Figure - available from: Evidence-based Complementary and Alternative Medicine
This content is subject to copyright. Terms and conditions apply.
Protein gyrate, MSD, and SASA result of four targets complex with candidates. Different colors lines mean different ligand-receptor interaction. (a) CK2A2 protein; (b) mtHSP70 protein; (c) STK3 protein; (d) LATS1 protein.
Source publication
Several pathways are crucial in Huntington’s disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions woul...
Citations
... [2][3] Previous studies have identified Brucea Javanica as a potential herbal treatment for Huntington's disease. [4] Brucea Javanica is a herbal medicine with a bitter taste and mild toxicity, it is the dried and ripe fruit of Brucea javanica (L.) Merr., a plant in the family Siberaceae. It has the effect of clearing away heat and detoxifying, and can also treat malaria, stop dysentery, and corrode warts.Accelrys Discovery Studio(DS) software was used to connect All Compounds from TCM Database@Taiwan to STK3 and CK2A2, and Bruceine F, which is one of the extracts of Brucea javanica, was screened by calculating the ligand-protein docking fraction. ...
... Bruceine F is believed to act as an ATP competition inhibitor at STK3 and CK2A2 sites. [4] 2. Hippo pathway ...
Huntington’s disease is an autosomal dominant disorder characterized by progressive central nervous system degeneration characterized by motor, cognitive, and psychiatric disorders. This disease occurs frequently and is not easy to cure. We set out to investigate the pathogenesis of The Hippo pathway mediated by Brucea javanica in Huntington’s disease (HD) by studying the critical role of Brucea javanica in the pathogenesis of Huntington’s disease (HD) to provide a basis for the study of targeted drugs for this disease. Methods: Western blotting was used to rapidly determine YAP/TAZ activity and the expression level of HSF1 in tissues. YAP/TAZ activity and the expression level of HSF1 in tissues will show us the correct transcription level in the nucleus to measure efficacy. Hypothesis: Bruceine F in Brucea Javanica could effectively act on STK3 and CK2A2 targets to increase nuclear YAP activity and HSF1 expression, thus effectively treating Huntington’s disease. Possible Results: 1: Bruceine F does not act effectively on SYK3 and CK2A2 sites. There were no significant changes in YAP activity in either the nucleus or cytoplasm, as well as in STK3 and CK2A2 for all samples,the expression of HSF1 in tissues also did not change significantly. 2: Bruceine F successfully inhibited STK3, but activated CK2A2, and YAP activity was decreased in the cytoplasm and elevated in the nucleus. , HSF1 expression levels were decreased. 3:Bruceine F activated STK3 but inhibited CK2A2, and YAP activity was elevated in the cytoplasm and decreased in the nucleus. , HSF1 expression levels were elevated. 4: Bruceine F activated STK3 and CK2A2, and YAP activity was elevated in the cytoplasm and decreased in the nucleus. , HSF1 expression levels were reduced. 5:Bruceine F inhibited STK3 and CK2A2, YAP activity was decreased in the cytoplasm and increased in the nucleus, and HSF1 expression levels were increased. 6:Bruceine F activated CK2A2 in knockout STK3 mice, and YAP activity was unchanged in the cytoplasmic nucleus with reduced HSF1 expression. 7: Bruceine F inhibited CK2A2 in knockout STK3 mice, YAP activity was unchanged in the cytoplasmic nucleus, and HSF1 expression levels were elevated. 8: Bruceine F inhibited STK3 in knockout CK2A2 mice, and YAP activity was reduced in the cytoplasm and increased in the nucleus, while HSF1 expression levels remained unchanged. 9: Bruceine F activated STK3 in knockout CK2A2 mice, and YAP activity was increased in the cytoplasm and decreased in the nucleus, while HSF1 expression levels remained unchanged.
... The SVM, with its kernels, is frequently employed in network pharmacology to identify effective compounds against complex diseases. Dai et al. [68] used network pharmacology-based approach for the identification of traditional Chinese medicinal formula against Huntington's disease. Later, they used SVM to validate their findings. ...
Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from ‘one-target one-drug’ to highly potent ‘multi-target drug’. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein–protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.
... Few studies bring computational techniques for the treatment of Huntington's disease, among the last years only one article addressed these techniques aimed at discovering multi-target compounds. Dai and collaborators (2018) [98] developed research addressing network pharmacology-based analysis, from which a database developed a network of protein-protein interactions that produced similar proteins. ...
Background
Neurological disorders are composed of several diseases that affect the central and peripheral nervous system; among these are neurodegenerative diseases, which lead to neuronal death. Many of these diseases have treatment for the disease and symptoms, leading patients to use several drugs that cause side effects.
Introduction
The search for new treatments has led to the investigation of multi-target drugs.
Method
This review aimed to investigate in the literature the multi-target effect in neurological disorders through an in silico approach. Studies were reviewed on the diseases such as epilepsy, Alzheimer's disease, Amyotrophic Lateral Sclerosis (ALS), Huntington's disease, cerebral ischemia, and Parkinson's disease.
