Integrating GEO, network pharmacology, and in vitro assays to explore the pharmacological mechanism of Bruceae Fructus against laryngeal cancer

The goal of this study is to look into the pharmacological mechanism of Bruceae Fructus in conjunction with GEO, network pharmacology, and in vitro assays for the treatment of laryngeal cancer to provide theoretical support for its therapeutic use. The active components and matching targets of Bruceae Fructus were retrieved from the TCMSP database, while genes linked with laryngeal cancer were obtained from the GEO, GeneCards, DisGeNET, and DrugBank databases. Besides, the components and targets were supplemented by literatures in PubMed database. Cytoscape software was used to create the active ingredients-target network diagram. The String database was used to build the PPI network. Following that, the core targets were subjected to GO enrichment and KEGG pathway analysis using the DAVID database. Finally, AutoDock was used to perform molecular docking between the core components and the core targets. To investigate the biological effects of beta-sitosterol, the viability of laryngeal cancer cells was assessed after beta-sitosterol therapy using the MTS technique. Following that, how beta-sitosterol affected colony formation after 14 days of culture of treated cells was researched. Flow cytometry was utilized to detect apoptosis to examine the influence of beta-sitosterol on laryngeal cancer cell apoptosis, and then detected mRNA and protein expression levels of 10 key genes by RT-qPCR and Western Blot assay. There were 1258 laryngeal cancer-related genes and 15 Bruceae Fructus components, with beta-sitosterol and luteolin serving as key components. Bruceae Fructus' primary targets against laryngeal cancer were IL6, JUN, TNF, IL2, IL4, IFNG, RELA, TP53, CDKN1A, and AKT1. GO enrichment yielded 41 CC, 78 MF, and 383 BP. Platinum drug resistance, the PI3K-Akt signaling pathway, the p53 signaling pathway, apoptosis, the HIF-1 signaling pathway, and 147 additional pathways have been added to KEGG. The results of molecular docking revealed that the core components had a high affinity for the core target. The results of the cell experiment indicate that beta-sitosterol suppressed Hep-2 cell activity in a concentration-dependent manner. Besides, beta-sitosterol has powerful antiproliferative properties in Hep-2 cells. Flow cytometry results showed that beta-sitosterol promoted laryngeal cancer cell apoptosis in a concentration-dependent manner. The results of RT-qPCR and Western Blot assay showed that the mRNA and protein expression levels of TP53, JUN, TNF-α, CDKN1A, and IL-2 were significantly up-regulated after beta-sitosterol treatment, while the mRNA and protein expression levels of RELA, AKT1, IL-6, IFNG, and IL-4 were significantly down-regulated. This study integrating GEO, network pharmacology, and in vitro assays investigated the probable mechanism of Bruceae Fructus' anti-laryngeal cancer activity, which can give a theoretical foundation for additional future animal experiments.

Identifying novel therapeutic inhibitors to target FMS-like tyrosine kinase-3 (FLT3) against acute myeloid leukemia: a molecular docking, molecular dynamics, and DFT study

Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to -11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended.Communicated by Ramaswamy H. Sarma.

Annonaceous acetogenins as promising DNA methylation inhibitors to prevent and treat leukemogenesis - an in silico approach

Leukemia is a haematological malignancy affecting blood and bone marrow, ranking 10th among the other common cancers. DNA methylation is an epigenetic dysregulation that plays a critical role in leukemogenesis. DNA methyltransferases (DNMTs) such as DNMT1, DNMT3A and DNMT3B are the key enzymes catalysing DNA methylation. Inhibition of DNMT1 with secondary metabolites from medicinal plants helps reverse DNA methylation. The present study focuses on inhibiting DNMT1 protein (PDB ID: 3PTA) with annonaceous acetogenins through in-silico studies. The docking and molecular dynamic (MD) simulation study was carried out using Schrödinger Maestro and Desmond, respectively. These compounds' drug likeliness, ADMET properties and bioactivity scores were analysed. About 76 different acetogenins were chosen for this study, among which 17 showed the highest binding energy in the range of -8.312 to -10.266 kcal/mol. The compounds with the highest negative binding energy were found to be annohexocin (-10.266 kcal/mol), isoannonacinone (-10.209 kcal/mol) and annonacin (-9.839 kcal/mol). MD simulation results reveal that annonacin remains stable throughout the simulation time of 100 ns and also binds to the catalytic domain of DNMT1 protein. From the above results, it can be concluded that annonacin has the potential to inhibit the DNA methylation process and prevent leukemogenesis.Communicated by Ramaswamy H. Sarma.

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.

Phytochemical Profiling, Biological Activities, and In Silico Molecular Docking Studies of Causonis trifolia (L.) Mabb. & J.Wen Shoot

