners

Background

Recurrent respiratory tract infections (RRTIs) are one of the most common diseases in children and adolescents. The causes of RRTIs are various. In addition to the factors related to infection, basic diseases such as respiratory system, immune system, and digestive system are also involved. The cost of patients' frequent medical treatment and hospitalization has been deemed to be a heavy burden to the society and family. In China, traditional Chinese medicine (TCM) is commonly used to treat RRTIs. TCM treatment has been appraised to be effective, for reducing the number of hospital stays. Illustrious senior TCM practitioners of pediatrics are recognized as a group of outstanding physicians with significantly better patient outcomes. However, different illustrious senior TCM practitioners can lead to differences in treatment strategies due to factors such as region, prescription theory, and individual differences of patients. This makes it difficult for the experience of illustrious senior TCM practitioners to be popularized. However, there have been no prescription mining studies for the treatment of RRTIs based on different and multiple illustrious senior TCM practitioners. We explored the core prescriptions and drug mechanisms through data mining based on the prescriptions of illustrious senior TCM practitioners treating RRTIs from different clinical settings. This is important to promote the effective treatment of RRTIs with TCM. The objective of this study is to reveal the strategies (core prescriptions) from the prescriptions of multiple illustrious senior TCM practitioners for the treatment of RRTIs. We hope that this core prescription can help all TCM pediatricians to improve RRTIs children's outcome. Meanwhile, it could provide a new way for researchers to study the treatment of RRTIs.

Methods

In this study, we prospectively collected 400 children's prescriptions with RRTIs receiving TCM treatment from four illustrious senior TCM practitioners in different hospitals. We described and analyzed the characteristics of TCM prescriptions. The prescription regularity was analyzed by hierarchical clustering and association rules. Network pharmacology methods has been used to reveal the pathway mechanism of core prescriptions which have been mined and visualized with the help of SymMap, Genecards, KEGG, Metascape databases, and R. The execution of all methods was completed in May 2022.

Results

According to RRTIs multiple clinical syndromes, five new prescriptions were obtained based on illustrious senior TCM practitioners. Among them, the prescription composed of Scutellariae radix (Huangqin), Armeniacae semen amarum (Kuxingren), Peucedani radix (Qianhu), and Pheretima (Dilong) is the core strategy for the treatment of RRTIs. Cold herbs and heat herbs in the core prescription are approximately equal. Scutellariae radix (Huangqin) was dominant, and other herbs exert synergistic effects. The core prescription covered 76 pathways and 226 herb-disease genes. It promotes the differentiation of Th1, Th2, and Th17 cells and the secretion of inflammatory factors through toll-like receptor signaling pathway in the immune system, T cell receptor signaling pathway, and PPAR signaling pathway in the endocrine system, thereby exerting immune regulation and anti-inflammation.

Conclusion

In this study, we revealed the prescription regularity of TCM in the treatment of RRTIs and analyzed the mechanism of core prescriptions, which provided new ideas for the treatment of RRTIs.

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.

Exploring the Core Prescription and Underlying Mechanism of Traditional Chinese Medicine in Treating Allergic Rhinitis in Children: A Real- World Study Based on an Illustrious Senior Traditional Chinese Medicine Practitioner

BACKGROUND

Traditional Chinese medicine (TCM) is widely used to treat allergic rhinitis (AR) in China, especially in children. However, due to the complicated composition rules and unclear underlying mechanisms, effective herbal prescriptions' popularization and application are limited.

PURPOSE

This study tried to detect the core prescription of herbs in treating AR in children, reveal its mechanism based on the ingredients' network, and explore the main signaling pathways.

METHODS

We screened medical records of children patients with AR who were treated by TCM in DongZhiMen Hospital from Aug 2009 to Jan 2020 and adopted a descriptive analysis method on herbal characteristics. We used association rules to mine core prescriptions and used network pharmacology to establish the ingredient-target-pathway network through online databases and TCMSP, Genecards, KEGG pathway, Excel, R-Studio, and Cytoscape software.

RESULTS

The analysis of 1,092 clinical visits highlighted that the principle of formulating prescription was as follows: 'pungent and warm herbs were used more frequently while cold-natured herbs were paid equal attention as warm-natured herbs.' The core prescription was formed by FangFeng, BaiZhi, CangErzi, and ChanTui. These herbs covered 130 underlying targets and 141 signaling pathways of AR, which mainly had an effect on signal transduction and immunoregulation.

