Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Elucidation of the key therapeutic targets and potential mechanisms of Andrographolide multi-targets against osteoarthritis via network pharmacological analysis and experimental validation.

OBJECTIVE: Our purpose is to unveil Andrographolide's potential multi-target and multi-mechanism therapeutic effects in treating OA via systematic network pharmacological analysis and cell experimental validation. MATERIALS AND METHODS: Initially, we gathered data from Andrographolide and OA-related databases to obtain information on Andrographolide's biological properties and the targets linked with OA. We developed a bioinformatic network about Andrographolide and OA, whereby we analyzed the network to identify potential therapeutic targets and mechanisms of action of Andrographolide. Subsequently, we used molecular docking to analyze the binding sites of Andrographolide to the target proteins. At the same time, SDF-1 was used to construct an OA cell model to verify the therapeutic effect of Andrographolide on OA and its effect on target proteins. RESULTS: Our experimental results show that Andrographolide has excellent pharmaceutical properties, by Lipinski's rules for drugs, suggesting that this compound can be considered to have a high therapeutic potential in drug development. 233 targets were preliminarily investigated, the mechanisms through which Andrographolide targets OA primarily involve the TNF signaling pathway, PI3K-AKT signaling pathway, IL-17 signaling pathway, and TLR signaling pathway. These mechanisms target OA by influencing immune and inflammatory responses in the joints, regulating apoptosis to prevent chondrocyte death. Finally, TNF-α, STAT3, TP53, IL-6, JUN, IL-1ß, HIF-1α, TGF-ß1, and AKT1 were identified as 9 key targets of Andrographolide anti-OA. In addition, our molecular docking analyzes with cell experimental validation further confirm the network pharmacology results. According to our molecular docking results, Andrographolide can bind to all the hub target proteins and has a good binding ability (binding energy < -5 kcal/mol), with the strongest binding affinity to AKT1 of -9.2 kcal/ mol. The results of cell experiments showed that Andrographolide treatment significantly increased the cell viability and the expression of COL2A1 and ACAN proteins. Moreover, 30 µM Andrographolide significantly reversed SDF-1-induced increases in the protein expression of TNF-α, STAT3, TP53, IL-6, JUN, IL-1ß, HIF-1α, and TGF-ß1, and decreases in the protein expression of AKT1. CONCLUSION: This study provides a comprehensive understanding of the potential therapeutic targets and mechanisms of action of Andrographolide in OA treatment. Our findings suggest that Andrographolide is a promising candidate for drug development in the management of OA.

Repurposing immune boosting and anti-viral efficacy of Parkia bioactive entities as multi-target directed therapeutic approach for SARS-CoV-2: exploration of lead drugs by drug likeness, molecular docking and molecular dynamics simulation methods.

The COVID-19 pandemic has caused adverse health (severe respiratory, enteric and systemic infections) and environmental impacts that have threatened public health and the economy worldwide. Drug repurposing and small molecule multi-target directed herbal medicine therapeutic approaches are the most appropriate exploration strategies for SARS-CoV-2 drug discovery. This study identified potential multi-target-directed Parkia bioactive entities against SARS-CoV-2 receptors (S-protein, ACE2, TMPRSS2, RBD/ACE2, RdRp, MPro, and PLPro) using ADMET, drug-likeness, molecular docking (AutoDock, FireDock and HDOCK), molecular dynamics simulation and MM-PBSA tools. One thousand Parkia bioactive entities were screened out by virtual screening and forty-five bioactive phytomolecules were selected based on favorable binding affinity and acceptable pharmacokinetic and pharmacodynamics properties. The binding affinity values of Parkia phyto-ligands (AutoDock: -6.00--10.40 kcal/mol; FireDock: -31.00--62.02 kcal/mol; and HDOCK: -150.0--294.93 kcal/mol) were observed to be higher than the reference antiviral drugs (AutoDock: -5.90--9.10 kcal/mol; FireDock: -35.64--59.35 kcal/mol; and HDOCK: -132.82--211.87 kcal/mol), suggesting a potent modulatory action of Parkia bioactive entities against the SARS-CoV-2. Didymin, rutin, epigallocatechin gallate, epicatechin-3-0-gallate, hyperin, ursolic acid, lupeol, stigmasta-5,24(28)-diene-3-ol, ellagic acid, apigenin, stigmasterol, and campesterol strongly bound with the multiple targets of the SARS-CoV-2 receptors, inhibiting viral entry, attachment, binding, replication, transcription, maturation, packaging and spread. Furthermore, ACE2, TMPRSS2, and MPro receptors possess significant molecular dynamic properties, including stability, compactness, flexibility and total binding energy. Residues GLU-589, and LEU-95 of ACE2, GLN-350, HIS-186, and ASP-257 of TMPRSS2, and GLU-14, MET-49, and GLN-189 of MPro receptors contributed to the formation of hydrogen bonds and binding interactions, playing vital roles in inhibiting the activity of the receptors. Promising results were achieved by developing multi-targeted antiviral Parkia bioactive entities as lead and prospective candidates under a small molecule strategy against SARS-CoV-2 pathogenesis. The antiviral activity of Parkia bioactive entities needs to be further validated by pre-clinical and clinical trials.