Causonis trifolia (L.) Mabb. & J.Wen, commonly known as "fox grape", is an ethnomedicinally important twining herb of the Vitaceae family, and it is used by ethnic communities for its wide range of therapeutic properties. Our research aims to investigate the chemical composition; antioxidant, anti-inflammatory, and antidiabetic activities; and mechanisms of interaction between the identified selective chemical compounds and the target proteins associated with antioxidant, anti-inflammatory, and antidiabetic effects of the optimised phenolic extract of Causonis trifolia (L.) Mabb. & J.Wen, shoot (PECTS) to endorse the plant as a potential drug candidate for a future bioprospecting programme. Here, we employed the response surface methodology (RSM) with a Box-Behnken design to enrich the methanolic extract of C. trifolia shoot with phenolic ingredients by optimising three key parameters: solvent concentration (% v/v, methanol:water), extraction temperature (°C), and extraction duration (hours). From the quantitative phytochemical estimation, it was evident that the PECTS contained good amounts of phenolics, flavonoids, tannins, and alkaloids. During the HPLC analysis, we identified a total of eight phenolic and flavonoid compounds (gallic acid, catechin hydrate, chlorogenic acid, caffeic acid, p-coumaric acid, sinapic acid, coumarin, and kaempferol) and quantified their respective contents from the PECTS. The GC-MS analysis of the PECTS highlighted the presence of 19 phytochemicals. In addition, the bioactivity study of the PECTS showed remarkable potentiality as antioxidant, anti-inflammatory, and antidiabetic agents. In silico molecular docking and computational molecular modelling were employed to investigate the anti-inflammatory, antioxidant, and antidiabetic properties of the putative bioactive compounds derived from the PECTS using the GC-MS technique to understand the drug-receptor interactions, including their binding pattern. Out of the 19 phytocompounds identified by the GC-MS analysis, one compound, ergosta-5,22-dien-3-ol, acetate, (3β,22E), exhibited the best binding conformations with the target proteins involved in anti-inflammatory (e.g., Tnf-α and Cox-2), antioxidant (SOD), and antidiabetic (e.g., α-amylase and aldo reductase) activities. The nontoxic nature of this optimised extract was also evident during the in vitro cell toxicity assay against the Vero cell line and the in vivo acute toxicity study on BALB/c mice. We believe the results of the present study will pave the way for the invention of novel drugs efficacious for several ailments using the C. trifolia plant.

Plant-derived natural products for drug discovery: current approaches and prospects

Nature has abundant source of drugs that need to be identified/purified for use as essential biologics, either individually or in combination in the modern medical field. These drugs are divided into small bio-molecules, plant-made biologics, and a recently introduced third category known as phytopharmaceutical drugs. The development of phytopharmaceutical medicines is based on the ethnopharmacological approach, which relies on the traditional medicine system. The concept of ‘one-disease one-target drug’ is becoming less popular, and the use of plant extracts, fractions, and molecules is the new paradigm that holds promising scope to formulate appropriate drugs. This led to discovering a new concept known as polypharmacology, where natural products from varying sources can engage with multiple human physiology targets. This article summarizes different approaches for phytopharmaceutical drug development and discusses the progress in systems biology and computational tools for identifying drug targets. We review the existing drug delivery methods to facilitate the efficient delivery of drugs to the targets. In addition, we describe different analytical techniques for the authentication and fingerprinting of plant materials. Finally, we highlight the role of biopharming in developing plant-based biologics.

Polypharmacology-based approach for screening TCM against coinfection of Mycoplasma gallisepticum and Escherichia coli

Natural products and their unique polypharmacology offer significant advantages for finding novel therapeutics particularly for the treatment of complex diseases. Meanwhile, Traditional Chinese Medicine exerts overall clinical benefits through a multi-component and multi-target approach. In this study, we used the previously established co-infection model of Mycoplasma gallisepticum and Escherichia coli as a representative of complex diseases. A new combination consisting of 6 herbs were obtained by using network pharmacology combined with transcriptomic analysis to reverse screen TCMs from the Chinese medicine database, containing Isatdis Radix, Forsythia Fructus, Ginkgo Folium, Mori Cortex, Licorice, and Radix Salviae. The results of therapeutic trials showed that the Chinese herbal compounds screened by the target network played a good therapeutic effect in the case of co-infection. In summary, these data suggested a new method to validate target combinations of natural products that can be used to optimize their multiple structure-activity relationships to obtain drug-like natural product derivatives.

Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity

The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.

Structural-Guided Identification of Small Molecule Inhibitor of UHRF1 Methyltransferase Activity

Ubiquitin-like containing plant homeodomain Ring Finger 1 (UHRF1) protein is recognized as a cell-cycle-regulated multidomain protein. UHRF1 importantly manifests the maintenance of DNA methylation mediated by the interaction between its SRA (SET and RING associated) domain and DNA methyltransferase-1 (DNMT1)-like epigenetic modulators. However, overexpression of UHRF1 epigenetically responds to the aberrant global methylation and promotes tumorigenesis. To date, no potential molecular inhibitor has been studied against the SRA domain. Therefore, this study focused on identifying the active natural drug-like candidates against the SRA domain. A comprehensive set of in silico approaches including molecular docking, molecular dynamics (MD) simulation, and toxicity analysis was performed to identify potential candidates. A dataset of 709 natural compounds was screened through molecular docking where chicoric acid and nystose have been found showing higher binding affinities to the SRA domain. The MD simulations also showed the protein ligand interaction stability of and in silico toxicity analysis has also showed chicoric acid as a safe and nontoxic drug. In addition, chicoric acid possessed a longer interaction time and higher LD50 of 5000 mg/kg. Moreover, the global methylation level (%5 mC) has been assessed after chicoric acid treatment was in the colorectal cancer cell line (HCT116) at different doses. The result showed that 7.5 µM chicoric acid treatment reduced methylation levels significantly. Thus, the study found chicoric acid can become a possible epidrug-like inhibitor against the SRA domain of UHRF1 protein.

Therapeutic Effects of Retinoic Acid in Lipopolysaccharide-Induced Cardiac Dysfunction: Network Pharmacology and Experimental Validation

Purpose

Sepsis, which is deemed as a systemic inflammation reaction syndrome in the face of infectious stimuli, is the primary cause of death in ICUs. Sepsis-induced cardiomyopathy (SIC) may derive from systemic inflammation reaction and oxidative stress. Retinoic acid (RA) is recognized by its beneficial roles in terms of the immunoresponse to infections and antioxygen actions. However, the treatment efficacy and potential causal links of RA in SIC are still elusive.