CONCLUSION

The core prescription based on these real-world clinical records includes FangFeng, BaiZhi, CangErzi, and ChanTui. It principally acts on targets of signal transduction pathways and immune pathways.

Cold-hot nature identification of Chinese herbal medicines based on the similarity of HPLC fingerprints

The nature theory of Chinese herbal medicines (CHMs) is the core theory of traditional Chinese medicine (TCM). Cold-hot nature is an important part of CHM nature. It is found that the material basis of cold-hot nature is CHM ingredients. To test the scientific hypothesis that “CHMs with similar cold-hot nature should have similar material basis,” we explored an intelligent method for cold-hot nature identification of CHMs based on the feature similarity of CHM ingredients in this work. Sixty one CHMs were selected for cold-hot nature identification. High performance liquid chromatography (HPLC) was used to separate the chemical ingredients of CHMs and extract the feature information of CHM ingredients. A distance metric learning algorithm was then learned to measure the similarity of HPLC fingerprints. With the learned distance metric, cold-hot nature identification scheme (CHNIS) was proposed to build an identification model to evaluate the cold-hot nature of CHMs. A number of experiments were designed to verify the effectiveness and feasibility of the proposed CHNIS model. The total identification accuracy rate of 61 CHMs is 80.3%. The performance of the proposed CHNIS algorithm outperformed that of the compared classical algorithms. The experimental results confirmed our inference that CHMs with similar cold-hot nature had similar composition of substances. The CHNIS model was proved to be effective and feasible.

Mechanism of Jujube (Ziziphus jujuba Mill.) Fruit in the Appetite Regulation Based on Network Pharmacology and Molecular Docking Method

Objective

To investigate the mechanism of jujube (Ziziphus jujuba Mill.) in appetite regulation based on network pharmacology.

Methods

The active components and action targets of jujube were retrieved through the TCMSP and TCMID databases. GeneCards, DisGeNet, Therapeutic Target Database, and OMIM were used to screen the related targets for appetite, appetite suppression, and appetite regulation, and the intersection target of the two was selected. A protein-protein interaction (PPI) network was constructed. Important protein nodes and subnets were predicted based on the cytoHubba plug-in, and the hub gene was screened. Additionally, GO and KEGG pathway analyses were performed to obtain potential biological processes and signaling pathways of key targets. And the active ingredient-target-action pathway diagram was constructed.

Results

A total of 16 active components were screened from jujube, including 131 action targets related to appetite and appetite regulation. Three key targets (MAOA, MMP2, and HSPB1) were screened out by MCODE analysis. KEGG enrichment analysis was mainly enriched in neuroactive ligand-receptor interaction, serotonin-containing synapse, gap junction, cAMP signaling pathway, and dopaminergic synapse. Molecular docking results showed that the components coclaurine, (−)-catenin, (+)-stepholidine, berberine, cianidanol, coclaurine, and moupinamide in jujube had strong binding activity to the main targets (ESR1, ADRA2C, and MMP2).

Conclusion

Based on network pharmacology, the appetite modulating effects of jujube on multiple components, targets, and channels were explored, and the main active components of jujube were predicted to act on multiple signaling pathways to regulate appetite. The molecular docking results showed that the components in jujube had strong binding activity to the main targets, which provided new ideas and methods to further investigate the mechanisms of appetite regulation by jujube.

Quantification of “Cold-Hot” Medicinal Properties of Chinese Medicines Based on Primary Metabolites and Fisher’s Analysis

Background

Chinese medicinal properties (CMP) are an important part of the basic theory of traditional Chinese medicines (TCMs). Quantitative research on the properties of TCMs is of great significance to deepen the understanding and application of the theory of drug properties and promoting the modernization of TCMs. However, these studies are limited to strong subjectivity or distinguish different drug properties based on certain indicators since CMP studies are diverse.

Objective

To realize quantitative comparison of same medicinal properties of different Chinese medicines.

Method

To solve the above problem, we proposed and explored quantification of Chinese medicinal properties (QMP) and the quantification value of medicinal properties “R”. The correlation between primary metabolites and “cold-hot” medicinal properties was explored on the premise of material basis of Chinese herbal medicines and Fisher's analysis. Based on indicators related to “cold-hot” medicinal properties, we utilized quantitative values “R” to characterize the strength or weakness of “cold-hot” medicinal properties.