Exploring therapeutic targets and drug candidates for obesity: a combined network pharmacology, bioinformatics approach.

The remarkably high prevalence of obesity in Saudi Arabia reflects a global epidemic demanding urgent attention due to its associated health risks. The integration of traditional medicine, a vital cultural aspect, involves the use of medicinal plants to address various diseases, including obesity. This research merges network pharmacology (NP) and bioinformatics to innovate obesity treatment by identifying effective phytochemicals from native plants in the Taif valley. Focusing on six indigenous plants-Senna alexandrina, Capsicum annuum, Zingiber officinale, Curcuma longa, Trigonella foenum-graecum, and Foeniculum vulgare-we conducted preliminary screenings for potential bioactive compounds. We systematically compiled compound data from public databases and reviewed literature, revealing active compounds like apigenin, kaempferol, moupinamide, cyclocurcumin, chrysoeriol, isorhamnetin, rheinanthrone, cyclocurcumin, and riboflavin.Constructing a compound-target genes-obesity network unveiled their significant impact on metabolic regulation and fat accumulation, interacting notably with key proteins AKT1 and PTGS2. Molecular docking and 100 ns Molecular Dynamic (MD) simulations demonstrated robust binding affinity and stability at the docking site. Employing adipocytes as a cellular model, we gauged their viability and response to obesity-related stressors post-treatment with these native plant compounds.In conclusion, Saudi Arabia's indigenous plants hold promise as natural solutions for obesity treatment. This research opens new avenues in the battle against this pervasive health crisis by incorporating the potential of native botanicals.Communicated by Ramaswamy H. Sarma.

Multi-effective components and their target mechanism of Ziziphi Spinosae Semen in the treatment of insomnia.

Insomnia is a common and refractory disease. Since more than 2000 years ago, people have been using Ziziphi Spinosae Semen (ZSS). However, there are lack of molecular mechanisms of sleep promotion effects of ZSS. The purpose of this study is to clarify the active ingredients in ZSS that are used to treat insomnia. Using a method called cellular label-free integrative pharmacology (CLIP), we established five insomnia-related target models, including serotonin (5HT2A and 5HT1A), melatonin (MT1), dopamine (D2) and epinephrine (ß2) receptors. The one-dimensional (1D) fractions of ZSS extract were prepared on a RZC18 column and assayed on five models. Subsequently, the active fraction was further analyzed, fractionated and quantified using a two-dimensional (2D) liquid phase method coupled with a charged aerosol detector (CAD), This CAD-coupled 2D-LC method requires micro-fractions from the 1D separation and thus it greatly saves sample amounts and corresponding preparation time, and quickly conduct activity screening. The composition of the active 2D fractions was then determined using three-dimensional (3D) HPLC-MS, and molecular docking was separately carried out for the described compounds on the targets for activity prediction. Seven compounds were predicted to be active on 5HT2A, and two compounds on D2. We experimentally verified the prediction and found that vitexin exhibited D2 agonistic activity, and nuciferine exhibited 5HT2A antagonistic activity. This study revealed the effective components and their targets of ZSS in the treatment of insomnia, also highlighted the potential of the CLIP technique and bioactivity guided multi-dimensional HPLC-MS in molecular mechanism elucidation for traditional Chinese medicines.

