Abstract: The number of transporter proteins that are not fully characterized is immense. Here, we used Drosophila melanogaster and human cell lines to perform a first in-depth characterization of CG4928, an ortholog to the human UNC93A, of which little is known. Solute carriers regulate and maintain biochemical pathways important for the body, and malfunctioning transport is associated with multiple diseases. Based on phylogenetic analysis, CG4928 is closely related to human UNC93A and has a secondary and a tertiary protein structure and folding similar to major facilitator superfamily transporters. Ubiquitous knockdown of CG4928 causes flies to have a reduced secretion rate from the Malpighian tubules; altering potassium content in the body and in the Malpighian tubules, homologous to the renal system; and results in the development of edema. The edema could be rescued by using amiloride, a common diuretic, and by maintaining the flies on ion-free diets. CG4928-overexpressing cells did not facilitate the transport of sugars and amino acids; however, proximity ligation assay revealed that CG4928 co-localized with TASK(1) channels. Overexpression of CG4928 resulted in induced apoptosis and cytotoxicity, which could be restored when cells were kept in high-sodium media. Furthermore, the basal membrane potential was observed to be disrupted. Taken together, the results indicate that CG4928 is of importance for generating the cellular membrane potential by an unknown manner. However, we speculate that it most likely acts as a regulator or transporter of potassium flows over the membrane.

Abstract: BACKGROUND: The receptor binding domain (RBD) of spike protein S1 domain SARS-CoV-2 plays a key role in the interaction with ACE2, which leads to subsequent S2 domain mediated membrane fusion and incorporation of viral RNA into host cells. In this study we tend to repurpose already approved drugs as inhibitors of the interaction between S1-RBD and the ACE2 receptor. METHODS: 2456 approved drugs were screened against the RBD of S1 protein of SARS-CoV-2 (target PDB ID: 6M17). As the interacting surface between S1-RBD and ACE2 comprises of bigger region, the interacting surface was divided into 3 sites on the basis of interactions (site 1, 2 and 3) and a total of 5 grids were generated (site 1, site 2, site 3, site 1+site 2 and site 2+site 3). A virtual screening was performed using GLIDE implementing HTVS, SP and XP screening. The top hits (on the basis of docking score) were further screened for MM-GBSA. All the top hits were further evaluated in molecular dynamics studies. Performance of the virtual screening protocol was evaluated using enrichment studies. RESULT: and discussion: We performed 5 virtual screening against 5 grids generated. A total of 42 compounds were identified after virtual screening. These drugs were further assessed for their interaction dynamics in molecular dynamics simulation. On the basis of molecular dynamics studies, we come up with 10 molecules with favourable interaction profile, which also interacted with physiologically important residues (residues taking part in the interaction between S1-RBD and ACE2. These are antidiabetic (acarbose), vitamins (riboflavin and levomefolic acid), anti-platelet agents (cangrelor), aminoglycoside antibiotics (Kanamycin, amikacin) bronchodilator (fenoterol), immunomodulator (lamivudine), and anti-neoplastic agents (mitoxantrone and vidarabine). However, while considering the relative side chain fluctuations when compared to the S1-RBD: ACE2 complex riboflavin, fenoterol, cangrelor and vidarabine emerged out as molecules with prolonged relative stability. CONCLUSION: We identified 4 already approved drugs (riboflavin, fenoterol, cangrelor and vidarabine) as possible agents for repurposing as inhibitors of S1:ACE2 interaction. In-vitro validation of these findings are necessary for identification of a safe and effective inhibitor of S1: ACE2 mediated entry of SARS-CoV-2 into the host cell.

Abstract: Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

Abstract: Introduction: Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age-related neurodegenerative diseases (NDD). Methods: This retrospective cohort study surveyed US-based Humana claims, which includes prescription and patient records from private-payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin. Results: Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow-up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44-0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54-0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41-0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48-0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30-0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65-1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy. Discussion: Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at-risk NDD populations. Common statin activated pathways indicate overarching systems required for risk reduction whereas unique targets could advance a precision medicine approach to prevent neurodegenerative diseases.