Results

According to QMP, the same medicinal properties were quantified and compared by quantification value of medicinal properties that expressed by alphabet “R”. The general theoretical formula of “R” deduced is R = (‖l‖ × cos θ)/‖L‖ = ∑_i=1ⁿj_ip_i/∑_i=1ⁿp_i², in which n ≥ 1. In the light of formula of “R” and indicators related to “cold-hot” medicinal properties, we got “R” value of “cold-cool” and “warm-hot” medicinal properties. “R” values of “cold-cool” medicinal properties of Phellodendri chinensis cortex, Coptidis rhizoma, and Menthae haplocalycis herba were 0.63, 1.00, and 0.49, respectively. The result showed that Coptidis rhizoma is the most “cold-cool”, followed by Phellodendri chinensis cortex, with Menthae haplocalycis herba is the weakest in the three Chinese medicines, consistent with cognition of TCM theory.

Conclusion

QMP has certain guiding significance for the quantification of “cold and hot” drug properties. “R” is feasible to realize the quantitative comparison of the same drug properties of different traditional Chinese medicine, which is helpful to promote process of modern Chinese medicine construction.

Ethnopharmacological Survey of Traditional Chinese Medicine Pharmacy Prescriptions for Dysmenorrhea

Chinese herbal medicines have long been used for the treatment of dysmenorrhea. The treatment experiences of traditional Chinese medicine (TCM) pharmacies passed down through generations have contributed to a wealth of prescriptions for dysmenorrhea that have achieved significant therapeutic effects in countless Taiwanese women. Therefore, surveying and analyzing these prescriptions may enable us to elucidate the core medication combinations used in TCM prescriptions for dysmenorrhea. In the present study, a field investigation was conducted on various TCM pharmacies in Taiwan. A total of 96 TCM pharmacies were sampled, and 99 prescriptions for dysmenorrhea containing 77 different medicinal materials were collected. Compositae (8%) was the most common botanical source of the medicinal materials, and the predominant TCM property and flavor of the materials were warm (45%) and sweet (73%), respectively. The blood-activating and stasis-dispelling effect (23%) and the qi-tonifying effect (23%) were the most prevalent traditional effects, and the modern pharmacological effects most commonly found in the materials were anti-inflammatory (73%), antitumor (59%), and analgesic (12%) effects. Network analysis of the 77 medicinal materials used in the prescriptions, which was performed using the Traditional Chinese Medicine Inheritance Support System, yielded seven core medicinal materials and the corresponding network diagram. The seven core medicinal materials ranked in order of relative frequency of citation (RFC) were Angelica sinensis (Oliv.) Diels (Dang Gui), Ligusticum chuanxiong Hort (Chuan Qiong), Rehmannia glutinosa Libosch (Di Huang), Paeonia lactiflora Pall (Bai Shao), Hedysarum polybotrys Hand.-Mazz (Hong Qi), Lycium chinense Mill (Gou Qi Zi), and Cinnamomum cassia (L.). J. Presl (Gui Zhi). A total of 58 combinations, each consisting of two to five of the seven medicinal materials and 107 association rules among the materials, were identified. This study provides a record of valuable knowledge on TCM pharmacy prescriptions for dysmenorrhea. The rich medicinal knowledge of TCM pharmacies in Taiwan is worthy of further exploration, and the results of this study can serve as a basis for future pharmacological research and the development of naturally derived medications for dysmenorrhea.

Prediction of the taxonomical classification of the Ranunculaceae family using a machine learning method

A machine learning method is successfully applied to determine lineage-specific features among various genera within the Ranunculaceae family.

"Efficacy-Nature-Structure" Relationship of Traditional Chinese Medicine Based on Chemical Structural Data and Bioinformatics Analysis

Traditional Chinese medicines (TCMs) have wide pharmacological activities, and the ingredients in individual TCMs determine their efficacies. To understand the "efficacy-nature-structure" relationship of TCM, compounds from 2444 kinds of herbs were collected, and the associations between family, structure, nature, and biological activities were mined and analyzed. Bernoulli Naïve Bayes profiling and a data analysis method were used to predict the targets of compounds. The results show that genetic material determined the representation of ingredients from herbs and the nature of TCMs and that the superior scaffolds of compounds of cold nature were 2-phenylochrotinone, anthraquinone, and coumarin, while the compounds of hot nature were cyclohexene. The results of the similarity analysis and distribution for molecular descriptors of compounds show that compounds associated with the same nature were similar and compounds associated with different natures occurred as a transition in part. As for integral compounds from 2-phenylochrotinone, anthraquinone, coumarin, and cyclohexene, the value of the shape index increased, indicating the transition of scaffolds from a spherical structure to a linear structure, with various molecular descriptors decreasing. Three medicines and three recipes prescribed based on "efficacy-nature-structure" had a higher survival rate in the clinic and provided powerful evidence for TCM principles. The research improves the understanding of the "efficacy-nature-structure" relationship and extends TCM applications.

