An Empirical Analysis of Director Mingjun Zhao’s Experience in Treating Chronic Heart Failure

With the aggravation of an aging population, the prevalence of chronic heart failure is increasing. As a famous traditional Chinese medicine practitioner in Shaanxi Province, Mingjun Zhao is amicable at using traditional Chinese medicine to treat chronic heart failure. He believes that the pathogenesis of the condition is mainly due to the lack of heart Yang, deficiency of heart Qi, as well as the combination of water and blood stasis. Therefore, in treating this condition, it is essential to warm and nourish heart Yang, replenish Qi to benefit water, and promote blood circulation to remove blood stasis. Based on the standardized treatment used in western medicine, traditional Chinese medicine should be reasonably and accurately combined with it as a drug pair into a prescription, thus highlighting the advantages of the combination of traditional Chinese medicine in the treatment of chronic heart failure to a great extent.

A Network Pharmacology Study to Explore Mechanism of the Drug Pair of Astragalus-Saposhnikoviae Radix in the Treatment of Allergic Rhinitis

Allergic rhinitis (AR) has now become one of the major diseases affecting people’s lives, and Traditional Chinese medicine (TCM) always has good efficacy in clinical treatment. In the present study, we analyzed the most frequently used drug pair of Astragalus-Saposhnikoviae Radix (SR) in prescriptions for the treatment of allergic rhinitis by network pharmacology to reveal the modern pharmacological mechanisms of drug prevention and treatment of the disease. Firstly, the 38 active ingredients with good ADME properties from the Astragalus-SR drug pair were collected from the database, and the collated drug targets of Astragalus and SR and the targets of allergic rhinitis were mapped against each other by the network visualization software Cytoscape, followed by the establishment of a “drug active ingredient-target-disease” network diagram and the construction of a high-confidence protein-protein interaction network. Then, the common targets obtained from the disease and drug active ingredients were imported by R language for GO enrichment analysis and KEGG pathway enrichment analysis. The KEGG pathways associated with the targets of Astragalus and SR for the treatment of allergic rhinitis obtained from R enrichment analysis were imported into Cytoscape, and the CytoNCA plug-in was loaded to construct a “target-pathway” network map, and the core target wogonin (FN1) was screened. These evidences suggest that the drug pair of Astragalus-SR works in a multi-component, multi-target and integrated modulation manner for the treatment of allergic rhinitis, which provides an important basis for the treatment of allergic rhinitis.

Network Pharmacology-Based Analysis on the Mechanism of Drug Pair of Astragalus Membranaceus and Acorus Tatarinowii in the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is a degenerative disease of central nervous system, which seriously threatens the life and health of the elderly people. It has been for long time that Traditional Chinese medicine (TCM) treatment for AD is effective. This study analyzed the potential target and molecular mechanism of the most often used drug pair of Astragalus membranaceus and Acorus tatarinowii to treat AD by network pharmacological method. Firstly, the method was performed to screen and sort out the active ingredients with good ADME properties and drug targets of Astragalus membranaceus and Acorus tatarinowii. Then, we searched for the disease targets related to AD, followed by the construction of the “active ingredients-target-disease” network. We implemented GO enrichment analysis and KEGG pathway enrichment analysis of related overlapped target proteins, and then constructed the “target-pathway” network diagram. Finally, the above overlapped target proteins are mapped to build a PPI high-position protein interoperability network, and we conducted the network topology analysis to screen out the core targets of Astragalus membranaceus-Acorus tatarinowii drug pair in the treatment of AD. According to network pharmacology, this research predicted the potential targets of the drug pair of Astragalus membranaceus and Acorus tatarinowii in the treatment of AD, and explored that Astragalus membranaceus-Acorus tatarinowii drug pair in the treatment of AD was the overall systematic regulating action of “multiple-ingredients, multiple-target and multiple-pathway”. It affords the reference for understanding the pathogenesis of AD and exploring new therapeutic methods and drug development in the future.

Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of the Drug Pair Astragalus-Angelica for Treatment of Male Infertility

Background. The traditional Chinese medicines Astragalus and Angelica are often combined to treat male infertility, but the specific therapeutic mechanism is not clear. Therefore, this study applies a network pharmacology approach to investigate the possible mechanism of action of the drug pair Astragalus-Angelica (PAA) in the treatment of male infertility. Methods. Relevant targets for PAA treatment of male infertility are obtained through databases. Protein-protein interactions (PPIs) are constructed through STRING database and screen core targets, and an enrichment analysis is conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target protein and the ligand of PAA. Results. The active ingredients of 112 PAA, 980 corresponding targets, and 374 effective targets of PAA for the treatment of male infertility were obtained, which are related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway, IL-17 signaling pathway, and thyroid hormone signaling pathway. Conclusion. In this study, using a network pharmacology method, we preliminarily analyzed the effective components and action targets of the PAA. We also explored the possible mechanism of action of PAA in treating male infertility. They also lay a foundation for expanding the clinical application of PAA and provide new ideas and directions for further research on the mechanisms of action of the PAA and its components for male infertility treatment.

