Abstract: Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

Abstract: Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Abstract: Eczema is a complex chronic inflammatory skin disease impacted by environmental factors, infections, immune disorders, and deficiencies in skin barrier function. Shi Zhen Tea (SZT), derived from traditional Chinese medicine Xiao-Feng-San, has shown to be an effective integrative therapy for treating skin lesions, itching, and sleeping loss, and it facilitates reduction of topical steroid and antihistamine use in pediatric and adult patients with severe eczema. Yet, its active compounds and therapeutic mechanisms have not been elucidated. In this study, we sought to investigate the active compounds and molecular mechanisms of SZT in treating eczema using systems pharmacology and in silico docking analysis. SZT is composed of 4 medicinal herbs, Baizhu (Atractylodis macrocephalae rhizome), Jingjie (Schizonepetae herba), Kushen (Sophorae flavescentis radix), and Niubangzi (Arctii fructus). We first identified 51 active compounds from SZT and their 81 potential molecular targets by high-throughput computational analysis, from which we identified 4 major pathways including Th17 cell differentiation, metabolic pathways, pathways in cancer, and the PI3K-Akt signaling pathway. Through network analysis of the compound-target pathway, we identified hub molecular targets within these pathways including carbonic anhydrase II (CA2), peroxisome proliferator activated receptor gamma (PPAR gamma), retinoid X receptor alpha (RXRA), and vitamin D receptor (VDR). We further identified top 5 compounds including cynarine, stigmasterin, kushenol, beta-sitosterol, and (24S)-24-propylcholesta-5-ene-3beta-ol as putative key active compounds on the basis of their molecular docking scores with identified hub target proteins. Our study provides an insight into the therapeutic mechanism underlying multiscale benefits of SZT for eczema and paves the way for developing new and potentially more effective eczema therapies.

Abstract: Guanxin-Shutong capsule (GXSTC), a combination of Mongolian medicines and traditional herbs, has been clinically proven to be effective in treating cerebrovascular diseases (CBVDs). However, the underlying pharmacological mechanisms of GXSTC in CBVDs remain largely unknown. In this study, a combination of systems pharmacology and experimental assessment approach was used to investigate the bioactive components, core targets, and possible mechanisms of GXSTC in the treatment of CBVDs. A total of 15 main components within GXSTC were identified using high-performance liquid chromatography coupled with diode array detector (HPLC-DAD) and a literature research. Fifty-five common genes were obtained by matching 252 potential genes of GXSTC with 462 CBVD-related genes. Seven core components in GXSTC and 12 core genes of GXSTC on CBVDs were further determined using the protein-protein interaction (PPI) and component-target-pathway (C-T-P) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results predicted that the molecular mechanisms of GXSTC on CBVDs were mainly associated with the regulation of the vascular endothelial function, inflammatory response, and neuronal apoptosis. Molecular docking results suggested that almost all of core component-targets have an excellent binding activity (affinity < -5 kcal/mol). More importantly, in middle cerebral artery occlusion (MCAO) -injured rats, GXSTC significantly improved the neurological function, reduced the infarct volume, and decreased the percentage of impaired neurons in a dose-dependent manner. Western blotting results indicated that GXSTC markedly upregulated the expression of vascular endothelial growth factor A (VEGFA) and endothelial nitric oxide synthase (eNOS), while downregulating the expression of cyclooxygenase-2 (COX-2) and transcription factor AP-1 (c-Jun) in MCAO-injured rats. These findings confirmed our prediction that GXSTC exerts a multi-target synergetic mechanism in CBVDs by maintaining vascular endothelial function, inhibiting neuronal apoptosis and inflammatory processes. The results of this study may provide a theoretical basis for GXSTC research and the clinical application of GXSTC in CBVDs.

Abstract: As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.

Abstract: Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein-protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.

Abstract: Background: Colorectal cancer (CRC) is one of the most common gastrointestinal tumors, which accounts for approximately 10% of all diagnosed cancers and cancer deaths worldwide per year. Scutellariae barbatae Herba (SBH) is one of the most frequently used traditional Chinese medicine (TCM) in the treatment of CRC. Although many experiments have been carried out to explain the mechanisms of SBH, the mechanisms of SBH have not been illuminated fully. Thus, we constructed a network pharmacology and molecular docking to investigate the mechanisms of SBH. Methods: We adopted active constituent prescreening, target predicting, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, differentially expressed gene analysis, and molecular docking to establish a system pharmacology database of SBH against CRC. Results: A total of 64 active constituents of SBH were obtained and 377 targets were predicted, and the result indicated that quercetin, luteolin, wogonin, and apigenin were the main active constituents of SBH. Glucocorticoid receptor (NR3C1), pPhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), cellular tumor antigen p53 (TP53), transcription factor AP-1 (JUN), mitogen-activated protein kinase 1 (MAPK1), Myc protooncogene protein (MYC), cyclin-dependent kinase 1 (CDK1), and broad substrate specificity ATP-binding cassette transporter ABCG2 (ABCG2) were the major targets of SBH in the treatment of CRC. GO analysis illustrated that the core biological process regulated by SBH was the regulation of the cell cycle. Thirty pathways were presented and 8 pathways related to CRC were involved. Molecular docking presented the binding details of 3 key targets with 6 active constituents. Conclusions: The mechanisms of SBH against CRC depend on the synergistic effect of multiple active constituents, multiple targets, and multiple pathways.