Nature Identification of Chinese Herbal Medicine Compounds Based on Molecular Descriptors

BACKGROUND

The nature of Chinese herbal medicines (CHMs) is a bridge between traditional Chinese medicine and clinical application. Accurate nature identification of CHMs is essential for guiding the clinical application of CHMs.

OBJECTIVE

To develop a new method for nature identification of CHMs according to compounds in CHMs.

METHODS

The nature of a CHM is a comprehensive manifestation of the properties of various compounds in the CHM. In this study, 2012 CHM compounds were extracted to construct a compound data set. Molecular descriptors were utilized to build an identification model for classification of the cold-hot-neutral nature of CHM compounds.

RESULTS

The predictive accuracy and confusion matrix were validated using the assembled data set. The best model produced accuracies of 96.5 ± 0.5% and 86.5 ± 1.5% on training set and test set, respectively. Furthermore, the identification model is robust in predicting the cold-hot-neutral nature of CHM compounds.

CONCLUSION

This work shows how a classification model for medical nature identification can be developed. The derived model can be utilized for the application of CHMs.

HIGHLIGHTS

To construct a nature identification model for analysis of the cold-hot-neutral nature of CHMs according to the compounds in CHMs.

Cold-hot nature identification based on GC similarity analysis of Chinese herbal medicine ingredients

The theory of cold-hot nature of Chinese herbal medicines (CHMs) is the core theory of CHM. It has been found that the volatile oil ingredients in CHMs are closely related to their cold-hot nature. Guided by the scientific hypothesis that "CHMs with similar component substances should have similar medicinal natures", exploration of the intelligent identification of the cold-hot nature of CHMs based on the similarity of their volatile oil ingredients has become a research focus. Gas chromatography (GC) chemical fingerprints have been widely used in the separation of volatile oil ingredients to analyze the cold-hot nature of CHMs. To verify the above hypothesis, in this work, we study the quantification of the similarity of the volatile oil ingredients of CHMs to their fingerprint similarity and explore the relationship between the volatile oil ingredients of CHMs and their cold-hot nature. In this study, we utilize GC technology to analyze the chemical ingredients of 61 CHMs that have a clear cold-hot nature (including 30 'cold' CHMs and 31 'hot' CHMs). Using the constructed fingerprint dataset of CHMs, a distance metric learning algorithm is applied to measure the similarity of the GC fingerprints. Furthermore, an improved k-nearest neighbor (kNN) algorithm is proposed to build a predictive identification model to identify the cold-hot nature of CHMs. The experimental results prove our inference that CHMs with similar component substances should have similar medicinal natures. Compared with existing classical models, the proposed identification scheme has better predictive performance. The proposed prediction model is proved to be effective and feasible.

Investigating the Biomarkers of the Sasang Constitution via Network Pharmacology Approach

Sasang constitutional (SC) medicine classifies people into Soeum (SE), Soyang (SY), Taeeum (TE), and Taeyang (TY) types based on psychological and physical traits. However, biomarkers of these types are still unclear. We aimed to identify biomarkers among the SC types using network pharmacology methods. Target genes associated with the SC types were identified by grouping herb targets that preserve and strengthen the requisite energy (Bomyeongjiju). The herb targets were obtained by constructing an herb-compound-target network. We identified 371, 185, 146, and 89 target genes and their unique biological processes related to SE, SY, TE, and TY types, respectively. While the targets of SE and SY types were the most similar among the target pairs of the SC types, those of TY type overlapped with only a few other SC-type targets. Moreover, SE, SY, TE, and TY were related to "diseases of the digestive system," "diseases of the nervous system," "endocrine, nutritional, and metabolic diseases," and "congenital malformations, deformations, and chromosomal abnormalities," respectively. We successfully identified the target genes, biological processes, and diseases related to each SC type. We also demonstrated that a drug-centric approach using network pharmacology analysis provides a deeper understanding of the concept of Sasang constitutional medicine at a phenotypic level.

