Abstract: Background: Tingli Dazao Xiefei decoction (TDXD) has been shown to have a therapeutic effect on heart failure (HF). Nevertheless, its molecular mechanism for treating HF is still unclear. Materials and Methods: TDXD and HF targets were collected from the databases, and protein-protein interaction (PPI) analysis and enrichment analysis were performed on the overlapping targets. Then, AutoDock was employed for molecular docking. Finally, we used the left anterior descending coronary artery (LAD) ligation to induce HF model rats for in vivo experiments and verified the effect and mechanism of TDXD on HF. Results: Network pharmacological analysis showed that the main active components of TDXD in treating HF were quercetin, kaempferol, beta-carotene, isorhamnetin, and beta-sitosterol, and the core targets were IL-6, VEGFA, TNF, AKT1, and MAPK1. Multiple gene functions and signaling pathways were obtained by enrichment analysis, among which inflammation-related, PI3K/Akt, and MAPK signaling pathways were closely related to HF. Furthermore, the molecular docking results showed that the core targets had good binding ability with the main active components. Animal experiments showed that TDXD could effectively improve left ventricular ejection fraction (EF) and left ventricular fractional shortening (FS), decrease left ventricular internal diastolic diameter (LVIDd) and left ventricular internal systolic diameter (LVIDs), reduce the area of myocardial fibrosis, and decrease serum BNP, LDH, CK-MB, IL-6, IL-1beta, and TNF-alpha levels in HF rats. Meanwhile, TDXD could upregulate the expression of Bcl-2, downregulate the expression of Bax, and reduce cardiomyocyte apoptosis. At the same time, it was verified that TDXD could significantly decrease the expression of PI3K, P-Akt, and P-MAPK. Captopril showed similar effects. Conclusions: Combining network pharmacological analysis and experimental validation, this study verified that TDXD could improve cardiac function and protect against cardiac injury by inhibiting the activation of PI3K/Akt and MAPK signaling pathways.

Abstract: Sheng-Ji Hua-Yu (SJHY) formula has been proved to reduce the severity of diabetic wound healing without significant adverse events in our previous clinical trials. However, based on multi-target characteristics, the regulatory network among herbs, ingredients, and hub genes remains to be elucidated. The current study aims to identify the biomarkers of the SJHY formula for the treatment of diabetic wound healing. First, a network of components and targets for the SJHY formula was constructed using network pharmacology. Second, the ClusterONE algorithm was used to build a modular network and identify hub genes along with kernel pathways. Third, we verified the kernel targets by molecular docking to select hub genes. In addition, the biomarkers of the SJHY formula were validated by animal experiments in a diabetic wound healing mice model. The results revealed that the SJHY formula downregulated the mRNA expression of Cxcr4, Oprd1, and Htr2a, while upregulated Adrb2, Drd, Drd4, and Hrh1. Besides, the SJHY formula upregulated the kernel pathways, neuroactive ligand-receptor interaction, and cAMP signaling pathway in the skin tissue homogenate of the diabetic wound healing mice model. In summary, this study identified the potential targets and kernel pathways, providing additional evidence for the clinical application of the SJHY formula for the treatment of diabetic wound healing.

Abstract: Xiaoxianxiong Tang (XXXT) is a well-known traditional Chinese medicine formula. Evidence is emerging supporting the benefits of XXXT in ameliorating therapy for non-small cell lung cancer (NSCLC). The purpose of this study aimed to explore the effects and mechanisms of XXXT through network pharmacological analysis and biological validation. TCMSP database was used to identify potentially active compounds in XXXT with absorption, distribution, metabolism, excretion screening, and their potential targets. The disease targets related to NSCLC were predicted by searching for Therapeutic Target database, GeneCards database, DrugBank database, and DisGeNET database. Of the 4385 NSCLC-related targets, 156 targets were also the targets of compounds present in XXXT. Subsequently, GO function and KEGG pathway enrichment and PPI network analyses revealed that, of the 95 targets and 20 pathways influenced by 20 ingredients in XXXT, 20 targets were associated with patient survival, and XXXT could exert an inhibitory action on the PI3K-AKT signaling pathway. Moreover, XXXT restrained the proliferation of A549 and H460 cells in a concentration-dependent manner and suppressed the mRNA and protein levels of key targets CCNA2, FOSL2, and BIRC5 closely linked to the PI3K-AKT pathway. Hence, XXXT has the potential to improve therapy for NSCLC by targeting the PI3K-AKT signaling pathway.

