Abstract: CONTEXT: Due to the poor prognosis of T-cell acute lymphoblastic leukaemia (T-ALL), there is an urgent need to identify safer and more cost-effective drugs. OBJECTIVE: This study evaluated the antitumour activity of Shuanghuanglian (SHL) on T-ALL cells and elucidated the mechanism. MATERIALS AND METHODS: Jurkat and Molt4 cells were treated with SHL (0.1, 0.2 and 0.4 mg/mL) for 24 and 48 h. The controls were treated with RPMI 1640 containing 10% foetal bovine serum. Cell viability was evaluated through Cell Counting Kit-8 assay. Patterns of death and signalling pathway alterations caused by SHL were identified by network pharmacology combined with GO enrichment analysis and then were verified by Hoechst 33342 staining, Annexin V-FITC/PI staining and Western blotting. Interactions of the active ingredients with targets were analysed by molecular docking. RESULTS: The IC(50) values of SHL in Jurkat and Molt4 cells were 0.30 +/- 0.10 and 0.48 +/- 0.07 mg/mL, respectively, at 24 h and 0.27 +/- 0.05 and 0.30 +/- 0.03 mg/mL at 48 h. In T-ALL, 117 target genes of SHL were mainly enriched in the apoptosis and NOTCH signalling pathways. SHL induced apoptosis was confirmed by Hoechst 33342 staining and flow cytometry. The protein levels of cleaved caspase-7 and cleaved PARP were significantly increased but those of cleaved NOTCH1 and MYC were reduced. The active ingredients of SHL can interact with gamma-secretase.Discussion and conclusions: SHL induces apoptosis in T-ALL cells via the NOTCH1-MYC pathway and may be a potential drug for the treatment of T-ALL.

Abstract: BACKGROUND: With the intensification of population aging, the proportion of aging livers in the donor pool is increasing rapidly. Compared with young livers, aging livers are more susceptible to ischemia-reperfusion injury (IRI) during liver transplantation, which greatly affects the utilization rate of aging livers. The potential risk factors associated with IRI in aging livers have not been fully elucidated. METHODS: In this work, five human liver tissue expression profiling datasets (GSE61260, GSE107037, GSE89632, GSE133815, and GSE151648) and a total of 28 young and aging liver tissues of human (N = 20) and mouse (N = 8) were used to screen and verify the potential risk factors associated with aging livers being more prone to IRI. DrugBank Online was used to screen drugs with potential to alleviate IRI in aging livers. RESULTS: The gene expression profile and immune cell composition between young and aging livers had significant differences. Among the differentially expressed genes, aryl hydrocarbon receptor nuclear translocator-like (ARNTL), BTG antiproliferation factor 2 (BTG2), C-X-C motif chemokine ligand 10 (CXCL10), chitinase 3-like 1 (CHI3L1), immediate early response 3 (IER3), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha (PPARGC1A), mainly involved in the regulation of cell proliferation, metabolism, and inflammation, were also dysregulated in liver tissues suffered from IRI and could form a FOS-centered interaction network. Nadroparin was screened out with the potential to target FOS in DrugBank Online. In addition, the proportion of dendritic cells (DCs) was significantly upregulated in aging livers. CONCLUSIONS: We combined the expression profiling datasets of liver tissues and samples collected in our hospital for the first time to reveal that the changes in the expression of ARNTL, BTG2, CXCL10, CHI3L1, IER3, FOS, and PPARGC1A and the proportion of dendritic cells may be associated with aging livers being more prone to IRI. Nadroparin may be used to mitigate IRI in aging livers by targeting FOS, and regulation of DC activity may also reduce IRI.

Abstract: OBJECTIVE: We used network pharmacology, molecular docking, and cellular analysis to explore the pharmacodynamic components and action mechanism of Alpinia officinarum Hance (A. officinarum) in improving type 2 diabetes mellitus (T2DM). METHODS: The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the potential targets and mechanism of A. officinarum toward improving T2DM. The first 9 core targets and potential active compounds were docked using Discovery Studio 2019. Finally, IR-HepG2 cells and qPCR were applied to determine the mRNA expression of the top 6 core targets of the PPI network. RESULTS: A total of 29 active ingredients and 607 targets of A. officinarum were obtained. T2DM-related targets overlapped with 176 targets. The core targets of the PPI network were identified as AKT serine/threonine kinase 1 (AKT1), an activator of transcription 3 (STAT3), tumor necrosis factor (TNF), tumor protein p53 (TP53), SRC proto-oncogene, nonreceptor tyrosine kinase (SRC), epidermal growth factor receptor (EGFR), albumin (ALB), mitogen-activated protein kinase 1 (MAPK1), and peroxisome proliferator-activated receptor gamma (PPARG). A. officinarum performs an antidiabetic role via the AGE-RAGE signaling pathway, the HIF-1 signaling pathway, the PI3K-AKT signaling pathway, and others, according to GO and KEGG enrichment analyses. Molecular docking revealed that the binding ability of diarylheptanoid active components in A. officinarum to core target protein was higher than that of flavonoids. The cell experiments confirmed that the A. officinarum extracts improved the glucose uptake of IR-HepG2 cells and AKT expression while inhibiting the STAT3, TNF, TP53, SRC, and EGFR mRNA expression. CONCLUSION: A. officinarum Hance improves T2DM by acting on numerous components, multiple targets, and several pathways. Our results lay the groundwork for the subsequent research and broaden the clinical application of A. officinarum Hance.

Abstract: Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Abstract: Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.