New insights into molecular mechanisms of "Cold or Hot" nature of food: When East meets West

Traditional Chinese medicines are largely adopted in China and have a key importance in the world medical system. Cold-hot nature is the important characteristics of food and Chinese Materia Medica in the traditional Chinese medicine, relating to food functions in the organism. As compared to the studies on the cold and hot nature in Chinese medicine, the research studies carried out to establish the association between cold-hot nature and food are insufficient. Intending to investigate the criteria to discriminate the cold-hot nature of food and Chinese medicine scientifically, this review collected the cold-hot nature-related literature in recent 20 years in several popular databases such as PubMed, Google Scholar, and Science Direct. This review explored that the cold and hot natures are not only linked to the chemical components such as water, carbohydrates, lipids, and amino acids, but also correlated to the biological effects, comprising of energy metabolism, inflammation response, oxidation reaction, immune response, and cell growth and proliferation. Besides, this review further put forward the possibility that cold-hot nature of food and Chinese medicine exert different biological effects on the inflammatory response via regulating the signaling pathways viz. NF-κB and MAPK. More extensive studies are needed to consider the overall connections between both the biological effects and chemical components and how food processing affects the cold-hot nature of the food.

A Song of Ice and Fire: Cold and Hot Properties of Traditional Chinese Medicines

The theory of cold and hot properties is the basic theory of traditional Chinese medicines (TCMs) and has been successfully applied to combat human diseases for thousands of years. Although the theory of cold and hot is very important to guide the clinical application of TCMs, this ancient theory remains an enigma for a long time. In recent years, more and more researchers have tried to uncover this ancient theory with the help of modern techniques, and the cold and hot properties of a myriad of TCMs have been studied. However, there is no review of cold and hot properties. In this review, we first briefly introduced the basic theories about cold and hot properties, including how to distinguish between the cold and hot properties of TCMs and the classification and treatment of cold and hot syndromes. Then, focusing on the application of cold and hot properties, we take several important TCMs with cold or hot property as examples to summarize their traditional usage, phytochemistry, and pharmacology. In addition, the mechanisms of thermogenesis and antipyretic effect of these important TCMs, which are related to the cold and hot properties, were summarized. At the end of this review, the perspectives on research strategies and research directions of hot and cold properties were also offered.

Evidence of TCM Theory in Treating the Same Disease with Different Methods: Treatment of Pneumonia with Ephedra sinica and Scutellariae Radix as an Example

Pneumonia is a serious global health problem and the leading cause of mortality in children. Antibiotics are the main treatment for bacterial pneumonia, but there are serious drug resistance problems. Traditional Chinese medicine (TCM) has been used to treat diseases for thousands of years and has a unique theory. This article takes the treatment of pneumonia with Ephedra sinica as a representative hot medicine and Scutellariae Radix as a representative cold medicine as an example. We explore and explain the theory of treating the same disease with different TCM treatments. Using transcriptomics and network pharmacology methods, GO, KEGG enrichment, and PPI network construction were carried out, demonstrating that Ephedra sinica plays a therapeutic role through the NF-κB and apoptosis signaling pathways targeting PLAU, CD40LG, BLC2L1, CASP7, and CXCL8. The targets of Scutellariae Radix through the IL-17 signaling pathway are MMP9, CXCL8, and MAPK14. Molecular docking technology was also used to verify the results. In short, our results provide evidence for the theory of treating the same disease with different treatments, and we also discuss future directions for traditional Chinese medicine.

Exploring the 'cold/hot' properties of traditional Chinese medicine by cell temperature measurement

Context: It is common sense that chewing a mint leaf can cause a cooling feeling, while chewing ginger root will produce a burning feeling. In Traditional Chinese Medicine (TCM), this phenomenon is referred to as 'cold/hot' properties of herbs. Herein, it is reported that TCM with different "cold/hot" properties have different effects on the variation of cells.Objective: To explore the intrinsic 'cold/hot' properties of TCM from the perspective of cellular and molecular biology.Materials and methods: A375 cells were selected using Cancer Cell Line Encyclopaedia (CCLE) analysis and western blots. Hypaconitine and baicalin were selected by structural similarity analysis from 56 and 140 compounds, respectively. A wireless thermometry system was used to measure cellular temperature change induced by different compounds. Alteration of intracellular calcium influx was investigated by means of calcium imaging.Results: The IC₅₀ values of GSK1016790A, HC067047, hypaconitine, and baicalin for A375 cells are 8.363 nM, 816.4 μM, 286.4 μM and 29.84 μM, respectively. And, 8 μM hypaconitine induced obvious calcium influx while 8 μM baicalin inhibited calcium influx induced by TRPV4 activation. Cellular temperature elevated significantly when treated with GSK1016790A or hypaconitine, while the results were reversed when cells were treated with HC067047 or baicalin.Discussion and conclusions: The changes in cellular temperature are speculated to be caused by the alteration of intracellular calcium influx mediated by TRPV4. In addition, the 'cold/hot' properties of compounds in TCM can be classified by using cellular temperature detection.