Methods

By virtue of the STITCH database, we identified the targets of RA. Differentially expressed genes in SIC were acquired from the GEO database. The PPI network of intersected targets was established. GO and KEGG pathway enrichment analysis was completed. Hub genes were analyzed by cytoHubba plug-in. In the process of experimental validation, a mouse sepsis model was established by lipopolysaccharide (LPS), and the treated mice were intraperitoneally injected with RA or Dexamethasone (DEX) 60 min prior to LPS injections. Survival conditions, cardiac functions and antioxidant levels of the mice were assessed. Cardiac inflammation and injury were detected by HE and TUNEL. The levels of key genes and signal pathway expression were analyzed by RT-PCR and Western blot.

Results

PPARA, ITGAM, VCAM-1, IGF-1 and IL-6 were identified as key therapeutic targets of RA by network pharmacology. PI3K-Akt signaling pathway is the main regulatory pathway of RA. In vivo researches unraveled that RA can improve the survival rate and cardiac function of LPS-treated mice, inhibit inflammatory factors and myocardial injury, and regulate the expression of key therapeutic targets and key pathways, which is PI3K-Akt signaling pathway.

Conclusion

Network pharmacological method offers a predicative strategy to explore the treatment efficacy and causal links of RA in endotoxemic myocarditis. Through experimental verification, we discover that RA can reduce lipopolysaccharide-induced cardiac dysfunction by regulating the PI3K-Akt signaling pathway and key genes.

Clinical Knowledge Mining Based on Image Enhancement Algorithm: Endoscopic Clinical Analysis of Peptic Ulcer in Children

The incidence rate of peptic ulcer is increasing gradually. Medical images can meet the needs of patients as an auxiliary diagnosis and treatment method for peptic ulcer. However, in the long-term treatment, the actual effect is average, and the diagnosis effect of gastrointestinal diseases will gradually deteriorate. In this paper, we use an image enhancement algorithm to study the mechanism of peptic ulcer from the perspective of a medical image. In this paper, 56 images of children with peptic ulcer were selected, and the gastroscopy based on the image enhancement algorithm provided technical support for the rapid diagnosis of patients with peptic ulcer. Experimental results show that the clinical features of peptic ulcer have different characteristics according to the age difference of patients, which can play a positive role in promoting the treatment of patients of different ages.

Qingfei Litan Decoction Against Acute Lung Injury/Acute Respiratory Distress Syndrome: The Potential Roles of Anti-Inflammatory and Anti-Oxidative Effects

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is an acute respiratory failure syndrome characterized by progressive arterial hypoxemia and dyspnea. Qingfei Litan (QFLT) decoction, as a classic prescription for the treatment of acute respiratory infections, is effective for the treatment of ALI/ARDS. In this study, the compounds, hub targets, and major pathways of QFLT in ALI/ARDS treatment were analyzed using Ultra high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) and systemic pharmacology strategies. UHPLC-MS identified 47 main components of QFLT. To explore its anti-inflammatory and anti-oxidative mechanisms, gene ontology (Go) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and network pharmacological analysis were conducted based on the main 47 components. KEGG enrichment analysis showed that TNF signaling pathway and Toll-like receptor signaling pathway may be the key pathways of ALI/ARDS. We explored the anti-inflammatory and anti-oxidative pharmacological effects of QFLT in treatment of ALI/ARDS in vivo and in vitro. QFLT suppressed the levels of proinflammatory cytokines and alleviated oxidative stress in LPS-challenged mice. In vitro, QFLT decreased the levels of TNF-α, IL-6, IL-1β secreted by LPS-activated macrophages, increased GSH level and decreased the LPS-activated reactive oxygen species (ROS) in lung epithelial A549 cells. This study suggested that QFLT may have anti-inflammatory and anti-oxidative effects on ALI/ARDS, combining in vivo and in vitro experiments with systemic pharmacology, providing a potential therapeutic strategy option.

Investigation of the Potential Key Genes and the Multitarget Mechanisms of Polygonum cuspidatum against Heart Failure Based on Network Pharmacology and Experimental Validation

In this study, systematic pharmacology and bioinformatic approaches were employed to identify the potential targets of Polygonum cuspidatum (PC) for treating heart failure (HF). The active ingredients of PC were screened by using the TCMSP database, and HF-related genes were identified in the GEO database. Then, the herb-HF targeted-gene networks were constructed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were performed to obtain the enriched molecular pathways associated with the pathogenesis of HF. Finally, in vitro experiment was performed to evidence network pharmacology analysis. 170 intersection genes were obtained, and key genes (FOXO3, NFKB1, and TNF) were identified. Besides, GO and KEGG findings indicated that PC treatment of HF was achieved via regulating apoptosis, IL-17 signaling pathway, TNF signaling pathway, response to oxidative stress, and response to reactive oxygen species. And cell experiment revealed that PC could decrease the expression of NFKB1 and TNF and increase the expression of FOXO3, SOD1, and GPX1 in H9C2 cells. These findings showed that the therapeutic mechanism of PC in the treatment of HF may be associated with the regulation of inflammation-related and oxidative stress-related genes.

Integrated Whole-Transcriptome Profiling and Bioinformatics Analysis of the Polypharmacological Effects of Ganoderic Acid Me in Colorectal Cancer Treatment

