Abstract: Background: Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods: The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the "SZF-key targets-DKD" network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results: We obtained 182 potential targets and 30 key targets. Furthermore, a "SZF-key targets-DKD" network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion: This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

Abstract: Fluvoxamine is a well-tolerated, widely available, inexpensive selective serotonin reuptake inhibitor that has been shown in a small, double-blind, placebo-controlled, randomized study to prevent clinical deterioration of patients with mild coronavirus disease 2019 (COVID-19). Fluvoxamine is also an agonist for the sigma-1 receptor, through which it controls inflammation. We review here a body of literature that shows important mechanisms of action of fluvoxamine and other SSRIs that could play a role in COVID-19 treatment. These effects include: reduction in platelet aggregation, decreased mast cell degranulation, interference with endolysosomal viral trafficking, regulation of inositol-requiring enzyme 1alpha-driven inflammation and increased melatonin levels, which collectively have a direct antiviral effect, regulate coagulopathy or mitigate cytokine storm, which are known hallmarks of severe COVID-19.

Abstract: Multi-omics molecules regulate complex biological processes (CBPs), which reflect the activities of various molecules in living organisms. Meanwhile, the applications to represent disease subtypes and cell types have created an urgent need for sample grouping and associated CBP-inferring tools. In this paper, we present CBP-JMF, a practical tool primarily for discovering CBPs, which underlie sample groups as disease subtypes in applications. Differently from existing methods, CBP-JMF is based on a joint non-negative matrix tri-factorization framework and is implemented in Python. As a pragmatic application, we apply CBP-JMF to identify CBPs for four subtypes of breast cancer. The result shows significant overlapping between genes extracted from CBPs and known subtype pathways. We verify the effectiveness of our tool in detecting CBPs that interpret subtypes of disease.

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: SARS-CoV-2 has triggered a major epidemic among people around the world, and it is the newest in the sequence to become prevalent among other infectious diseases. The drug repurposing concept has been utilized effectively for numerous viral infections. Considering the situation and the urgency, the idea of drug repurposing for coronavirus infection (COVID-19) is also being studied. The molecular docking method was used for the screening of 29 antiviral drugs against primary protease proteins (MPP) of SARS-CoV-2, spike ecto-domain, spike receptor binding domain, Nsp9 RNA binding protein, and HR2 domain. Among these drugs, in terms of least binding energy, Indinavir, Sorivudine, Cidofovir, and Darunavir showed minimum docking scores with all the key proteins. For ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis, the ADMET properties of the top 4 drug candidates were retrieved through literature study. This analysis revealed that these drug candidates are well metabolized, distributed, and bioavailable, but have some undesirable effects. Furthermore, some approved structural analogues, such as Telbivudine, Tenofovir, Amprenavir, Fosamprenavir, etc., were predicted as similar drugs which may also be used for treating viral infections. We highly recommend these drug candidates as potential fighters against the deadly SARS-CoV-2 virus, and suggest in vivo trials for experimental validation of our findings.

Abstract: Huang-Lian-Jie-Du decoction (HLJDD) has been used to treat pneumonia for thousands of years in China. However, our understanding of its mechanisms on treating pneumonia is still unclear. In the present work, network pharmacology was used to analyze the potential active ingredients and molecular mechanisms of HLJDD on treating pneumonia. A total of 102 active ingredients were identified from HLJDD, among which 54 were hit by the 69 targets associated with pneumonia. By performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we obtained the main pathways associated with pneumonia and those associated with the mechanism of HLJDD in the treatment of pneumonia. By constructing the protein-protein interaction network of common targets, 10 hub genes were identified, which were mainly involved in the tumor necrosis factor (TNF) signaling pathway, interleukin 17 (IL-17) signaling pathway, and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway. Moreover, the results of molecular docking showed that the active ingredients of HLJDD had a good affinity with the hub genes. The final results indicate that HLJDD has a greater effect on bacterial pneumonia than on viral pneumonia. The therapeutic effect is mainly achieved by regulating the host immune inflammatory response and oxidative stress reaction, antibacterial microorganisms, alleviating the clinical symptoms of pneumonia, repairing damaged cells, and inhibiting cell migration.

Abstract: Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.

Abstract: Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http://zoulab.dalton.missouri.edu/mdock.htm .

Abstract: A genome-wide association study (GWAS) has discovered that a polymorphism in the ZFP90 gene is associated with systemic lupus erythematosus (SLE). In this study, we explored the candidate function of a ZFP90 variant (rs1170426) in the context of SLE and detected the relationship between SLE susceptible genes and SLE drug target genes. First, we investigated the regulatory role of rs1170426 on ZFP90 expression by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells (PBMCs), T, B, and monocytes cells and annotated the regulatory function of rs1170426 using bioinformatic databases. Second, we compared the case-control difference in ZFP90 expression levels. Third, we analyzed the association of genotype and ZFP90 expression levels with SLE clinical characters. Last, we showed the interaction of SLE susceptibility genes with SLE drug target genes. Subjects with the risk allele "C" of rs1170426 had lower expression levels of ZFP90 in PBMCs (P = 0.006) and CD8+ T cells (P = 0.003) from controls. SLE cases also had lower expression levels compared with controls (P = 2.78E-9). After correction for multiple testing, the ZFP90 expression levels were related to serositis (FDR p = 0.004), arthritis (FDR p = 0.020), hematological involvement (FDR p = 0.021), and increased C-reactive protein (CRP) (FDR p = 0.005) in cases. Furthermore, the SLE susceptible genes and the recognized SLE drug target genes were more likely to act upon each other compared with non-SLE genetic genes (OR = 2.701, P = 1.80E-5). These findings suggest that ZFP90 might play a role in the pathogenesis of SLE, and SLE genetics would contribute to therapeutic drug discovery.