Abstract: OBJECTIVE: To analyze the effects and mechanisms of berberine in the treatment of aging-related cognitive dysfunction based on network pharmacology methods, molecular docking techniques, and animal experiments. METHODS: A mouse model of cognitive dysfunction was constructed by subcutaneous injection of D-galactose (D-gal) for 10 weeks, and the neuroprotective effects of berberine on aging-related cognitive dysfunction mice were evaluated by the Morris water maze (MWM) and immunofluorescence staining. The targets of berberine were obtained by SwissTargetPrediction, GeneCards, and PharmMapper. Putative targets of cognitive dysfunction were obtained by GeneCards, TTD, and DrugBank database. The STRING database and Cytoscape software were applied for protein-protein interaction (PPI) analysis and further screening of core targets. The DAVID database was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis to clarify the biological processes and pathways involved in the intersection targets, and AutoDockTools was adopted for molecular docking verification of core targets. Finally, the core genes were validated using real-time quantitative PCR. RESULTS: The MWM results showed that treatment with berberine significantly improved spatial learning and memory in mice with cognitive decline induced by D-gal. Immunofluorescence staining indicated that berberine modified the levels of aging-related markers in the brain. A total of 386 berberine putative targets associated with cognitive dysfunction were identified based on the public database. The core targets of berberine for improving cognitive function, include Mapk1, Src, Ctnnb1, Akt1, Pik3ca, Tp53, Jun, and Hsp90aa1. GO enrichment and KEGG pathway enrichment analyses indicated that the mechanism of berberine in the treatment of aging-related cognitive dysfunction is attributed to pathways such as PI3K-AKT and MAPK pathways. In vivo experiments further confirmed that Akt1, Ctnnb1, Tp53, and Jun were involved in the neuroprotective actions of berberine. CONCLUSION: This study reveals the multi-target and multi-pathway effects of berberine on regulating aging-related cognitive dysfunction, which provides preclinical evidence and may promote new drug development in mitigating cognitive dysfunction.

Abstract: Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

Abstract: Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.

Abstract: Due to the lack of classic estrogen receptors, there has been a shortage of targeted therapy for triple-negative breast cancer (TNBC), resulting in a poor prognosis. However, the newly discovered G protein-coupled estrogen receptor (GPER) has been found to be expressed in TNBC cells. Salvia miltiorrhiza (Danshen) is an essential Chinese medicine for gynecological disorders, and its component tanshinone IIA (Tan IIA) exerts an anticancer effect. Therefore, this study attempted to investigate whether GPER is involved in the inhibitory effect of Tan IIA on TNBC. We applied various databases and GO pathway analysis to predict the possible mechanism of Tan IIA. We identified 39 overlapping targets, including c-Jun, c-Fos, and caspase-3, and enriched cell cycle-related pathways. Next, we demonstrated the strong binding ability of Tan IIA to GPER by molecular docking assay. In the subsequent validation tests, Cell Counting Kit-8 (CCK8) assay showed that Tan IIA inhibited proliferation of MDA-MB-231 cells time and dose dependently without affecting normal cells. Using Transwell plate, flow cytometry, and Western blot assays, we showed that Tan IIA inhibited migration and induced apoptosis of MDA-MB-231 dose dependently. Importantly, protein expressions of GPER, epidermal growth factor receptor (EGFR), extracellular regulated protein kinases (ERK), c-Fos, and c-Jun were all decreased by Tan IIA dose dependently. Administration of GPER inhibitor partly abolished these effects. Furthermore, nuclear translocation of c-Fos and c-Jun as well as cell cycle-related proteins was downregulated by Tan IIA dose dependently. In summary, Tan IIA could inhibit the proliferation and migration of MDA-MB-231 cells and induce apoptosis, and the possible mechanism may be the regulation of GPER-mediated pathways, suggesting that GPER could be a therapeutic target for TNBC.

Abstract: INTRODUCTION: Toll-like receptor 4 (TLR4) has attracted interest due to its role in chemotherapy-induced gastrointestinal inflammation. This structural study aimed to provide in silico rational of the recognition and potential binding of TLR4 ligands IAXO-102, TAK-242, and SN-38 (the toxic metabolite of the chemotherapeutic irinotecan hydrochloride), which could contribute to rationale development of therapeutic anti-inflammation drugs targeting TLR4 in the gastrointestinal tract. METHODS: In silico docking was performed between the human TLR4-MD-2 complex and ligands (IAXO-102, TAK-242, SN-38) using Autodock Vina, setting the docking grids to cover either the upper or the lower bound of TLR4. The conformation having the lowest binding energy value (kcal/mol) was processed for post-hoc analysis of the best-fit model. Hydrogen bonding was calculated by using ChimeraX. RESULTS: Binding energies of IAXO-102, TAK-242 and SN-38 at the upper bound of TLR4-MD-2 ranged between - 3.8 and - 3.1, - 6.9 and - 6.3, and - 9.0 and - 7.0, respectively. Binding energies of IAXO-102, TAK-242 and SN-38 at the lower bound ranged between - 3.9 and - 3.5, - 6.5 and - 5.8, and - 8.2 and - 6.8, respectively. Hydrogen bonding at the upper bound of TLR4/MD-2 with IAXO-102, TAK-242 and SN-38 was to aspartic acid 70, cysteine 133 and serine 120, respectively. Hydrogen bonding at the lower bound of TLR4-MD-2 with IAXO-102, TAK-242 and SN-38 was to serine 528, glycine 480 and glutamine 510, respectively. CONCLUSION: The in silico rational presented here supports further investigation of the binding activity of IAXO-102 and TAK-242 for their potential application in the prevention of gastrointestinal inflammation caused by SN-38.