Data Mining and Systematic Pharmacology to Reveal the Mechanisms of Traditional Chinese Medicine in Recurrent Respiratory Tract Infections' Treatment

Traditional Chinese medicine (TCM) was widely used in the treatment of recurrent respiratory tract infections (RRTIs) in East Asia, but its mechanism was not clear because of its complex prescription rules. This research prospectively collected 100 prescriptions of RRTI children treated with TCM. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by hierarchical clustering and association rules. The results showed that the principle of RRTI was to pay equal attention to cold and mild, and six new meaningful prescriptions were obtained. Among them, the new prescription composed of Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), Saposhnikoviae Radix (Fangfeng), Angelicae Sinensis Radix (Danggui), and Paeoniae Radix Rubra (Chishao) was an important method to treat RRTI. In order to explore the mechanism of the new prescription, the research obtained the action target of each herb of the core prescription on Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine, TCMIP v2.0. The target genes were enriched by Metascape, and 93 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained. According to the classification and statistics of KEGG type, it was found that the new prescription mainly intervened in the metabolic pathway dominated by amino acid metabolism. In addition, there were also many interventions in the nervous system-, endocrine system-, and digestive system-related pathways. This study summarized the prescription rule of TCM in the treatment of RRTI, analyzed the mechanism of supplementing deficiency, and provided a new idea for the treatment of RRTI.

Identifying potential treatments of COVID-19 from Traditional Chinese Medicine (TCM) by using a data-driven approach

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions.

AIM OF THE STUDY

This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19.

MATERIALS AND METHODS

We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism.

RESULTS

574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat "Warm diseases (Wenbing)", "Pestilence (Wenyi or Yibing)" or "Epidemic diseases (Shiyi)". Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases.

CONCLUSIONS

These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds.

Cross-Species Annotation of Expressed Genes and Detection of Different Functional Gene Modules Between 10 Cold- and 10 Hot-Propertied Chinese Herbal Medicines

According to the traditional Chinese medicine (TCM) system, Chinese herbal medicines (HMs) can be divided into four categories: hot, warm, cold, and cool. A cool nature usually is categorized as a cold nature, and a warm nature is classified as a hot nature. However, the detectable characteristics of the gene expression profile associated with the cold and hot properties have not been studied. To address this question, a strategy for the cross-species annotation of conserved genes was established in the present study by using transcriptome data of 20 HMs with cold and hot properties. Functional enrichment analysis was performed on group-specific expressed genes inferred from the functional genome of the reference species (i.e., Arabidopsis). Results showed that metabolic pathways relevant to chrysoeriol, luteolin, paniculatin, and wogonin were enriched for cold-specific genes, and pathways of inositol, heptadecane, lauric acid, octanoic acid, hexadecanoic acid, and pentadecanoic acid were enriched for hot-specific genes. Six functional modules were identified in the HMs with the cold property: nucleotide biosynthetic process, peptidy-L-cysteine S-palmitoylation, lipid modification, base-excision repair, dipeptide transport, and response to endoplasmic reticulum stress. For the hot HMs, another six functional modules were identified: embryonic meristem development, embryonic pattern specification, axis specification, regulation of RNA polymerase II transcriptional preinitiation complex assembly, mitochondrial RNA modification, and cell redox homeostasis. The research provided a new insight into HMs’ cold and hot properties from the perspective of the gene expression profile of plants.

Predicting the Associations between Meridians and Chinese Traditional Medicine Using a Cost-Sensitive Graph Convolutional Neural Network

Natural products are the most important and commonly used in Traditional Chinese Medicine (TCM) for healthcare and disease prevention in East-Asia. Although the Meridian system of TCM was established several thousand years ago, the rationale of Meridian classification based on the ingredient compounds remains poorly understood. A core challenge for the traditional machine learning approaches for chemical activity prediction is to encode molecules into fixed length vectors but ignore the structural information of the chemical compound. Therefore, we apply a cost-sensitive graph convolutional neural network model to learn local and global topological features of chemical compounds, and discover the associations between TCM and their Meridians. In the experiments, we find that the performance of our approach with the area under the receiver operating characteristic curve (ROC-AUC) of 0.82 which is better than the traditional machine learning algorithm and also obtains 8%–13% improvement comparing with the state-of-the-art methods. We investigate the powerful ability of deep learning approach to learn the proper molecular descriptors for Meridian prediction and to provide novel insights into the complementary and alternative medicine of TCM.