Ganoderic acid Me (GA-Me) is a natural bioactive compound derived from Ganoderma lucidum. Our present results suggested that GA-Me inhibited proliferation, induced DNA fragmentation and significantly activated caspase-9 and caspase-3 in HCT116 cells. As shown in our previous studies, GA-Me targets several genes to prevent cancer, including colorectal cancer (CRC). Thus, we hypothesized that GA-Me might be a multitarget ligand against cancer. However, its exact mechanism in CRC remains unclear. Here, whole-transcriptome sequencing was employed to assess the long noncoding RNA (lncRNA), circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) profiles of GA-Me-treated HCT116 cells. In total, 1572 differentially expressed (DE) lncRNAs, 123 DEcircRNAs, 87 DEmiRNAs, and 1508 DEmRNAs were identified. DCBLD2 and RAPGEF5 were validated as two core mRNAs in the DElncRNA, DEcircRNA, and DEmiRNA networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the biological functions and potential mechanisms of TCONS-00008997, XR-925056.2, circRNA-07908, hsa-miR-100-3p, hsa-miR-1257, hsa-miR-3182, NAV3, ADAM20, and STARD4, which were altered after GA-Me treatment. The regulatory relationships of the XR-925056.2-hsa-miR-3182-NAV3/ADAM20/STARD4, circRNA-07908|Chr22:38986298-39025349-hsa-miR-3182-NAV3/ADAM20, ENST00000414039/ENST00000419190-novel874_mature-MMP9 and circRNA-00314|Chr1:35470863-35479212/circRNA-05460|Chr17:72592203-72649268-novel874_mature-MMP9 immune-regulatory networks involved both noncoding RNAs (ncRNAs) and mRNAs. Molecular docking studies showed that Zn²⁺ and the His201, His205, His211, Glu202, and Ala165 residues of MMP2 contributed to its high affinity for GA-Me. Zn²⁺ and the Glu402 and Gly186 residues of MMP9 are important for its interaction with GA-Me. Our results suggested and confirmed that GA-Me is a potential multitarget lead compound for CRC treatment with unique polypharmacological advantages.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Differentiating the Neuropharmacological Properties of Nicotinic Acetylcholine Receptor-Activating Alkaloids

Alkaloids that target nicotinic acetylcholine receptors (nAChR) are of great interest because of the critical role they play in mood and anxiety. However, understanding of the neuropharmacological effects of nicotinic alkaloids, such as cotinine and anatabine, is very limited. In this study, we investigated the neuropharmacological effects of three naturally occurring alkaloids-nicotine, cotinine, and anatabine-in vitro and in vivo. A single injection of nicotine induced anxiolytic-like behavioral features in mice by using the SmartCube^® behavioral profiling system, while cotinine and anatabine had no detectable effect. The results were corroborated by using the zebrafish novel tank test (NTT), which showed a profound anxiolytic-like effect induced by multiple doses of nicotine after a single 20-min treatment. When the regulation of dopamine and norepinephrine release-the neurotransmitter systems relevant for anxiety-were examined in vitro, we found that nicotine stimulated the release of both norepinephrine and dopamine, while cotinine and anatabine mainly stimulated the dopamine release. The molecular targets of nicotine were confirmed to be nAChRs with its most potent activities against α4β2 and α6/3β2β3 subtypes in vitro. Anatabine was a weaker agonist for these receptors than nicotine. Cotinine was the least potent nAChR compound, only being able to activate α4β2 and α6/3β2β3 subtypes at high doses and no detectable activities against α3β4 and α7 subtypes at the concentrations tested. The observed effects were unlikely due to the off-target effect, because these alkaloids did not bind or regulate >160 other molecular targets in vitro. Thus, the present results suggest that natural nicotinic alkaloids can induce an anxiolytic-like behavior in nonclinical animal models, potency of which may depend on the activation of various nAChRs and regulation of various neurotransmitter systems. Further investigations would help understand their effects on humans, because non-clinical studies should not be taken as a direct indication for human behavior and nicotine is not risk free.

In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula

An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug.

Evolving Roles of Natural Terpenoids From Traditional Chinese Medicine in the Treatment of Osteoporosis

Osteoporosis (OP) is a systemic metabolic skeletal disease which can lead to reduction in bone mass and increased risk of bone fracture due to the microstructural degradation. Traditional Chinese medicine (TCM) has been applied in the prevention and treatment of osteoporosis for a long time. Terpenoids, a class of natural products that are rich in TCM, have been widely studied for their therapeutic efficacy on bone resorption, osteogenesis, and concomitant inflammation. Terpenoids can be classified in four categories by structures, monoterpenoids, sesquiterpenoids, diterpenoids, and triterpenoids. In this review, we comprehensively summarize all the currently known TCM-derived terpenoids in the treatment of OP. In addition, we discuss the possible mechanistic-of-actions of all four category terpenoids in anti-OP and assess their therapeutic potential for OP treatment.

Network pharmacology reveals the potential mechanism of Baiying Qinghou decoction in treating laryngeal squamous cell carcinoma

Context: Baiying Qinghou as a traditional Chinese medicine decoction shows anticancer property on laryngeal squamous cell carcinoma. However, little is known about the precise mechanism of Baiying Qinghou detection against laryngeal squamous cell carcinoma.

Objective: This study was aimed to explore potential mechanism of therapeutic actions of Baiying Qinghou decoction on laryngeal squamous cell carcinoma.

Materials and Methods: The active chemical components of Baiying Qinghou decoction were predicted, followed by integrated analysis of network pharmacology and molecular docking approach. The network pharmacology approach included target protein prediction, protein-protein interaction network construction and functional enrichment analysis.

Results: Sitosterol and quercetin were predicted to be the overlapped active ingredients among three Chinese herbs of Baiying Qinghou decoction. The target proteins were closely associated with response to chemical, response to drug related biological process and cancer related pathways such as PI3K-Akt signaling, HIF-1 signaling and Estrogen signaling pathway. The target proteins of TP53, EGFR, PTGS2, NOS3 and IL1B as the key nodes in PPI network were cross-validated, among which EGFR, IL1B, NOS3 and TP53 were significantly correlated with the prognosis of patients with laryngeal squamous cell carcinoma. Finally, the binding modes of EGFR, IL1B, NOS3 and TP53 with quercetin were visualized.

Discussion and Conclusion: Quercetin of Baiying Qinghou decoction showed therapeutic effect against laryngeal squamous cell carcinoma by regulating TP53, EGFR, NOS3 and IL1B involved with drug resistance and PI3K-AKT signaling pathway. TP53, EGFR, NOS3 and IL1B may be the candidate targets for the treatment of laryngeal squamous cell carcinoma.

