Abstract: A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug for which a small number of side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We show that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.

Abstract: Last two decades have witnessed several global infectious outbreaks. Among these, coronavirus is identified as a prime culprit ranging from its involvement in severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) to COVID-19. These infections involved in huge healthcare and economic cost incurred globally. Every time, coronavirus improved its infection ability and surprised the medical practitioners and researchers. Currently, COVID-19 is also causing numerous infections and stalled global activities. Global efforts are underway to identify potential viral targets for management of these outbreaks, but significant progress in prevention of these outbreaks is not yet achieved. We explored host-pathogen protein-protein interactions of MERS, SARS and COVID-19, and identified host targets common among all recent coronavirus outbreaks. Further, we tried to understand their potential for management of coronavirus. The common proteins involved in coronavirus host-pathogen interactions indicate their indispensable role in the pathogenesis and therefore targeting these proteins can give strategies to prevent current and future coronavirus outbreaks. Viral variability necessitates development of new therapeutic modalities for every outbreak, in contrast targeting necessary human proteins required by all coronaviruses can provide us a clue to prevent current and future coronavirus outbreaks. We found that targeting FURIN and TMPRSS2 can provide good results due to their common involvement in current and previous outbreaks. We also listed some known molecules against these two targets for their potential drug repurposing evaluation. Although, several recent studies undergoing with targeting these proteins for management of coronavirus, but safety evaluation and risk assessment must be given prime importance while targeting human proteins.

Abstract: Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

Abstract: RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Abstract: Urgent treatments, in any modality, to fight SARS-CoV-2 infections are desired by society in general, by health professionals, by Estate-leaders and, mainly, by the scientific community, because one thing is certain amidst the numerous uncertainties regarding COVID-19: knowledge is the means to discover or to produce an effective treatment against this global disease. Scientists from several areas in the world are still committed to this mission, as shown by the accelerated scientific production in the first half of 2020 with over 25,000 published articles related to the new coronavirus. Three great lines of publications related to COVID-19 were identified for building this article: The first refers to knowledge production concerning the virus and pathophysiology of COVID-19; the second regards efforts to produce vaccines against SARS-CoV-2 at a speed without precedent in the history of science; the third comprehends the attempts to find a marketed drug that can be used to treat COVID-19 by drug repurposing. In this review, the drugs that have been repurposed so far are grouped according to their chemical class. Their structures will be presented to provide better understanding of their structural similarities and possible correlations with mechanisms of actions. This can help identifying anti-SARS-CoV-2 promising therapeutic agents.

Abstract: BACKGROUND: The traditional Chinese medicine (TCM) formula Qing-Fei-Pai-Du decoction (QFPDD) was the most widely used prescription in China's campaign to contain COVID-19, which has exhibited positive effects. However, the underlying mode of action is largely unknown. PURPOSE: A systems pharmacology strategy was proposed to investigate the mechanisms of QFPDD against COVID-19 from molecule, pathway and network levels. STUDY DESIGN AND METHODS: The systems pharmacological approach consisted of text mining, target prediction, data integration, network study, bioinformatics analysis, molecular docking, and pharmacological validation. Especially, we proposed a scoring method to measure the confidence of targets identified by prediction and text mining, while a novel scheme was used to identify important targets from 4 aspects. RESULTS: 623 high-confidence targets of QFPDD's 12 active compounds were identified, 88 of which were overlapped with genes affected by SARS-CoV-2 infection. These targets were found to be involved in biological processes related with the development of COVID-19, such as pattern recognition receptor signaling, interleukin signaling, cell growth and death, hemostasis, and injuries of the nervous, sensory, circulatory, and digestive systems. Comprehensive network and pathway analysis were used to identify 55 important targets, which regulated 5 functional modules corresponding to QFPDD's effects in immune regulation, anti-infection, anti-inflammation, and multi-organ protection, respectively. Four compounds (baicalin, glycyrrhizic acid, hesperidin, and hyperoside) and 7 targets (AKT1, TNF-alpha, IL6, PTGS2, HMOX1, IL10, and TP53) were key molecules related to QFPDD's effects. Molecular docking verified that QFPDD's compounds may bind to 6 host proteins that interact with SARS-CoV-2 proteins, further supported the anti-virus effect of QFPDD. At last, in intro experiments validated QFPDD's important effects, including the inhibition of IL6, CCL2, TNF-alpha, NF-kappaB, PTGS1/2, CYP1A1, CYP3A4 activity, the up-regulation of IL10 expression, and repressing platelet aggregation. CONCLUSION: This work illustrated that QFPDD could exhibit immune regulation, anti-infection, anti-inflammation, and multi-organ protection. It may strengthen the understanding of QFPDD and facilitate more application of this formula in the campaign to SARS-CoV-2.

Abstract: To examine the molecular targets and therapeutic mechanism of a clinically proven Chinese medicinal pentaherbs formula (PHF) in atopic dermatitis (AD), we analyzed the active compounds and core targets, performed network and molecular docking analysis, and investigated interacting pathways. Information on compounds in PHF was obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and target prediction was performed using the Drugbank database. AD-related genes were gathered using the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Network analysis was performed by Cytoscape software and protein-protein interaction was analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources were applied for the enrichment analysis of the potential biological process and pathways associated with the intersection targets between PHF and AD. Autodock software was used to perform protein compound docking analysis. We identified 43 active compounds in PHF associated with 117 targets, and 57 active compounds associated with 107 targets that form the main pathways linked to oral and topical treatment of AD, respectively. Among them, quercetin, luteolin, and kaempferol are key chemicals targeting the core genes involved in the oral use of PHF against AD, while apigenin, ursolic acid, and rosmarinic acid could be used in topical treatment of PHF against AD. The compound-target-disease network constructed in the current study reveals close interactions between multiple components and multiple targets. Enrichment analysis further supports the biological processes and signaling pathways identified, indicating the involvement of IL-17 and tumor necrosis factor signaling pathways in the action of PHF on AD. Our data demonstrated the main compounds and potential pharmacological mechanisms of oral and topical application of PHF in AD.

Abstract: Ticks and tick-borne pathogens (TBPs) continuously causing substantial losses to the public and veterinary health sectors. The identification of putative drug targets and vaccine candidates is crucial to control TBPs. No information has been recorded on designing novel drug targets and vaccine candidates based on proteins. Subtractive proteomics is an in silico approach that utilizes extensive screening for the identification of novel drug targets or vaccine candidates based on the determination of potential target proteins available in a pathogen proteome that may be used effectively to control diseases caused by these infectious agents. The present study aimed to investigate novel drug targets and vaccine candidates by utilizing subtractive proteomics to scan the available proteomes of TBPs and predict essential and non-host homologous proteins required for the survival of these diseases causing agents. Subtractive proteome analysis revealed a list of fifteen essential, non-host homologous, and unique metabolic proteins in the complete proteome of selected pathogens. Among these therapeutic target proteins, three were excluded due to the presence in host gut metagenome, eleven were found to be highly potential drug targets, while only one was found as a potential vaccine candidate against TBPs. The present study may provide a foundation to design potential drug targets and vaccine candidates for the effective control of infections caused by TBPs.

Abstract: In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

Abstract: BACKGROUND: The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. METHODS: In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. RESULTS: In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. CONCLUSIONS: In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.