Predicting Meridian in Chinese traditional medicine using machine learning approaches

Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.

Linking endotypes to omics profiles in difficult-to-control asthma using the diagnostic Chinese medicine syndrome differentiation algorithm

Objective: Patients with difficult-to-control asthma have difficulty breathing almost all of the time, even leading to life-threatening asthma attacks. However, only few diagnostic markers for this disease have been identified. We aimed to take advantage of unique Chinese medicine theories for phenotypic classification and to explore molecular signatures in difficult-to-control asthma. Methods: The Chinese medicine syndrome differentiation algorithm (CMSDA) is a syndrome-scoring classification method based on the Chinese medicine overall observation theory. Patients with difficult-to-control asthma were classified into Cold- and Hot-pattern groups according to the CMSDA. DNA methylation and metabolomic profiles were obtained using Infinium Human Methylation 450 BeadChip and gas chromatography-mass spectrometer. Subsequently, an integrated bioinformatics analysis was performed to compare those two patterns and identify Cold/Hot-associated candidates, followed by functional validation studies. Results: A total of 20 patients with difficult-to-control asthma were enrolled in the study. Ten were grouped as Cold and 10 as Hot according to the CMSDA. We identified distinct whole-genome DNA methylation and metabolomic profiles between Cold- and Hot-pattern groups. ALDH3A1 gene exhibited variations in the DNA methylation probe cg10791966, while two metabolic pathways were associated with those two patterns. Conclusions: Our study introduced a novel diagnostic classification approach, the CMSDA, for difficult-to-control asthma. This is an alternative way to categorize diverse syndromes and link endotypes with omics profiles of this disease. ALDH3A1 might be a potential biomarker for precision diagnosis of difficult-to-control asthma.

OptiPharm: An evolutionary algorithm to compare shape similarity

Virtual Screening (VS) methods can drastically accelerate global drug discovery processes. Among the most widely used VS approaches, Shape Similarity Methods compare in detail the global shape of a query molecule against a large database of potential drug compounds. Even so, the databases are so enormously large that, in order to save time, the current VS methods are not exhaustive, but they are mainly local optimizers that can easily be entrapped in local optima. It means that they discard promising compounds or yield erroneous signals. In this work, we propose the use of efficient global optimization techniques, as a way to increase the quality of the provided solutions. In particular, we introduce OptiPharm, which is a parameterizable metaheuristic that improves prediction accuracy and offers greater computational performance than WEGA, a Gaussian-based shape similarity method. OptiPharm includes mechanisms to balance between exploration and exploitation to quickly identify regions in the search space with high-quality solutions and avoid wasting time in non-promising areas. OptiPharm is available upon request via email.

The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs

"Yin-Yang" and "Five Elements" theories are the basis theories of Traditional Chinese Medicine (TCM). To probe and clarify the theoretical basis of these ancient Chinese wisdoms, extensive efforts have been taken, however, without a full success. In the classification of TCM herbs, hot, cold and neutral herbs are believed to possess distinct profile of chemical compositions of which the compounds should have different polarity and mass: this view provides a new perspective for further illustration. To understand the chemical properties of TCMs in the classification of "Yin-Yang" and "Five Elements," 15 commonly used herbs attributed to spleen-meridian were selected for analyses. Chemically standardized water extracts, 50% ethanol extracts and 90% ethanol extracts were prepared and subjected to different analytic measurements. Principle component analysis (PCA) of full spectrum of HPLC, NMR and LC-MS of the extracts were established. The results revealed that the LC-MS profile showed a strong correlation with the "Yin-Yang" classification criterion. The Yang-stimulating herbs generally contain more compounds with lower molecular weight and less polar property. Additionally, a comprehensive anti-oxidative profiles of selected herbs were developed, and the results showed that its correlation with cold and hot properties of TCM, however, was rather low. Taken together, the "Yin-Yang" nature of TCM is closely related to the physical properties of the ingredients, such as polarity and molecular mass; while such classification has little correlation with anti-oxidative property. Therefore, the present results provide a new direction in probing the basic principle of TCM classification.