Licochalcone A: A Potential Multitarget Drug for Alzheimer's Disease Treatment.

Licochalcone A (Lico-A) is a flavonoid compound derived from the root of the Glycyrrhiza species, a plant commonly used in traditional Chinese medicine. While the Glycyrrhiza species has shown promise in treating various diseases such as cancer, obesity, and skin diseases due to its active compounds, the investigation of Licochalcone A's effects on the central nervous system and its potential application in Alzheimer's disease (AD) treatment have garnered significant interest. Studies have reported the neuroprotective effects of Lico-A, suggesting its potential as a multitarget compound. Lico-A acts as a PTP1B inhibitor, enhancing cognitive activity through the BDNF-TrkB pathway and exhibiting inhibitory effects on microglia activation, which enables mitigation of neuroinflammation. Moreover, Lico-A inhibits c-Jun N-terminal kinase 1, a key enzyme involved in tau phosphorylation, and modulates the brain insulin receptor, which plays a role in cognitive processes. Lico-A also acts as an acetylcholinesterase inhibitor, leading to increased levels of the neurotransmitter acetylcholine (Ach) in the brain. This mechanism enhances cognitive capacity in individuals with AD. Finally, Lico-A has shown the ability to reduce amyloid plaques, a hallmark of AD, and exhibits antioxidant properties by activating the nuclear factor erythroid 2-related factor 2 (Nrf2), a key regulator of antioxidant defense mechanisms. In the present review, we discuss the available findings analyzing the potential of Lico-A as a neuroprotective agent. Continued research on Lico-A holds promise for the development of novel treatments for cognitive disorders and neurodegenerative diseases, including AD. Further investigations into its multitarget action and elucidation of underlying mechanisms will contribute to our understanding of its therapeutic potential.

Stearyl palmitate a multi-target inhibitor against breast cancer: in-silico, in-vitro & in-vivo approach.

Multi-target inhibitors are currently trending in the pharmaceutical research, as they possess increased efficacy and reduced toxicity. In this study multi-target inhibitors for breast cancer are explored from a curated list of natural products, i.e. 4,670 phytochemicals belonging to 360 medicinal plants. In-silico screening of phytochemicals using SeeSAR and AutoDock Vina resulted in identification of Stearyl Palmitate as a potential drug molecule that inhibits three drug targets, i.e. HER-2, MEK-1 and PARP-1 proteins. Molecular Dynamics Simulation for 100 ns each for these three protein-ligand complexes using Desmond, Maestro platform also confirmed the prediction of multi-target inhibition by Stearyl Palmitate. Further in-vitro MTT assay demonstrated that Stearyl Palmitate has a significant IC50 value of 40 µM against MCF-7 cells and >1000 µM against L929 cells. This confirmed that Stearyl Palmitate is having selective cytotoxicity towards breast cancer cells in comparison to non-cancerous cells. Fluorescence staining and flow cytometry analysis confirmed that, Stearyl Palmitate is inducing apoptosis in MCF-7 cells at IC50 concentration. Finally, in-vivo efficacy and toxicity studies were performed using zebrafishes (Danio rerio). It was observed that the fishes treated with IC50 concentration of Stearyl Palmitate demonstrated 2x folds reduction in tumour size, while double dose resulted in 4x folds reduction in tumour size. Stearyl Palmitate did not demonstrate any toxicity or side effects in the zebrafishes. It is concluded that, Stearyl Palmitate, a phytochemical reported to be present in Althea officinalis is a potential anti-breast cancer agent, with ability to inhibit multiple targets such as HER-2, MEK-1 and PARP-2 proteins.Communicated by Ramaswamy H. Sarma.

Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening.

The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of -10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88-161.89 nm2), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

A Novel Paradigm Defines Functional Molecule Clusters for an Anti-Alzheimer's Disease Recipe from Traditional Chinese Medicine.