Exploring the Molecular Mechanism of Astragali Radix-Curcumae Rhizoma against Gastric Intraepithelial Neoplasia by Network Pharmacology and Molecular Docking

Background. Astragali Radix-Curcumae Rhizoma (ARCR), a classic drug pair, has been widely used for the treatment of gastric intraepithelial neoplasia (GIN) in China. However, the underlying mechanisms of this drug pair are still unknown. Thus, elucidating the molecular mechanism of ARCR for treating GIN is imperative. Methods. The active components and targets of ARCR were determined from the TCMSP database, and the differentially expressed genes related to GIN were identified from the GSE130823 dataset. The protein-protein interaction (PPI) network and ARCR-active component-target-pathway network were constructed by STRING 11.0 and Cytoscape 3.7.2, respectively. In addition, a receiver operating characteristic curve (ROC) was conducted to verify the key targets, and enrichment analyses were performed using R software. Molecular docking was carried out to test the binding capacity between core active components and key targets. Results. 31 active components were obtained from ARCR, among which 22 were hit by the 51 targets associated with GIN. Gene Ontology (GO) functional enrichment analysis showed that biological process (BP), molecular function (MF), and cellular component (CC) were most significantly enriched in response to a drug, catecholamine binding, and apical part of the cell, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated ARCR against GIN through regulation of neuroactive ligand-receptor interaction, nitrogen metabolism, calcium signaling pathway, chemical carcinogenesis-receptor activation, drug metabolism, gap junction, and cancers. In the PPI network, 15 potential targets were identified, of which nine key targets were proven to have higher diagnostic values in ROC. Molecular docking revealed a good binding affinity of active components (quercetin, bisdemethoxycurcumin, and kaempferol) with the corresponding targets (CYP3A4, CYP1A1, HMOX1, DRD2, DPP4, ADRA2A, ADRA2C, NR1I2, and LGALS4). Conclusion. This study revealed the active components and molecular mechanism by which ARCR treatment is effective against GIN through regulating multipathway, such as neuroactive ligand-receptor interaction, nitrogen metabolism, and calcium signaling pathway.

Standardized Astragalus Mongholicus Bunge-Curcuma Aromatica Salisb. Extract Efficiently Suppresses Colon Cancer Progression Through Gut Microbiota Modification in CT26-Bearing Mice

Altered gut microbiota and a damaged colon mucosal barrier have been implicated in the development of colon cancer. Astragalus mongholicus Bunge-Curcuma aromatica Salisb. (ACE) is a common herbal drug pair that widely used clinically to treat cancer. However, whether the anti-cancer effect of ACE is related to gut microbiota remains unclear yet. We standardized ACE and investigated the effects of ACE on tumour suppression and analyze the related mechanisms on gut microbiota in CT26 colon cancer-bearing mice in the present study. Firstly, four flavonoids (calycosin-7-glucoside, ononin, calycosin, formononetin) and three astragalosides (astragaloside A, astragaloside II, astragaloside I) riched in Astragalus mongholicus Bunge, three curcumins (bisdemethoxycurcumin, demethoxycurcumin, curcumin) and four essential oils (curdione, curzerene, germacrone and β-elemene) from Curcuma aromatica Salisb., in concentrations from 0.08 to 2.07 mg/g, were examined in ACE. Then the results in vivo studies indicated that ACE inhibited solid tumours, liver and spleen metastases of colon cancer while simultaneously reducing pathological tissue damage. Additionally, ACE regulated gut microbiota dysbiosis and the short chain fatty acid content in the gut, repaired intestinal barrier damage. ACE treatment suppressed the overgrowth of conditional pathogenic gut bacteria, including Escherichia-Shigella, Streptococcus and Enterococcus, while the probiotic gut microbiota like Lactobacillus, Roseburia, Prevotellaceae_UCG-001 and Mucispirillum were increased. More interestingly, the content level of SCFAs such as propionic acid and butyric acid was increased after ACE administration, which further mediates intestinal SDF-1/CXCR4 signalling pathway to repair the integrity of the intestinal barrier, decrease Cyclin D1 and C-myc expressions, eventually suppress the tumor the growth and metastasis of colon cancer. To sum up, the present study demonstrated that ACE could efficiently suppress colon cancer progression through gut microbiota modification, which may provide a new explanation of the mechanism of ACE against colon cancer.