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

TCMAnalyzer: A Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has been widely used and proven effective in long term clinical practice. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This is one of the major roadblocks in TCM modernization. In order to solve this problem, we have developed TCMAnalyzer, which is a free web-based toolkit allowing a user to (1) identify the potential compounds that are responsible for the bioactivities for a TCM herb through scaffold-activity relation searches using structural search techniques, (2) investigate the molecular mechanism of action for a TCM herb at the systemic level, and (3) explore the potentially targeted bioactive herbs. The toolkit can result in TCM networks that demonstrate the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. These networks are graphically depicted to reveal the mechanism of actions for a TCM herb or to identify new molecular scaffolds for new chemotherapies. TCMAnalyzer is freely available at http://www.rcdd.org.cn/tcmanalyzer .

Integrated Modules Analysis to Explore the Molecular Mechanisms of Phlegm-Stasis Cementation Syndrome with Ischemic Heart Disease

Background: Ischemic heart disease (IHD) has been the leading cause of death for several decades globally, IHD patients usually hold the symptoms of phlegm-stasis cementation syndrome (PSCS) as significant complications. However, the underlying molecular mechanisms of PSCS complicated with IHD have not yet been fully elucidated. Materials and Methods: Network medicine methods were utilized to elucidate the underlying molecular mechanisms of IHD phenotypes. Firstly, high-quality IHD-associated genes from both human curated disease-gene association database and biomedical literatures were integrated. Secondly, the IHD disease modules were obtained by dissecting the protein-protein interaction (PPI) topological modules in the String V9.1 database and the mapping of IHD-associated genes to the PPI topological modules. After that, molecular functional analyses (e.g., Gene Ontology and pathway enrichment analyses) for these IHD disease modules were conducted. Finally, the PSCS syndrome modules were identified by mapping the PSCS related symptom-genes to the IHD disease modules, which were further validated by both pharmacological and physiological evidences derived from published literatures. Results: The total of 1,056 high-quality IHD-associated genes were integrated and evaluated. In addition, eight IHD disease modules (the PPI sub-networks significantly relevant to IHD) were identified, in which two disease modules were relevant to PSCS syndrome (i.e., two PSCS syndrome modules). These two modules had enriched pathways on Toll-like receptor signaling pathway (hsa04620) and Renin-angiotensin system (hsa04614), with the molecular functions of angiotensin maturation (GO:0002003) and response to bacterium (GO:0009617), which had been validated by classical Chinese herbal formulas-related targets, IHD-related drug targets, and the phenotype features derived from human phenotype ontology (HPO) and published biomedical literatures. Conclusion: A network medicine-based approach was proposed to identify the underlying molecular modules of PSCS complicated with IHD, which could be used for interpreting the pharmacological mechanisms of well-established Chinese herbal formulas (e.g., Tao Hong Si Wu Tang, Dan Shen Yin, Hunag Lian Wen Dan Tang and Gua Lou Xie Bai Ban Xia Tang). In addition, these results delivered novel understandings of the molecular network mechanisms of IHD phenotype subtypes with PSCS complications, which would be both insightful for IHD precision medicine and the integration of disease and TCM syndrome diagnoses.

Network pharmacology exploration reveals endothelial inflammation as a common mechanism for stroke and coronary artery disease treatment of Danhong injection

Although Danhong injection (DHI) is the most widely prescribed Chinese medicine for both stroke and coronary artery disease (CAD), its underlying common molecular mechanisms remain unclear. An integrated network pharmacology and experimental verification approach was used to decipher common pharmacological mechanisms of DHI on stroke and CAD treatment. A compound-target-disease & function-pathway network was constructed and analyzed, indicating that 37 ingredients derived from DH (Salvia miltiorrhiza Bge., Flos Carthami tinctorii and DHI) modulated 68 common targets shared by stroke and CAD. In-depth network analysis results of the top diseases, functions, pathways and upstream regulators implied that a common underlying mechanism linking DHI’s role in stroke and CAD treatment was inflammatory response in the process of atherosclerosis. Experimentally, DHI exerted comprehensive anti-inflammatory effects on LPS, ox-LDL or cholesterol crystal-induced NF-κB, c-jun and p38 activation, as well as IL-1β, TNF-α, and IL-10 secretion in vascular endothelial cells. Ten of 14 predicted ingredients were verified to have significant anti-inflammatory activities on LPS-induced endothelial inflammation. DHI exerts pharmacological efficacies on both stroke and CAD through multi-ingredient, multi-target, multi-function and multi-pathway mode. Anti-endothelial inflammation therapy serves as a common underlying mechanism. This study provides a new understanding of DHI in clinical application on cardiovascular and cerebrovascular diseases.