Abstract: Acute respiratory distress syndrome (ARDS) is a complex cascade that develops from acute lung injury (ALI). Ginseng can be used to treat ALI/ARDS. Studies have shown that some of ingredients in ginseng had anti-inflammation, antioxidative, and immune regulation effects and can protect alveolar epithelial cells in mice. However, the potential targets, biological processes, and pathways related to ginseng against ALI/ARDS have not been investigated systematically. We employed network pharmacology, molecular docking, and animal experiments to explore the therapeutic effects and underlying mechanism of action of ginseng against ALI/ARDS. We identified 25 compounds using ultrahigh-performance liquid chromatography Q-Orbitrap mass spectrometry and their 410 putative targets through database analyses. Sixty-nine of them were considered to be key targets of ginseng against ALI/ARDS according to overlapping with ALI/ARDS-related targets and further screening in a protein-protein interaction (PPI) network. The phosphatidylinositol 3-kinase-protein kinase B (PI3K-AkT) and mitogen-activated protein kinase (MAPK) pathways were recognized to have critical roles for ginseng in ALI/ARDS treatment. Signal transducer and activator of transcription (STAT) 3, vascular endothelial growth factor A (VEGFA), fibroblast growth factor (FGF) 2, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), MAPK1, and interleukin (IL) 2 were the top six nodes identified by analyses of a compound-target-pathway network. Molecular docking showed that most of the ingredients in ginseng could combine well with the six nodes. Ginseng could reduce the pathologic damage, neutrophil aggregation, proinflammatory factors, and pulmonary edema in vivo and inhibit the PI3K-Akt signaling pathway and MAPK signaling pathway through downregulating expressions of STAT3, VEGFA, FGF2, PIK3CA, MAPK1, and IL2. Our study provides a theoretical basis for ginseng treatment of ALI/ARDS.

Abstract: Identification of the toxicity of compounds is more crucial before entering clinical trials. Awareness of physiochemical properties, possible targets and side effects has become a major public health issue to reduce risks. Experimental determination of analyzing the physiochemical properties of a drug, their interaction with specific receptors and identifying their side-effects remain challenging is time consuming and costly. We describe a manually compiled database named DaiCee database, which contains 2100 anticancer drugs with information on their physiochemical properties, targets of action and side effects. It includes both synthetic and herbal anti-cancer compounds. It allows the search for SMILES notation, Lipinski's and ADME/T properties, targets and side effect profiles of the drugs. This helps to identify drugs with effective anticancer properties, their toxic nature, drug-likeness for in-vitro and in-vivo experiments. It also used for comparative analysis and screening of effective anticancer drugs using available data for compounds in the database. The database will be updated regularly to provide the users with latest information. The database is available at the URL http://www.hccbif.org/usersearch.php.

Abstract: Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

Abstract: Sphingosine kinase 1 (SphK1) is a promising therapeutic target against several diseases including mammary cancer. The aim of present work is to identify a potent lead compound against breast cancer using ligand-based virtual screening, molecular docking, MD simulations, and the MMPBSA calculations. The LBVS in molecular and virtual libraries yielded 20,800 hits, which were reduced to 621 by several parameters of drug-likeness, lead-likeness, and PAINS. Furthermore, 55 compounds were selected by ADMET descriptors carried forward for molecular interaction studies with SphK1. The binding energy (DeltaG) of three screened compounds namely ZINC06823429 (-11.36 kcal/mol), ZINC95421501 (-11.29 kcal/mol), and ZINC95421070 (-11.26 kcal/mol) exhibited stronger than standard drug PF-543 (-9.9 kcal/mol). Finally, it was observed that the ZINC06823429 binds tightly to catalytic site of SphK1 and remain stable during MD simulations. This study provides a significant understanding of SphK1 inhibitors that can be used in the development of potential therapeutics against breast cancer.

Abstract: Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Abstract: Background: Qi She Pill (QSP) is a traditional prescription for the treatment of neuropathic pain (NP) that is widely used in China. However, no network pharmacology studies of QSP in the treatment of NP have been conducted to date. Objective: To verify the potential pharmacological effects of QSP on NP, its components were analyzed via target docking and network analysis, and network pharmacology methods were used to study the interactions of its components. Materials and Methods: Information on pharmaceutically active compounds in QSP and gene information related to NP were obtained from public databases, and a compound-target network and protein-protein interaction network were constructed to study the mechanism of action of QSP in the treatment of NP. The mechanism of action of QSP in the treatment of NP was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment, and the drug-like component-target-pathway network was constructed. Results: The compound-target network contained 60 compounds and 444 corresponding targets. The key active compounds included quercetin and beta-sitosterol. Key targets included PTGS2 and PTGS1. The protein-protein interaction network of the active ingredients of QSP in the treatment of NP featured 48 proteins, including DRD2, CHRM, beta2-adrenergic receptor, HTR2A, and calcitonin gene-related peptide. In total, 53 GO entries, including 35 biological process items, 7 molecular function items, and 11 cell related items, were identified. In addition, eight relevant (KEGG) pathways were identified, including calcium, neuroactive ligand-receptor interaction, and cAMP signaling pathways. Conclusion: Network pharmacology can help clarify the role and mechanism of QSP in the treatment of NP and provide a foundation for further research.