Result
As a result, the study emphasize the relevance of research by computational techniques such as quantitative structure-activity relationship (QSAR) prediction models, pharmacokinetic prediction models, molecular docking, and molecular dynamics, besides presenting possible drug candidates with multi-target activity.
Conclusion
It was possible to identify several targets with pharmacological activities. Some of these targets had diseases in common such as carbonic anhydrase, acetylcholinesterase, NMDA, and MAO being relevant for possible multi-target approaches.
... Network pharmacology adopts a similar holistic approach in aparadigm shift from "one target, one drug" to "network target, multi-compound" therapeutics . Thus, network pharmacology is used widely to investigate the molecular mechanisms underlying the pharmacologic effects of TCM formulations (Li H. et al., 2014;Xiong et al., 2018;Piao et al., 2019); appropriate TCM prescriptions for the treatment of specific diseases (Li et al., 2007;Li X. et al., 2014;Shi et al., 2014;Ke et al., 2016;Fang et al., 2017aFang et al., , 2017bHu and Sun, 2017;Dai et al., 2018;Shi et al., 2019); bioactive components of medicines (Lv et al., 2014;Zhang Y.-F. et al., 2019;Ma et al., 2019;Song et al., 2019;Guo et al., 2020). ...
Licorice (Glycyrrhiza spp.) is used widely in traditional Chinese medicine (TCM) due to its numerous pharmacologic effects. However, the mechanisms of action of the chemical constituents of licorice and their structure–function relationships are not fully understood. To address these points, we analyzed the chemical compounds in licorice listed in the TCM Systems Pharmacology database and TCM Integrated database. Target proteins of the compounds were predicted using Integrative Pharmacology-based Research Platform of TCM v2.0. Information on the pharmacologic effects of licorice was obtained from the 2020 Chinese Pharmacopoeia, and disease-related genes that have been linked to these effects were identified from the Encyclopedia of TCM database. Pathway analyses using the Kyoto Encyclopedia of Genes and Genomes database were carried out for target proteins, and pharmacologic networks were constructed based on drug target–disease-related gene and protein–protein interactions. A total of 451 compounds were analyzed, of which 211 were from the medicinal parts of the licorice plant. The 241 putative targets of 106 bioactive compounds in licorice comprised 52 flavonoids, 47 triterpenoids, and seven coumarins. Four distinct pharmacologic effects of licorice were defined: 61 major hubs were the putative targets of 23 compounds in heat-clearing and detoxifying effects; 68 were targets of six compounds in spleen-invigorating and qi-replenishing effects; 28 were targets of six compounds in phlegm-expulsion and cough-suppressant effects; 25 compounds were targets of six compounds in spasm-relieving and analgesic effects. The major bioactive compounds of licorice were identified by ultra-high-performance liquid chromatography–quadrupole time-of-flight–tandem mass spectrometry. The anti-inflammatory properties of liquiritin apioside, liquiritigenin, glycyrrhizic acid and isoliquiritin apioside were demonstrated by enzyme-linked immunosorbent assay (ELISA) and Western blot analysis. Liquiritin apioside, liquiritigenin, isoliquiritin, isoliquiritin apioside, kaempferol, and kumatakenin were the main active flavonoids, and 18α- and 18β-glycyrrhetinic acid were the main active triterpenoids of licorice. The former were associated with heat-clearing and detoxifying effects, whereas the latter were implicated in the other three pharmacologic effects. Thus, the compounds in licorice have distinct pharmacologic effects according to their chemical structure. These results provide a reference for investigating the potential of licorice in treatment of various diseases.
... Additional databases, such as OMIM [47], NCBI (gene) [48], TCMSP [49], CAD Gene [50], GeneCards [51], MalaCards [52] and PubMed, were manually searched to replenish the omissive diseases-related targets of IPA. Information on QSYQ (Huangqi, Danshen, Sanqi and Jiangxiang) ingredients were obtained from several online TCM databases, including TCMSP [53], TCMID [54], TCM-ID [55], TCM Database@-Taiwan [56], TCMGeneDIT [57] and BATMAN-TCM [58]. PubChem [59] was also used to retrieve and verify ingredients of TCM. ...