Plant Derived Bioactive Compounds, Their Anti-Cancer Effects and In Silico Approaches as an Alternative Target Treatment Strategy for Breast Cancer: An Updated Overview

Cancer is one of the most common malignant diseases that occur worldwide, among which breast cancer is the second leading cause of death in women. The subtypes are associated with differences in the outcome and were selected for treatments according to the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor. Triple-negative breast cancer, one of the subtypes of breast cancer, is difficult to treat and can even lead to death. If breast cancer is not treated during the initial stages, it may spread to nearby organs, a process called metastasis, through the blood or lymph system. For in vitro studies, MCF-7, MDA-MB-231, MDA-MB-468, and T47B are the most commonly used breast cancer cell lines. Clinically, chemotherapy and radiotherapy are usually expensive and can also cause side effects. To overcome these issues, medicinal plants could be the best alternative for chemotherapeutic drugs with fewer side effects and cost-effectiveness. Furthermore, the genes involved in breast cancer can be regulated and synergized with signaling molecules to suppress the proliferation of breast cancer cells. In addition, nanoparticles encapsulating (nano-encapsulation) medicinal plant extracts showed a significant reduction in the apoptotic and cytotoxic activities of breast cancer cells. This present review mainly speculates an overview of the native medicinal plant derived anti-cancerous compounds with its efficiency, types and pathways involved in breast cancer along with its genes, the mechanism of breast cancer brain metastasis, chemoresistivity and its mechanism, bioinformatics approaches which could be an effective alternative for drug discovery.

Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome

Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.

Antiviral activity of diallyl trisulfide against H9N2 avian influenza virus infection in vitro and in vivo

Background

Diallyl trisulfide (DATS) is a garlic-derived organosulfur compound. As it has been shown to have anti-viral activity, we hypothesized that it may alleviate infections caused by H9N2 avian influenza virus (AIV), which is prevalent in poultry with pandemic potential.

Methods

Human lung A549 epithelial cells were treated with three different concentrations of DATS 24 h before (pre-treatment) or one hour after (post-treatment) H9N2 AIV infection. Culture supernatants were collected 24 h and 48 h post-infection and analyzed for viral titers and levels of inflammatory and anti-viral immune responses. For in vivo experiments, BABL/c mice were administered daily by intraperitoneal injection with DATS (30 mg/kg) for 2 weeks starting 1 day after H9N2 AIV infection. Clinical signs, lung pathology, and inflammatory and anti-viral immune responses were assessed 2, 4, and 6 days after infection.

Results

Both pre-treatment and post-treatment of A549 cells with DATS resulted in reduced viral loads, increased expression of anti-viral genes (RIG-I, IRF-3, and interferon-β), and decreased expression of inflammatory cytokines (TNF-α and IL-6). These effects were also observed in H9N2 AIV-infected mice treated with DATS. Such treatment also reduced lung edema and inflammation in mice.

Conclusions

Results suggest that DATS has anti-viral activity against H9N2 AIV and may be used as an alternative treatment for influenza virus infection.

How can artificial intelligence be used for peptidomics?

INTRODUCTION

Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases.

AREAS COVERED

The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text].

EXPERT OPINION

Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).

Effects of nicotinic acetylcholine receptor-activating alkaloids on anxiety-like behavior in zebrafish

Alkaloids are a structurally complex group of natural products that have a diverse range of biological activities and significant therapeutic applications. In this study, we examined the acute, anxiolytic-like effects of nicotinic acetylcholine receptor (nAChR)-activating alkaloids with reported neuropharmacological effects but whose effects on anxiety are less well understood. Because α4β2 nAChRs can regulate anxiety, we first demonstrated the functional activities of alkaloids on these receptors in vitro. Their effects on anxiety-like behavior in zebrafish were then examined using the zebrafish novel tank test (NTT). The NTT is a relatively high-throughput behavioral paradigm that takes advantage of the natural tendency of fish to dive down when stressed or anxious. We report for the first time that cotinine, anatabine, and methylanatabine may suppress this anxiety-driven zebrafish behavior after a single 20-min treatment. Effective concentrations of these alkaloids were well above the concentrations naturally found in plants and the concentrations needed to induce anxiolytic-like effect by nicotine. These alkaloids showed good receptor interactions at the α4β2 nAChR agonist site as demonstrated by in vitro binding and in silico docking model, although somewhat weaker than that for nicotine. Minimal or no significant effect of other compounds may have been due to low bioavailability of these compounds in the brain, which is supported by the in silico prediction of blood–brain barrier permeability. Taken together, our findings indicate that nicotine, although not risk-free, is the most potent anxiolytic-like alkaloid tested in this study, and other natural alkaloids may regulate anxiety as well.

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA)

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood–brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure–activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel “desirability scoring function” was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

Exploring the Oxidative Stress Mechanism of Buyang Huanwu Decoction in Intervention of Vascular Dementia Based on Systems Biology Strategy

OBJECTIVE

To explore the oxidative stress mechanism of modified Buyang Huanwu decoction (MBHD) in intervention of vascular dementia (VD) based on systems biology strategy.

METHODS

In this study, through the reverse virtual target prediction technology and transcriptomics integration strategy, the active ingredients and potential targets of MBHD treatment of VD were analyzed, and the drug-disease protein-protein interaction (PPI) network was constructed. Then, bioinformatics analysis methods are used for Gene Ontology (GO) enrichment analysis and pathway enrichment analysis, and finally find the core biological process. After that, in animal models, low-throughput technology is used to detect gene expression and protein expression of key molecular targets in oxidative stress-mediated inflammation and apoptosis signaling pathways to verify the mechanism of MBHD treatment of VD rats. Finally, the potential interaction relationship between MBHD and VD-related molecules is further explored through molecular docking technology.