Traditional Chinese Medicine (TCM) prescriptions are organically composed of compatible herbs according to the TCM theory. The complex ingredients of TCM could act on multiple targets through various pathways simultaneously to exert pharmacological effects, making TCM an unrivaled gem in the medical world. However, due to a lack of comprehensive and standard study methods, the research of TCM products has been quite limited. A novel paradigm that could aid in the discovery of the material basis and fully clarify the mechanism of TCM prescriptions is urgently needed. In this study, a similarity analysis based on molecular fingerprints was adopted to explore the representative molecules of the Tiaoxin recipe, a Chinese patent formula approved by the National Medical Products Administration (NMPA) for the treatment of mild-to-moderate Alzheimer's disease (AD), and 38 out of 1047 chemicals were finally screened out. Next, we tried to define a new concept of a "functional molecule cluster" for chemicals with similar pharmacological effects to elucidate how the chemical mixture from TCMs produce their therapeutic effects. Four anti-AD functional molecule clusters from the Tiaoxin recipe were identified: an anti-inflammatory cluster, an anti-ROS cluster, an anti-AChE activity cluster, and an anti-A[Formula: see text] aggregation cluster. Furthermore, the chemicals from the anti-inflammatory cluster and anti-ROS cluster were proved to display their multi-target and multi-pathway roles partially or mainly through molecules of the TLR4-MYD88-NF-[Formula: see text]B and Keap1-Nrf2-ARE pathways. The functional molecule clusters may be vital to the explanation of the efficacy of the Tiaoxin recipe, which could give us a more profound understanding of TCM prescriptions. Our paradigm may open a novel path for TCM research.

Multi-target bioactive compound screening from the infructescence of Platycarya strobilacea Sieb. et Zucc. by affinity chromatography using immobilized ß2 -adrenoceptor and muscarinic-3 acetylcholine receptor as the stationary phase.

As a main source for the recognition and identification of lead compounds, traditional Chinese medicine plays a pivotal role in preventing diseases for years. However, screening bioactive compounds from traditional Chinese medicine remains challenging because of the complexity of the systems and the occurrence of the synergic effect of the compounds. The infructescence of Platycarya strobilacea Sieb. et Zucc is prescribed for allergic rhinitis treatment with unknown bioactive compounds and unclear mechanisms. Herein, we immobilized the ß2 -adrenoceptor and muscarine-3 acetylcholine receptor onto the silica gel surface to prepare the stationary phase in a covalent bond through one step. The feasibility of the columns was investigated by the chromatographic method. Ellagic acid and catechin were identified as the bioactive compounds targeting the receptors. The binding constants of ellagic acid were calculated to be (1.56 ± 0.23)×107  M-1 for muscarine-3 acetylcholine receptor and (2.93 ± 0.15)×107  M-1 for ß2 -adrenoceptor by frontal analysis. While catechin can bind with muscarine-3 acetylcholine receptor with an affinity of (3.21 ± 0.05)×105  M-1 . Hydrogen bonds and van der Waals' force were the main driving forces for the two compounds with the receptors. The established method provides an alternative for multi-target bioactive compound screening in complex matrices.

Bioinformatics and network pharmacology-based study to elucidate the multi-target pharmacological mechanism of the indigenous plants of Medina valley in treating HCV-related hepatocellular carcinoma.