Cerebral ischemia/reperfusion injury (CI/RI) is a common feature of ischemic stroke, involving a period of impaired blood supply to the brain, followed by the restoration of cerebral perfusion through medical intervention. Although ischemia and reperfusion brain damage is a complex pathological process with an unclear physiological mechanism, more attention is currently focused on the neuroinflammatory response of an ischemia/reperfusion origin, and anti-inflammatory appears to be a potential therapeutic strategy following ischemic stroke. QiShenYiQi (QSYQ), a component-based Chinese medicine with Qi-tonifying and blood-activating property, has pharmacological actions of anti-inflammatory, antioxidant, mitochondrial protectant, anti-apoptosis, and antiplatelet aggregation. We have previously reported that the cardioprotective effect of QSYQ against ischemia/reperfusion injury is via improvement of mitochondrial functional integrity. In this research work, we aimed to investigate the possible mechanism involved in the neuroprotection of QSYQ in mice model of cerebral ischemia/reperfusion injury based on the inflammatory pathway. The cerebral protection was evaluated in the stroke mice after 24 h reperfusion by assessing the neurological deficit, cerebral infarction, brain edema, BBB functionality, and via histopathological assessment. TCM-based network pharmacology method was performed to establish and analyze compound-target-disease & function-pathway network so as to find the possible mechanism linking to the role of QSYQ in CI/RI. In addition, RT-qPCR was used to verify the accuracy of predicted signaling gene expression. As a result, improvement of neurological outcome, reduction of infarct volume and brain edema, a decrease in BBB disruption, and amelioration of histopathological alteration were observed in mice pretreated with QSYQ after experimental stroke surgery. Network pharmacology analysis revealed neuroinflammatory response was associated with the action of QSYQ in CI/RI. RT-qPCR data showed that the mice pretreated with QSYQ could significantly decrease IFNG-γ, IL-6, TNF-α, NF-κB p65, and TLR-4 mRNA levels and increase TGF-β1 mRNA level in the brain compared to the untreated mice after CI/RI (p < 0.05). In conclusion, our study indicated the cerebral protective effect of pretreatment with QSYQ against CI/RI, which may be partly related to its potential to the reduction of neuroinflammatory response in a stroke subject.
... Each created data set may indicate the quantitative variation of between 5,000 and 10,000 experimental indices (transcripts, proteins, or metabolites), with perhaps 100s-1000s of these being statistically significant. In this context the ability of an individual scientist to appreciate the connectivity between these factors, which likely represents the true biomedical and pharmacological meaning of the data, is profoundly limited without the assistance of machine-based clustering and annotation Dai et al., 2018;Lee et al., 2018;Lin et al., 2018;Lim and Xie, 2019). While the intrinsic depth of such data streams is a tremendous analytical advance for the study of complex drug activities, a major hurdle for the clinical translation of such data are the pace of advanced data management and investigational platform development. ...
... In addition to the application of data deconvolution to identify key factors within complex networks, graphbased pipelines have been used to define drug-signaling pathway association analytics. For example, Dai et al. (2018) defined a computational process, integrative graph regularized matrix factorization, to enhance the drug-induced signaling cascade classification and prioritization. Integrative graph regularized matrix factorization employs graph regularization to encode data geometrical information and prevent possible overfitting in the prediction of the association of specific therapeutic agents with the strongest associated signaling paradigm. ...
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
... The pharmacodynamic mechanisms of TCM against liver fibrosis have multiple levels and multiple targets and pay attention to the characteristics of overall regulation [10]. A new approach that analyzes TCM with network pharmacology may be a reliable way to overcome disease [49]. Cumulating data have shown that network pharmacology can reveal the interactions between multiple targets of compounds present in Chinese herbal medicines [50]. ...
This study aims to predict the active ingredients, potential targets, signaling pathways and investigate the “ingredient-target-pathway” mechanisms involved in the pharmacological action of Danshiliuhao Granule (DSLHG) on liver fibrosis. Pharmacodynamics studies on rats with liver fibrosis showed that DSLHG generated an obvious anti-liver fibrosis action. On this basis, we explored the possible mechanisms underlying its antifibrosis effect using network pharmacology approach. Information about compounds of herbs in DSLHG was collected from TCMSP public database and literature. Furthermore, the oral bioavailability (OB) and drug-likeness (DL) were screened according to ADME features. Compounds with OB≥30% and DL≥0.18 were selected as active ingredients. Then, the potential targets of the active compounds were predicted by pharmacophore mapping approach and mapped with the target genes of the specific disease. The compound-target network and Protein-Protein Interaction (PPI) network were built by Cytoscape software. The core targets were selected by degree values. Furthermore, GO biological process analysis and KEGG pathway enrichment analysis were carried out to investigate the possible mechanisms involved in the anti-hepatic fibrosis effect of DSLHG. The predicted results showed that there were 108 main active components in the DSLHG formula. Moreover, there were 192 potential targets regulated by DSLHG, of which 86 were related to liver fibrosis, including AKT1, EGFR, and IGF1R. Mechanistically, the anti-liver fibrosis effect of DSLHG was exerted by interfering with 47 signaling pathways, such as PI3K-Akt, FoxO signaling pathway, and Ras signaling pathway. Network analysis showed that DSLHG could generate the antifibrosis action by affecting multiple targets and multiple pathways, which reflects the multicomponent, multitarget, and multichannel characteristics of traditional Chinese medicine and provides novel basis to clarify the mechanisms of anti-liver fibrosis of DSLHG.
Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington’s disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis.
Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.
