Abstract: Tremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

Abstract: Myasthenia gravis (MG) is an autoimmune disease and the most common type of neuromuscular disease. Genes and miRNAs associated with MG have been widely studied; however, the molecular mechanisms of transcription factors (TFs) and the relationship among them remain unclear. A TF-miRNA-gene network (TMGN) of MG was constructed by extracting six regulatory pairs (TF-miRNA, miRNA-gene, TF-gene, miRNA-TF, gene-gene and miRNA-miRNA). Then, 3/4/5-node regulatory motifs were detected in the TMGN. Then, the motifs with the highest Z-score, occurring as 3/4/5-node composite feed-forward loops (FFLs), were selected as statistically significant motifs. By merging these motifs together, we constructed a 3/4/5-node composite FFL motif-specific subnetwork (CFMSN). Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MG. In addition, the genes, TFs and miRNAs in the CFMSN were also utilized to identify potential drugs. Five related genes, 3 TFs and 13 miRNAs, were extracted from the CFMSN. As the most important TF in the CFMSN, MYC was inferred to play a critical role in MG. Pathway enrichment analysis showed that the genes and miRNAs in the CFMSN were mainly enriched in pathways related to cancer and infections. Furthermore, 21 drugs were identified through the CFMSN, of which estradiol, estramustine, raloxifene and tamoxifen have the potential to be novel drugs to treat MG. The present study provides MG-related TFs by constructing the CFMSN for further experimental studies and provides a novel perspective for new biomarkers and potential drugs for MG.

Abstract: With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz. Herein, the cheminformatics is used to recognize the topological structure of molecules, the ML models are used to build the relationships between topology and computational cost, and the MWI framework is used to hold various combinations of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results of molecules from the DrugBank dataset show that (1) it can give quantitative predictions of computational costs, typical mean relative errors can be less than 0.2 for DFT/TDDFT calculations with derivations of +/-25% using the exactly pretrained ML models and (2) it can also be employed to various combinations of DFT functional and basis set cases without exactly pretrained ML models, while only slightly enlarge predicting errors.

Abstract: Simotang oral liquid (SMT), a well-known traditional Chinese medicine formula composed of four medicinal and edible plants, has been extensively used for treating gastrointestinal disorders (GIDs) since ancient times. However, the major active constituents and the underlying molecular mechanism of SMT on GIDs are still partially understood. Herein, the preliminary chemical profile of SMT was first identified by ultrahigh-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap). In total, 70 components were identified. Then, a network pharmacology approach integrating target prediction, pathway enrichment analysis, and network construction was adopted to explore the therapeutic mechanism of SMT. As a result, 170 main targets were screened out and considered as effective players in ameliorating GIDs. More importantly, the major hubs were found to be highly enriched in a calcium signaling pathway. Furthermore, 26 core SMT-related genes were identified, which may play key roles in ameliorating gastrointestinal motility. In conclusion, this work would provide valuable information for further development and clinical application of SMT.

Abstract: The recent advances of novel methodologies such as non-targeted and suspect screening based on high-resolution mass spectrometry (HRMS) have paved the way to a new paradigm for exposure assessment. These methodologies allow to profile simultaneously thousands of small unknown molecules present in environmental and biological samples, and therefore hold great promises in order to identify more efficiently hazardous contaminants potentially associated with increased risks of developing adverse health outcomes. In order to further explore the potential of these methodologies and push the transition from research applications towards regulatory purposes, robust harmonized quality standards have to be implemented. Here, we discuss the feasibility of using ISO/IEC 17025: 2017 as a guideline to implement non-targeted and suspect screening methodologies in laboratories, whether it is for accreditation purposes or not. More specifically, we identified and then discussed how specificities of non-targeted HRMS methodology can be accounted for in order to comply with the specific items of ISO/IEC 17025: 2017. We also discussed other specificities of HRMS methodologies (e.g., need for digital storage capacity) that are so far not included in the ISO/IEC 17025 requirements but should be considered. This works aims to fuel and expand the discussion in order to subsidize new opportunities of harmonization for non-targeted and suspect screening.