PriME-PGx: La Princesa University Hospital Multidisciplinary Initiative for the Implementation of Pharmacogenetics

The implementation of clinical pharmacogenetics in daily practice is limited for various reasons. Today, however, it is a discipline in full expansion. Accordingly, in the recent times, several initiatives promoted its implementation, mainly in the United States but also in Europe. In this document, the genotyping results since the establishment of our Pharmacogenetics Unit in 2006 are described, as well as the historical implementation process that was carried out since then. Finally, this progress justified the constitution of La Princesa University Hospital Multidisciplinary Initiative for the Implementation of Pharmacogenetics (PriME-PGx), promoted by the Clinical Pharmacology Department of Hospital Universitario de La Princesa (Madrid, Spain). Here, we present the initiative along with the two first ongoing projects: the PROFILE project, which promotes modernization of pharmacogenetic reporting (i.e., from classic gene-drug pair reporting to complete pharmacogenetic reporting or the creation of pharmacogenetic profiles specific to the Hospital’s departments) and the GENOTRIAL project, which promotes the communication of relevant pharmacogenetic findings to any healthy volunteer participating in any bioequivalence clinical trial at the Clinical Trials Unit of Hospital Universitario de La Princesa (UECHUP).

Dose Correlation of Panax ginseng and Atractylodes macrocephala Koidz. Drug Pairs in the Chinese Medicine Prescription Based on the Copula Function

Objective. Panax ginseng and Atractylodes macrocephala Koidz. (AMK) are widely used in treating various diseases; however, research is insufficient on measuring the relationship that exists by combining this drug pair using the copula function. Methods. In this study, 279 traditional Chinese medicine prescriptions containing the Panax ginseng and AMK drug pair were extracted from the prescription database for three types of screened indications, namely, diabetes mellitus, diarrhea, and insomnia. Following the principle of dose conversion, each dynasty unit was uniformly converted into a modern unit. Then, the kernel density distribution of Panax ginseng and AMK was fitted with their empirical distribution functions. Finally, the optimal copula function was selected from the copula function family as a t-copula function. Results. The empirical distribution and probability density functions of Panax ginseng and AMK were obtained. From the results, their Kendall rank correlation coefficients with indications of diabetes mellitus, insomnia, and diarrhea were 0.8689, 0.7858, and 0.7403, whereas their Spearman rank correlation coefficients were 0.9563, 0.9276, and 0.8958. Results also indicated that the use of the t-copula function can better reflect the correlation between Panax ginseng and AMK doses. Conclusion. From the three indications, the dose between Panax ginseng and AMK was positively correlated. This study, therefore, confirms the medicinal principle of Chinese medicine “combining” from the perspective of mathematical statistics. Results from this study are practical to evaluate the correlation between the drug pair doses, Panax ginseng and AMK, using the copula function model, which provides a new model for the scientific explanation of compatibility for Chinese medicines. This study also provides a methodological basis for more drug measurement studies and clinical medications.

APRILE: Exploring the Molecular Mechanisms of Drug Side Effects with Explainable Graph Neural Networks

With the majority of people 65 and over taking two or more medicines (polypharmacy), managing the side effects associated with polypharmacy is a global challenge. Explainable Artificial Intelligence (XAI) is necessary to reliably design safe polypharmacy. Here, we develop APRILE: a predictor-explainer framework based on graph neural networks to explore the molecular mechanisms underlying polypharmacy side effects by explaining predictions made by the predictors. For a side effect and its associated drug pair, or a set of side effects and their drug pairs, APRILE gives a set of proteins (drug targets or non-targets) and Gene Ontology (GO) items as the explanation. Using APRILE, we generate such explanations for 843,318 (learned) + 93,966 (novel) side effect--drug pair events, spanning 861 side effects (472 diseases, 485 symptoms and 9 mental disorders) and 20 disease categories. We show that our two new metrics, pharmacogenomic information utilization and protein-protein interaction information utilization, provide quantitative estimates of mechanism complexity. Explanations were significantly consistent with state of the art disease-gene associations for 232/239 (97%) side effects. Further, APRILE generated new insights into molecular mechanisms of four diverse categories of ADRs: infection, metabolic diseases, gastrointestinal diseases, and mental disorders, including paradoxical side effects. We demonstrate the viability of discovering polypharmacy side effect mechanisms by learning from an AI model trained on massive biomedical data. Consequently, it facilitates wider and more reliable use of AI in healthcare.

A General Propensity Score for Signal Identification using Tree-Based Scan Statistics

Tree-based scan statistics (TreeScan) are a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS) matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes. We selected 4 drug pairs with well understood safety profiles. For each pair, we evaluated 5 candidate PSs with different combinations of: predefined general covariates (comorbidity, frailty, utilization), empirically-selected (data driven) covariates, and covariates tailored to the drug pair. For each pair, statistical alerting patterns were similar with alternative PSs (≤11 alerts in 7,996 outcomes scanned). Including covariates tailored to exposure did not appreciably impact screening results. Including empirically-selected covariates can provide better proxy coverage for confounders but can also decrease power. Unlike tailored covariates, empirical and predefined general covariates can be applied “out of the box” for signal identification. The choice of PS depends on level of concern about residual confounding versus loss of power. Potential signals should be followed by pharmacoepidemiologic assessment where confounding control is tailored to the specific outcome(s) under investigation.