Abstract: Objective: Chaihu Shugan San (CSS) is a common antidepressant prescription in traditional Chinese medicines. However, its active ingredients and mechanisms are unknown. The aim of this study was to explore the potential active ingredients and pharmacological mechanisms of CSS for the treatment of major depressive disorder (MDD). Methods: Active compounds in CSS were screened using the Traditional Chinese Medicine Systems Pharmacology database. Compound-related targets were retrieved using the SwissTargetPrediction database. MDD-related targets were determined using DisGeNET, Therapeutic Target Database and DrugBank databases. The common targets of active compounds in CSS and MDD were retained to construct a compound-MDD target network. Then, functional enrichment analysis and protein-protein interaction analysis were performed to identify hub targets and explore the underlying molecular mechanisms. Finally, hub-targeted genes and pathways were validated by Western blotting and immunofluorescence using chronic unpredictable mild stress (CUMS) mice with or without CSS treatment. The affinities between the active compounds in CSS and hub-targeted genes were evaluated by molecular docking. Results: Network pharmacology analysis revealed 24 potential targets for treatment of MDD by CSS. Functional enrichment analysis showed that PI3K/AKT signaling pathway was likely to be evidently affected by CSS in the treatment of MDD. In vivo experiments showed that CSS could improve depressive-like behaviors and promote neurogenesis in CUMS mice. Furthermore, CSS could increase phosphorylated (p) PI3K/PI3K and pAKT/AKT levels and decrease the pGSK3beta/GSK3beta level in the hippocampus of CUMS mice. The active compounds mainly included quercetin and luteolin, which showed good docking scores targeting the PI3K protein. Conclusion: This network pharmacological and experimental study highlights that the PI3K/AKT pathway is the potential mechanism by which CSS is involved in MDD treatment. Quercetin, luteolin, and kaempferol are probable active compounds in CSS, and these results might provide valuable guidance for further studies of MDD treatment.

Abstract: SARS-CoV-2 requires serine protease, transmembrane serine protease 2 (TMPRSS2), and cysteine proteases, cathepsins B, L (CTSB/L) for entry into host cells. These host proteases activate the spike protein and enable SARS-CoV-2 entry. We herein performed genomic-guided gene set enrichment analysis (GSEA) to identify upstream regulatory elements altering the expression of TMPRSS2 and CTSB/L. Further, medicinal compounds were identified based on their effects on gene expression signatures of the modulators of TMPRSS2 and CTSB/L genes. Using this strategy, estradiol and retinoic acid have been identified as putative SARS-CoV-2 alleviation agents. Next, we analyzed drug-gene and gene-gene interaction networks using 809 human targets of SARS-CoV-2 proteins. The network results indicate that estradiol interacts with 370 (45%) and retinoic acid interacts with 251 (31%) human proteins. Interestingly, a combination of estradiol and retinoic acid interacts with 461 (56%) of human proteins, indicating the therapeutic benefits of drug combination therapy. Finally, molecular docking analysis suggests that both the drugs bind to TMPRSS2 and CTSL with the nanomolar to low micromolar affinity. The results suggest that these drugs can simultaneously target both the entry pathways of SARS-CoV-2 and thus can be considered as a potential treatment option for COVID-19.

Abstract: Gynura procumbens (GP) is a perennial herbal medicine and food homologous plant, which has been reported to have a good hypoglycemic effect. However, its active components and underlying mechanism of action are not clear. Here, we aimed to confirm the effects of GP on type 2 diabetes (T2DM) from several different aspects. We used UPLC/Q-TOF MS to analyze the metabolic patterns, which included blood samples of clinical subjects and db/db mice to screen for serum metabolic markers and metabolic pathways. We also used network pharmacology to study GP targets in the treatment of T2DM. Data from endogenous metabolites in plasma showed that two common pathways, including glycerol phosphate metabolism and retinol metabolism, were identified in plasma samples of the groups. Finally, Western blot analysis was used to verify the expression of proteins in the PI3K/AKT and AGE-RAGE signaling pathways. The protein expression of AKT, eNOS, iNS, and MAPK was significantly upregulated, and the expression of caspase-8 and caspase-3 was significantly downregulated. Thus, our findings indicated that GP could alleviate insulin resistance by regulating biometabolic markers and key proteins in the PI3K/AKT and AGE-RAGE signaling pathways to treat T2DM.