The incidence of Hepatocellular Carcinoma (HCC) in Saudi Arabia is not surprising given the relatively high prevalence of hepatitis C virus (HCV) infection. Hepatitis C is also common in Saudi Arabia with a prevalence rate of 1% to 3% of the population, which further increases the risk of HCC. The incidence of HCC has been increasing in recent years, with HCV-related HCC accounting for a significant proportion of cases. Traditional medicine has long been a part of Saudi Arabian culture, and many medicinal plants have been used for centuries to treat various ailments, including cancer. Following that, this study combines network pharmacology with bioinformatics approaches to potentially revolutionize HCV-related HCC treatment by identifying effective phytochemicals of indigenous plants of Medina valley. Eight indigenous plants including Rumex vesicarius, Withania somnifera, Rhazya stricta, Heliotropium arbainense, Asphodelus fistulosus, Pulicaria incise, Commicarpus grandiflorus, and Senna alexandrina, were selected for the initial screening of potential drug-like compounds. At first, the information related to active compounds of eight indigenous plants was retrieved from public databases and through literature review which was later combined with differentially expressed genes (DEGs) obtained through microarray datasets. Later, a compound-target genes-disease network was constructed which uncovered that kaempferol, rhazimol, beta-sitosterol, 12-Hydroxy-3-keto-bisnor-4-cholenic acid, 5-O-caffeoylquinic acid, 24-Methyldesmosterol, stigmasterone, fucosterol, and withanolide_J decisively contributed to the cell growth and proliferation by affecting ALB and PTGS2 proteins. Moreover, the molecular docking and Molecular Dynamic (MD) simulation of 20 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. But the findings were not validated in actual patients, so further investigation is needed to confirm the potential use of selected medicinal plants towards HCV-related HC.

Integrating network pharmacology approaches for the investigation of multi-target pharmacological mechanism of 6-shogaol against cervical cancer.

Traditional treatment of cancer has been plagued by a number of obstacles, such as multiple drug resistance, toxicity and financial constraints. In contrast, phytochemicals that modulate a variety of molecular mechanisms are garnering increasing interest in complementary and alternative medicine. Therefore, an approach based on network pharmacology was used in the present study to explore possible regulatory mechanisms of 6-shogaol as a potential treatment for cervical cancer (CC). A number of public databases were screened to collect information on the target genes of 6-shogaol (SuperPred, Targetnet, Swiss target prediction and PharmMapper), while targets pertaining to CC were taken from disease databases (DisGeNet and Genecards) and gene expression omnibus (GEO) provided expression datasets. With STRING and Cytoscape, protein-protein interactions (PPI) were generated and topology analysis along with CytoNCA were used to identify the Hub genes. The Gene Ontology (GO) database Enrichr was used to annotate the target proteins, while, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, signaling pathway enrichment analysis was conducted. Molecular docking and survival analysis for the Hub genes revealed four genes (HSP90AA1, HRAS, ESR1 and EGFR) with lowest binding energy and majority of the Hub genes (EGFR, SRC, CASP-3, HSP90AA1, MTOR, MAPK-1, MDM2 and ESR1) were linked with the overall survival of CC patients. In conclusion, the present study provides the scientific evidence which strongly supports the use of 6-shogoal as an inhibitor of cellular proliferation, growth, migration as well as inducer of apoptosis via targeting the hub genes involved in the growth of CC.Communicated by Ramaswamy H. Sarma.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research.

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Mechanistic Insights into the Neuroprotective Potential of Sacred Ficus Trees.

Ficus religiosa (Bo tree or sacred fig) and Ficus benghalensis (Indian banyan) are of immense spiritual and therapeutic importance. Various parts of these trees have been investigated for their antioxidant, antimicrobial, anticonvulsant, antidiabetic, anti-inflammatory, analgesic, hepatoprotective, dermoprotective, and nephroprotective properties. Previous reviews of Ficus mostly discussed traditional usages, photochemistry, and pharmacological activities, though comprehensive reviews of the neuroprotective potential of these Ficus species extracts and/or their important phytocompounds are lacking. The interesting phytocompounds from these trees include many bengalenosides, carotenoids, flavonoids (leucopelargonidin-3-O-ß-d-glucopyranoside, leucopelargonidin-3-O-α-l-rhamnopyranoside, lupeol, cetyl behenate, and α-amyrin acetate), flavonols (kaempferol, quercetin, myricetin), leucocyanidin, phytosterols (bergapten, bergaptol, lanosterol, ß-sitosterol, stigmasterol), terpenes (α-thujene, α-pinene, ß-pinene, α-terpinene, limonene, ß-ocimene, ß-bourbonene, ß-caryophyllene, α-trans-bergamotene, α-copaene, aromadendrene, α-humulene, alloaromadendrene, germacrene, γ-cadinene, and δ-cadinene), and diverse polyphenols (tannin, wax, saponin, leucoanthocyanin), contributing significantly to their pharmacological effects, ranging from antimicrobial action to neuroprotection. This review presents extensive mechanistic insights into the neuroprotective potential, especially important phytochemicals from F. religiosa and F. benghalensis. Owing to the complex pathophysiology of neurodegenerative disorders (NDDs), the currently existing drugs merely alleviate the symptoms. Hence, bioactive compounds with potent neuroprotective effects through a multitarget approach would be of great interest in developing pharmacophores for the treatment of NDDs.