RESULTS

There are a total of 54 MBHD components, 252 potential targets, and 360 VD genes. The results of GO enrichment analysis and pathway enrichment analysis showed that MBHD may regulate neuronal apoptosis, nitric oxide synthesis and metabolism, platelet activation, NF-κB signaling pathway-mediated inflammation, oxidative stress, angiogenesis, etc. Among them, SIRT1, NF-κB, BAX, BCL-2, CASP3, and APP may be important targets for MBHD to treat VD. Low-throughput technology (qRT-PCR/WB/immunohistochemical technology) detects oxidative stress-mediated inflammation and apoptosis-related signaling pathway molecules. The molecular docking results showed that 64474-51-7, cycloartenol, ferulic acid, formononetin, kaempferol, liquiritigenin, senkyunone, wallichilide, xanthinin, and other molecules can directly interact with NF-κB p65, BAX, BCL-2, and CASP3.

CONCLUSION

The active compounds of MBHD interact with multiple targets and multiple pathways in a synergistic manner, and have important therapeutic effects on VD mainly by balancing oxidative stress/anti-inflammatory and antiapoptotic, enhancing metabolism, and enhancing the immune system.

In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy

Advances in immunotherapy have revolutionized treatments in many types of cancer. Traditional Chinese Medicine (TCM), which has a long history of clinical adjuvant application against cancer, is emerging as an important medical resource for developing innovative cancer treatments, including immunotherapy. In this study, we developed a quantitative and systems pharmacology-based framework to identify TCM-derived natural products for cancer immunotherapy. Specifically, we integrated 381 cancer immune response-related genes and a compound-target interaction network connecting 3273 proteins and 766 natural products from 66 cancer-related herbs based on literature-mining. Via systems pharmacology-based prediction, we uncovered 182 TCM-derived natural products having potential anti-tumor immune responses effect. Importantly, 32 of the 49 most promising natural products (success rate = 65.31%) are validated by multiple evidence, including published experimental data from clinical studies, in vitro and in vivo assays. We further identified the mechanism-of-action of TCM in cancer immunotherapy using network-based functional enrichment analysis. We showcased that three typical natural products (baicalin, wogonin, and oroxylin A) in Huangqin (Scutellaria baicalensis Georgi) potentially overcome resistance of known oncology agents by regulating tumor immunosuppressive microenvironments. In summary, this study offers a novel and effective systems pharmacology infrastructure for potential cancer immunotherapeutic development by exploiting the medical wealth of natural products in TCM.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1β, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

To Explore the Mechanism and Equivalent Molecular Group of Fuxin Mixture in Treating Heart Failure Based on Network Pharmacology

Fuxin mixture (FXHJ) is a prescription for the treatment of heart failure. It has been shown to be effective in clinical trials, but its active ingredients and mechanism of action are not completely clear, which limits its clinical application and international promotion. In this study, we used network pharmacology to find, conclude, and summarize the mechanism of FXHJ in the treatment of heart failure. From FXHJ, we found 39 active ingredients and 47 action targets. Next, we constructed the action network and was conducted enrichment analysis. The results showed that FXHJ mainly treated heart failure by regulating the MAPK signaling pathway, PI3KAkt signaling pathway, cAMP signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, VEGF signaling pathway, NF-kappa B signaling pathway, and the apoptotic signaling molecule BCL2. Through the research method of network pharmacology, this study summarized the preliminary experiments of the research group and revealed the probable mechanism of FXHJ in the treatment of heart failure to a certain extent, which provided some ideas for the development of new drugs.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Veettil V, Noumi E, A. Al-Motair K, Aouadi K, Kadri A, De Feo V, Snoussi M. Allium subhirsutum L. as a Potential Source of Antioxidant and Anticancer Bioactive Molecules: HR-LCMS Phytochemical Profiling, In Vitro and In Vivo Pharmacological Study

This study investigated Allium subhirsutum L. (AS) anticancer and antioxidant effects and inhibition of tumor angiogenesis in a murine model of skeletal metastases due to inoculation of Walker 256/B cells. Phytochemical composition of AS extract (ASE) was studied by High Resolution-Liquid Chromatography Mass Spectroscopy (HR-LCMS). Total phenolic and flavonoid contents (TPC and TFC) were determined. In vitro, the antioxidant properties were evaluated by reducing power and antiradical activity against DPPH. Cancer cells' proliferation, apoptosis, metastatic development and angiogenesis were evaluated using Walker 256/B and MatLyLu cells. The p-coumaric acid was the major phenolic acid (1700 µg/g extract). ASE showed high levels of TPC and TFC and proved potent antioxidant effects. ASE inhibited Walker 256/B and MatLyLu cells' proliferation (Half-maximal inhibitory concentration: IC50 ≃ 150 µg/mL) and induced apoptosis. In silico and in vivo assays confirmed these findings. ASE effectively acts as a chemo-preventive compound, induces apoptosis and attenuates angiogenesis and osteolytic metastases due to Walker 256/B malignant cells.