Abstract: Natural products are an invaluable source of molecules with a large variety of biological activities. Interest in natural products in drug discovery is documented in an increasing number of publications of bioactive secondary metabolites. Among those, medicinal plants are one of the most studied for this endeavor. An ever thriving area of opportunity within the field concerns the discovery of antidiabetic natural products. As a result, a vast amount of secondary metabolites are isolated from medicinal plants used against diabetes mellitus but whose information has not been organized systematically yet. Several research articles enumerate antidiabetic compounds, but the lack of a chemical database for antidiabetic metabolites limits their application in drug development. In this work, we present DiaNat-DB, a comprehensive collection of 336 molecules from medicinal plants reported to have in vitro or in vivo antidiabetic activity. We also discuss a chemoinformatic analysis of DiaNat-DB to compare antidiabetic drugs and natural product databases. To further explore the antidiabetic chemical space based on DiaNat compounds, we searched for analogs in ZINC15, an extensive database listing commercially available compounds. This work will help future analyses, design, and development of new antidiabetic drugs. DiaNat-DB and its ZINC15 analogs are freely available at http://rdu.iquimica.unam.mx/handle/20.500.12214/1186.

Abstract: BACKGROUND: Solanum nigrum L. decoction has been used as a folklore medicine in China to prevent the postoperative recurrence of bladder cancer (BC). However, there are no previous pharmacological studies on the protective mechanisms of this activity of the plant. Thus, this study aimed to perform a systematic analysis and to predict the potential action mechanisms underlying S. nigrum activity in BC based on network pharmacology. METHODS: Based on network pharmacology, the active ingredients of S. nigrum and the corresponding targets were identified using the Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform database, and BC-related genes were screened using GeneCards and the Online Mendelian Inheritance in Man database. In addition, ingredient-target (I-T) and protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted, and then the pathways directly related to BC were integrated manually to reveal the pharmacological mechanism underlying S. nigrum-medicated therapeutic effects in BC. RESULTS: Seven active herbal ingredients from 39 components of S. nigrum were identified, which shared 77 common target genes related to BC. I-T network analysis revealed that quercetin was associated with all targets and that NCOA2 was targeted by four ingredients. Besides, interleukin 6 had the highest degree value in the PPI network, indicating a hub role. A subsequent gene enrichment analysis yielded 86 significant GO terms and 89 significant pathways, implying that S. nigrum had therapeutic benefits in BC through multi-pathway effects, including the HIF-1, TNF, P53, MAPK, PI3K/Akt, apoptosis and bladder cancer pathway. CONCLUSIONS: S. nigrum may mediate pharmacological effects in BC through multi-target and various signaling pathways. Further validation is required experimentally. Network pharmacology approach provides a predicative novel strategy to reveal the holistic mechanism of action of herbs.

Abstract: A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.

Abstract: RNA-dependent RNA polymerase (RdRp), also called nsp12, is considered a promising but challenging drug target for inhibiting replication and hence, the growth of various RNA-viruses. In this report, a computational study is performed to offer insights on the binding of Remdesivir and Galidesivir with SARS-CoV2 RdRp with natural substrate, ATP, as the control. It was observed that Remdesivir and Galidesivir exhibited similar binding energies for their best docked poses, -6.6 kcal/mole and -6.2 kcal/mole, respectively. ATP also displayed comparative and strong binding free energy of -6.3 kcal/mole in the catalytic site of RdRp. However, their binding locations within the active site are distinct. Further, the interaction of catalytic site residues (Asp760, Asp761, and Asp618) with Remdesivir and Galidesivir is comprehensively examined. Conformational changes of RdRp and bound molecules are demonstrated using 100 ns explicit solvent simulation of the protein-ligand complex. Simulation suggests that Galidesivir binds at the non-catalytic location and its binding strength is relatively weaker than ATP and Remdesivir. Remdesivir also binds at the catalytic site and showed high potency to inhibit the function of RdRp. Binding of co-factor units nsp7 and nsp8 with RdRp (nsp12) complexed with Remdesivir and Galidesivir was also examined. MMPBSA binding energy for all three complexes has been computed across the 100 ns simulation trajectory. Overall, this study suggests, Remdesivir has anti-RdRp activity via binding at a catalytic site. In contrast, Galidesivir may not have direct anti-RdRp activity but it can induce a conformational change in the RNA polymerase.

Abstract: BACKGROUND: Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. RESULTS: In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ([Formula: see text] = 0.867, and [Formula: see text]=0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework's performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with [Formula: see text] ranging from 0.879 to 0.931 and [Formula: see text] from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. CONCLUSION: In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php .