Tandem mass tag-based proteomics analysis reveals the multitarget mechanisms of Phyllanthus emblica against liver fibrosis.

Phyllanthus emblica (PE), a traditional multiethnic herbal medicine, is commonly applied to treat liver diseases. Our previous study demonstrated that aqueous extract of PE (AEPE) could alleviate carbon tetrachloride (CCl4)-induced liver fibrosis in vivo, but the underlying molecular mechanisms are still unclear. The present study was undertaken to clarify the multitarget mechanisms of PE in treating liver fibrosis by proteomics clues. A CCl4-induced liver fibrosis rat model was established. The anti-liver fibrosis effects of chemical fractions from AEPE were evaluated by serum biochemical indicators and pathological staining. Additionally, tandem mass tag (TMT) - based quantitative proteomics technology was used to detect the hepatic differentially expressed proteins (DEPs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, gene ontology (GO) enrichment and protein-protein interaction (PPI) network were used to perform bioinformatics analysis of DEPs. Western blot analysis was used to verify the key potential targets regulated by the effective fraction of AEPE. The low-molecular-weight fraction of AEPE (LWPE) was determined to be the optimal anti-liver fibrosis active fraction, that could significantly improve ALT, AST, HA, Col IV, PCIII, LN, Hyp levels and reduce the pathological fibrotic lesion of liver tissue in model rats. A total of 195 DEPs were screened after LWPE intervention. GO analysis showed that the DEPs were related mostly to extracellular matrix organization, actin binding, and extracellular exosomes. KEGG pathway analysis showed that DEPs are mainly related to ECM-receptor interactions, focal adhesion and PI3K-Akt signaling pathway. Combined with the GO, KEGG and Western blot results, COL1A2, ITGAV, TLR2, ACE, and PDGFRB may be potential targets for PE treatment of liver fibrosis. In conclusion, LWPE exerts therapeutic effects through multiple pathways and multiple targets regulation in the treatment of liver fibrosis. This study may provide proteomics clues for the continuation of research on liver fibrosis treatment with PE.

Pharmacologic characterization of TBP1901, a prodrug form of aglycone curcumin, and CRISPR-Cas9 screen for therapeutic targets of aglycone curcumin.

Curcumin (aglycone curcumin) has antitumor properties in a variety of malignancies via the alteration of multiple cancer-related biological pathways; however, its clinical application has been hampered due to its poor bioavailability. To overcome this limitation, we have developed a synthesized curcumin ß-D-glucuronide sodium salt (TBP1901), a prodrug form of aglycone curcumin. In this study, we aimed to clarify the pharmacologic characteristics of TBP1901. In ß-glucuronidase (GUSB)-proficient mice, both curcumin ß-D-glucuronide and its active metabolite, aglycone curcumin, were detected in the blood after TBP1901 injection, whereas only curcumin ß-D-glucuronide was detected in GUSB-impaired mice, suggesting that GUSB plays a pivotal role in the conversion of TBP1901 into aglycone curcumin in vivo. TBP1901 itself had minimal antitumor effects in vitro, whereas it demonstrated significant antitumor effects in vivo. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screen disclosed the genes associated with NF-κB signaling pathway and mitochondria were among the highest hit. In vitro, aglycone curcumin inhibited NF-kappa B signaling pathways whereas it caused production of reactive oxygen species (ROS). ROS scavenger, N-acetyl-L-cysteine, partially reversed antitumor effects of aglycone curcumin. In summary, TBP1901 can exert antitumor effects as a prodrug of aglycone curcumin through GUSB-dependent activation.

[Mystery of Yiyin decoction theory: rule discovery and evaluation strategy of atypical pharmacological effects of Chinese medicinal prescription].