GC-MS analysis of phytoconstituents from Amomum nilgiricum and molecular docking interactions of bioactive serverogenin acetate with target proteins

Amomum nilgiricum is one of the plant species reported from Western Ghats of India, belonging to the family Zingiberaceae, with ethno-botanical values, and is well-known for their ethno medicinal applications. In the present investigation, ethyl acetate and methanol extracts of A. nilgiricum were analyzed by Fourier transform infrared spectrometer (FTIR) and gas chromatography-mass spectrometry (GC-MS) to identify the important functional groups and phytochemical constituents. The FTIR spectra revealed the occurrence of functional characteristic peaks of aromatic amines, carboxylic acids, ketones, phenols and alkyl halides group from leaf and rhizome extracts. The GC-MS analysis of ethyl acetate and methanol extracts from leaves, and methanol extract from rhizomes of A. nilgiricum detected the presence of 25 phytochemical compounds. Further, the leaf and rhizome extracts of A. nilgiricum showed remarkable antibacterial and antifungal activities at 100 mg/mL. The results of DPPH and ferric reducing antioxidant power assay recorded maximum antioxidant activity in A. nilgiricum methanolic leaf extract. While, ethyl acetate leaf extract exhibited maximum α-amylase inhibition activity, followed by methanolic leaf extract exhibiting aldose reductase inhibition. Subsequently, these 25 identified compounds were analyzed for their bioactivity through in silico molecular docking studies. Results revealed that among the phytochemical compounds identified, serverogenin acetate might have maximum antibacterial, antifungal, antiviral, antioxidant and antidiabetic properties followed by 2,4-dimethyl-1,3-dioxane and (1,3-13C2)propanedioic acid. To our best knowledge, this is the first description on the phytochemical constituents of the leaves and rhizomes of A. nilgiricum, which show pharmacological significance, as there has been no literature available yet on GC-MS and phytochemical studies of this plant species. The in silico molecular docking of serverogenin acetate was also performed to confirm its broad spectrum activities based on the binding interactions with the antibacterial, antifungal, antiviral, antioxidant and antidiabetic target proteins. The results of the present study will create a way for the invention of herbal medicines for several ailments by using A. nilgiricum plants, which may lead to the development of novel drugs.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Chinese Medicine for Psoriasis Vulgaris Based on Syndrome Pattern: A Network Pharmacological Study

BACKGROUND

The long-term use of conventional therapy for psoriasis vulgaris remains a challenge due to limited or no patient response and severe side effects. Complementary and alternative treatments such as traditional Chinese medicine (TCM) are widely used in East Asia. TCM treatment is based on individual syndrome types. Three TCM formulae, Compound Qingdai Pills (F1), Yujin Yinxie Tablets (F2), and Xiaoyin Tablets (F3), are used for blood heat, blood stasis, and blood dryness type of psoriasis vulgaris, respectively.

OBJECTIVES

To explore the mechanism of three TCM formulae for three syndrome types of psoriasis vulgaris.

METHODS

The compounds of the three TCM formulae were retrieved from the Psoriasis Database of Traditional Chinese Medicine (PDTCM). Their molecular properties of absorption, distribution, metabolism, excretion and toxicity (ADME/T), and drug-likeness were compared by analyzing the distribution of compounds in the chemical space. The cellular targets of the compounds were predicted by molecular docking. By constructing the compound-target network and analyzing network centrality, key targets and compounds for each formula were screened. Three syndrome types of psoriasis vulgaris related pathways and biological processes (BPs) were enriched by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8.

RESULTS

The compounds of the three formulae exhibited structural diversity, good drug-like properties, and ADME/T properties. A total of 72, 97 and 85 targets were found to have interactions with compounds of F1, F2, and F3, respectively. The three formulae were all related to 53 targets, 8 pathways, 9 biological processes, and 10 molecular functions (MFs). In addition, each formula had unique targets and regulated different pathways and BPs.

CONCLUSION

The three TCM formulae exhibited common mechanisms to some extent. The differences at molecular and systems levels may contribute to their unique applications in individualized treatment.

TCMIO: A Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology

Advances in immuno-oncology (IO) are making immunotherapy a powerful tool for cancer treatment. With the discovery of an increasing number of IO targets, many herbs or ingredients from traditional Chinese medicine (TCM) have shown immunomodulatory function and antitumor effects via targeting the immune system. However, knowledge of underlying mechanisms is limited due to the complexity of TCM, which has multiple ingredients acting on multiple targets. To address this issue, we present TCMIO, a comprehensive database of Traditional Chinese Medicine on Immuno-Oncology, which can be used to explore the molecular mechanisms of TCM in modulating the cancer immune microenvironment. Over 120,000 small molecules against 400 IO targets were extracted from public databases and the literature. These ligands were further mapped to the chemical ingredients of TCM to identify herbs that interact with the IO targets. Furthermore, we applied a network inference-based approach to identify the potential IO targets of natural products in TCM. All of these data, along with cheminformatics and bioinformatics tools, were integrated into the publicly accessible database. Chemical structure mining tools are provided to explore the chemical ingredients and ligands against IO targets. Herb–ingredient–target networks can be generated online, and pathway enrichment analysis for TCM or prescription is available. This database is functional for chemical ingredient structure mining and network analysis for TCM. We believe that this database provides a comprehensive resource for further research on the exploration of the mechanisms of TCM in cancer immunity and TCM-inspired identification of novel drug leads for cancer immunotherapy. TCMIO can be publicly accessed at http://tcmio.xielab.net.

Discovery of Multitarget-Directed Ligands Against Influenza A Virus From Compound Yizhihao Through a Predictive System for Compound-Protein Interactions

Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus–host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.

Anticancer Plants: A Review of the Active Phytochemicals, Applications in Animal Models, and Regulatory Aspects

The rising burden of cancer worldwide calls for an alternative treatment solution. Herbal medicine provides a very feasible alternative to western medicine against cancer. This article reviews the selected plant species with active phytochemicals, the animal models used for these studies, and their regulatory aspects. This study is based on a meticulous literature review conducted through the search of relevant keywords in databases, Web of Science, Scopus, PubMed, and Google Scholar. Twenty plants were selected based on defined selection criteria for their potent anticancer compounds. The detailed analysis of the research studies revealed that plants play an indispensable role in fighting different cancers such as breast, stomach, oral, colon, lung, hepatic, cervical, and blood cancer cell lines. The in vitro studies showed cancer cell inhibition through DNA damage and activation of apoptosis-inducing enzymes by the secondary metabolites in the plant extracts. Studies that reported in vivo activities of these plants showed remarkable results in the inhibition of cancer in animal models. Further studies should be performed on exploring more plants, their active compounds, and the mechanism of anticancer actions for use as standard herbal medicine.