Abstract: Background: Mechanical ventilation could lead to ventilator-induced lung injury (VILI), but its underlying pathogenesis remains largely unknown. In this study, we aimed to determine the genes which were highly correlated with VILI as well as their expressions and interactions by analyzing the differentially expressed genes (DEGs) between the VILI samples and controls. Methods: GSE11434 was downloaded from the gene expression omnibus (GEO) database, and DEGs were identified with GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using DAVID. Next, we used the STRING tool to construct protein-protein interaction (PPI) network of the DEGs. Then, the hub genes and related modules were identified with the Cytoscape plugins: cytoHubba and MCODE. qRT-PCR was further used to validate the results in the GSE11434 dataset. We also applied gene set enrichment analysis (GSEA) to discern the gene sets that had a significant difference between the VILI group and the control. Hub genes were also subjected to analyses by CyTargetLinker and NetworkAnalyst to predict associated miRNAs and transcription factors (TFs). Besides, we used CIBERSORT to detect the contributions of different types of immune cells in lung tissues of mice in the VILI group. By using DrugBank, small molecular compounds that could potentially interact with hub genes were identified. Results: A total of 141 DEGs between the VILI group and the control were identified in GSE11434. Then, seven hub genes were identified and were validated by using qRT-PCR. Those seven hub genes were largely enriched in TLR and JAK-STAT signaling pathways. GSEA showed that VILI-associated genes were also enriched in NOD, antigen presentation, and chemokine pathways. We predicted the miRNAs and TFs associated with hub genes and constructed miRNA-TF-gene regulatory network. An analysis with CIBERSORT showed that the proportion of M0 macrophages and activated mast cells was higher in the VILI group than in the control. Small molecules, like nadroparin and siltuximab, could act as potential drugs for VILI. Conclusion: In sum, a number of hub genes associated with VILI were identified and could provide novel insights into the pathogenesis of VILI and potential targets for its treatment.

Abstract: Background: Diminished ovarian reserve (DOR) significantly increases the risk of female infertility and contributes to reproductive technology failure. Recently, the role of melatonin in improving ovarian reserve (OR) has attracted widespread attention. However, details on the pharmacological targets and mechanisms of melatonin-improved OR remain unclear. Objective: A systems pharmacology strategy was proposed to elucidate the potential therapeutic mechanism of melatonin on DOR at the molecular, pathway, and network levels. Methods: The systems pharmacological approach consisted of target identification, data integration, network construction, bioinformatics analysis, and molecular docking. Results: From the molecular perspective, 26 potential therapeutic targets were identified. They participate in biological processes related to DOR development, such as reproductive structure development, epithelial cell proliferation, extrinsic apoptotic signaling pathway, PI3K signaling, among others. Eight hub targets (MAPK1, AKT1, EGFR, HRAS, SRC, ESR1, AR, and ALB) were identified. From the pathway level, 17 significant pathways, including the PI3K-Akt signaling pathway and the estrogen signaling pathway, were identified. In addition, the 17 signaling pathways interacted with the 26 potential therapeutic targets to form 4 functional modules. From the network point of view, by regulating five target subnetworks (aging, cell growth and death, development and regeneration, endocrine and immune systems), melatonin could exhibit anti-aging, anti-apoptosis, endocrine, and immune system regulation effects. The molecular docking results showed that melatonin bound well to all hub targets. Conclusion: This study systematically and intuitively illustrated the possible pharmacological mechanisms of OR improvement by melatonin through anti-aging, anti-apoptosis, endocrine, and immune system regulation effects.

Abstract: The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

Abstract: The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer's three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community.

Abstract: Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

Abstract: Rosacea is significantly associated with dementia, particularly Alzheimer's disease (AD). However, the common underlying molecular mechanism connecting these two diseases remains limited. This study aimed to reveal the common molecular regulatory networks and identify the potential therapeutic drugs for rosacea and AD. There were 747 overlapped DEGs (ol-DEGs) that were detected in AD and rosacea, enriched in inflammation-, metabolism-, and apoptosis-related pathways. Using the TF regulatory network analysis, 37 common TFs and target genes were identified as hub genes. They were used to predict the therapeutic drugs for rosacea and AD using the DGIdb/CMap database. Among the 113 predicted drugs, melatonin (MLT) was co-associated with both RORA and IFN-gamma in AD and rosacea. Subsequently, network pharmacology analysis identified 19 pharmacological targets of MLT and demonstrated that MLT could help in treating AD/rosacea partly by modulating inflammatory and vascular signaling pathways. Finally, we verified the therapeutic role and mechanism of MLT on rosacea in vivo and in vitro. We found that MLT treatment significantly improved rosacea-like skin lesion by reducing keratinocyte-mediated inflammatory cytokine secretion and repressing the migration of HUVEC cells. In conclusion, this study contributes to common pathologies shared by rosacea and AD and identified MLT as an effective treatment strategy for rosacea and AD via regulating inflammation and angiogenesis.

Abstract: Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.