Yi Yin, a famous medical scientist and culinary master in the late Xia Dynasty and early Shang Dynasty, developed the Chinese medicinal liquids and Chinese medicinal prescriptions emerged after that. Chinese medicinal prescriptions have attracted much attention because of their unique advantages in the treatment of chronic multifactorial diseases, representing an important direction of drug discovery in the future. Yiyin decoction theory is the superior form of personalized combined medication with advanced consciousness. It is different from not only the magic bullet theory of single component action but also the connotation of modern multi-target drugs. The core of Yiyin decoction theory can be summarized as compound compatibility, multiple effects, and moderate regulation. Compound compatibility refers to that the formulation of Chinese medicinal prescriptions involves the complex synergy and interactions between sovereign, minister, assistant, and guide medicinal materials. Multiple effects mean that the prescriptions employ a variety of mechanisms to exert comprehensive pharmacological effects of nonlinear feedback. Moderate regulation reflects that the prescriptions can accurately regulate the multiple points of the disease biological network as a whole. To solve the mystery of Yiyin decoction theory, we should not only simply study the known active substances(components) and their independent target effects in the mixture, but also mine the "dark matter" and "dark effect" of Chinese medicinal prescriptions. That is, we should learn the neglected atypical pharmacological effects of Chinese medicinal prescriptions and the multi-point nesting mechanism that plays a precise regulatory function in the body. Yiyin decoction theory focuses on the overall pharmacological effect to reflect the comprehensive clinical value of Chinese medicinal prescriptions, which is of great significance for the development of a new model for the evaluation and application of new Chinese medicinal prescriptions in line with the theory of traditional Chinese medicine.

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

Roles of Syzygium in Anti-Cholinesterase, Anti-Diabetic, Anti-Inflammatory, and Antioxidant: From Alzheimer's Perspective.

Alzheimer's disease (AD) causes progressive memory loss and cognitive dysfunction. It is triggered by multifaceted burdens such as cholinergic toxicity, insulin resistance, neuroinflammation, and oxidative stress. Syzygium plants are ethnomedicinally used in treating inflammation, diabetes, as well as memory impairment. They are rich in antioxidant phenolic compounds, which can be multi-target neuroprotective agents against AD. This review attempts to review the pharmacological importance of the Syzygium genus in neuroprotection, focusing on anti-cholinesterase, anti-diabetic, anti-inflammatory, and antioxidant properties. Articles published in bibliographic databases within recent years relevant to neuroprotection were reviewed. About 10 species were examined for their anti-cholinesterase capacity. Most studies were conducted in the form of extracts rather than compounds. Syzygium aromaticum (particularly its essential oil and eugenol component) represents the most studied species owing to its economic significance in food and therapy. The molecular mechanisms of Syzygium species in neuroprotection include the inhibition of AChE to correct cholinergic transmission, suppression of pro-inflammatory mediators, oxidative stress markers, RIS production, enhancement of antioxidant enzymes, the restoration of brain ions homeostasis, the inhibition of microglial invasion, the modulation of ß-cell insulin release, the enhancement of lipid accumulation, glucose uptake, and adiponectin secretion via the activation of the insulin signaling pathway. Additional efforts are warranted to explore less studied species, including the Australian and Western Syzygium species. The effectiveness of the Syzygium genus in neuroprotective responses is markedly established, but further compound isolation, in silico, and clinical studies are demanded.

Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation.

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes, and multi-target drugs provide a promising therapy idea for the treatment of cancer. Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs. In this paper, 50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database, and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time. Through the multi-target anti-cancer prediction system, some dominant fragments that act on multiple tumor-related targets were analyzed, which could be helpful in designing multi-target anti-cancer drugs. Anti-cancer traditional Chinese medicine (TCM) and its natural products were collected to form a TCM formula-based natural products library, and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system. As a result, alkaloids, flavonoids and terpenoids were predicted to act on multiple tumor-related targets. The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments. In conclusion, the multi-target anti-cancer prediction system is very effective and reliable, and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs. The anti-cancer natural compounds found in this paper will lay important information for further study.