Binding site matching in rational drug design: algorithms and applications

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

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.

Synthesis of Novel Baicalein Amino Acid Derivatives and Biological Evaluation as Neuroprotective Agents

Baicalein, a famously effective component of the traditional Chinese medicine Rhizoma Huang Qin (Scutellaria altissima L.), has been proved to have potent neuroprotection and anti-platelet aggregation effects with few side effects. Meanwhile, recent studies have revealed that the introduction of amino acid to baicalein could improve its neuroprotective activity. In the present study, a series of novel baicalein amino acid derivatives were designed, synthesized, and screened for their neuroprotective effect against tert-butyl, hydroperoxide-induced, SH-SY5Y neurotoxicity cells and toxicity on the normal H9C2 cell line by standard methylthiazol tetrazolium (MTT) assay. In addition, all of the newly synthesized compounds were characterized by ¹H-NMR, ¹³C-NMR, and high resolution mass spectrometry (HR-MS). The results showed that most of the compounds provided more potent neuroprotection than baicalein, and were equivalent to the positive drug edaravin. They showed no obvious cytotoxicity on normal H9C2 cells. Notably, the most active compound 8 displayed the highest protective effect (50% effective concentration (EC₅₀) = 4.31 μM) against tert-butyl, hydroperoxide-induced, SH-SY5Y neurotoxicity cells, which was much better than the baicalein (EC₅₀ = 24.77 μM) and edaravin (EC₅₀ = 5.62 μM). Further research on the chick chorioallantoic membrane (CAM) model indicated that compound 8 could significantly increase angiogenesis, which might promote neurovascular proliferation. The detection of apoptosis analysis showed that compound 8 could dramatically alleviate morphological manifestations of cell damage. Moreover, the benzyloxycarbonyl (cbz)-protected baicalein amino acid derivatives showed better neuroprotective activity than the t-Butyloxy carbonyl (boc)-protected derivatives.

Chrysophanol, Physcion, Hesperidin and Curcumin Modulate the Gene Expression of Pro-Inflammatory Mediators Induced by LPS in HepG2: In Silico and Molecular Studies

Hepatitis is an inflammatory condition that can develop hepatocellular carcinoma. Traditional medicine has always been the pillar of medical practice. However, it became less compatible with the current understanding of the diseases and the possible treatment. Therefore, in silico tools could be utilized for building the bridge between the legacy of the past and the current medical approaches allowing access to new therapeutic discoveries. In this work, a Chinese traditional medicine database was screened using structure-based virtual screening to identify molecules that could inhibit p38 alpha mitogen-activated protein kinase (MAPK). Out of the identified compounds, four selected compounds: chrysophanol, physcion, curcumin and hesperidin were isolated from their respective sources and their structures were confirmed by spectroscopic methods. These compounds decreased the gene expression of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-1beta (IL-1β) in lipopolysaccharide (LPS) induced inflammation in a hepatocellular carcinoma cell line (HepG2) in a dose-dependent manner. The molecular docking study revealed the specificity of these compounds towards p38 MAPK rather than other MAPKs. In conclusion, the molecular and in silico studies suggest that the isolated compounds could be a potential treatment for hepatitis by resolving inflammation controlled by MAPKs, thus limiting the development of further complications and lower side effects.

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

An Insight Into the Molecular Mechanism of Berberine Towards Multiple Cancer Types Through Systems Pharmacology

Over the past several decades, natural products with poly-pharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Berberine (PubChem CID: 2353), a soliloquies quaternary alkaloid, has been validated to exert powerful effects in many cancers. However, the underlying molecular mechanism is not yet fully elucidated. In this study, we summarized the molecular effects of berberine against multiple cancers based on current available literatures. Furthermore, a systems pharmacology infrastructure was developed to discover new cancer indications of berberine and explore their molecular mechanisms. Specifically, we incorporated 289 high-quality protein targets of berberine by integrating experimental drug-target interactions (DTIs) extracted from literatures and computationally predicted DTIs inferred by network-based inference approach. Statistical network models were developed for identification of new cancer indications of berberine through integration of DTIs and curated cancer significantly mutated genes (SMGs). High accuracy was yielded for our statistical models. We further discussed three typical cancer indications (hepatocarcinoma, lung adenocarcinoma, and bladder carcinoma) of berberine with new mechanisms of actions (MOAs) based on our systems pharmacology framework. In summary, this study systematically provides a powerful strategy to identify potential anti-cancer effects of berberine with novel mechanisms from a systems pharmacology perspective.

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

Backgrounds and Aims

Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI.

Materials and Methods

In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM.

Results

16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity.

Conclusion

Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs.

Systems Pharmacological Approach to Investigate the Mechanism of Ohwia caudata for Application to Alzheimer's Disease

Ohwia caudata (OC)-a traditional Chinese medicine (TCM)-has been reported to have large numbers of flavonoids, alkaloids, and triterpenoids. The previous studies on OC for treating Alzheimer's disease (AD) only focused on single targets and its mechanisms, while no report had shown about the synergistic mechanism of the constituents from OC related to their potential treatment on dementia in any database. This study aimed to predict the bioactive targets constituents and find potential compounds from OC with better oral bioavailability and blood-brain barrier permeability against AD, by using a system network level-based in silico approach. The results revealed that two new flavonoids, and another 26 compounds isolated from OC in our lab, were highly connected to AD-related signaling pathways and biological processes, which were confirmed by compound-target network, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, respectively. Predicted by the virtual screening and various network pharmacology methods, we found the multiple mechanisms of OC, which are effective for alleviating AD symptoms through multiple targets in a synergetic way.