Abstract: BACKGROUND: Major differences exist between men and women in both physiology and pathophysiology. Dissecting the underlying processes and contributing mechanisms of sex differences in health and disease represents a crucial step towards precision medicine. Considering the significant differences between men and women in the response to pharmacotherapies, our aim was to develop an in silico model able to predict sex-specific drug responses in a large-scale. METHODS: For this purpose, we focused on cardiovascular effects because of their high morbidity and mortality. Our model predicted several drugs (including acebutolol and tacrine) with significant differences in the heart between men and women. To validate the sex-specific drug responses identified by our model, acebutolol was selected to lower blood pressure in spontaneous hypertensive rats (SHR), tacrine was used to assess cardiac injury in mice and metformin as control for a non-sex-specific response. RESULTS: As our model predicted, acebutolol exhibited a stronger decrease in heart rate and blood pressure in female than male SHRs. Tacrine lowered heart rate in male but not in female mice, induced higher plasma cTNI level and increased cardiac superoxide (DHE staining) generation in female than male mice, indicating stronger cardiac toxicity in female than male mice. To validate our model in humans, we employed two Chinese cohorts, which showed that among patients taking a beta-receptor blocker (metoprolol), women reached significantly lower diastolic blood pressure than men. CONCLUSIONS: We conclude that our in silico model could be translated into clinical practice to predict sex-specific drug responses, thereby contributing towards a more appropriate medical care for both men and women.

Abstract: BACKGROUND Osteosarcoma (OS) is a highly complicated bone cancer involving imbalance of signaling transduction networks in cells. Development of new anti-osteosarcoma drugs is very challenging, mainly due to lack of known key targets. MATERIAL AND METHODS In this study, we attempted to reveal more promising targets for drug design by "Target-Pathway" network analysis, providing the new therapeutic strategy of osteosarcoma. The potential targets used for the treatment of OS were selected from 4 different sources: DrugBank, TCRD database, dbDEMC database, and recent scientific literature papers. Cytoscape was used for the establishment of the "Target-Pathway" network. RESULTS The obtained results suggest that tankyrase 2 (TNKS2) might be a very good potential protein target for the treatment of osteosarcoma. An in vitro MTT assay proved that it is an available option against OS by targeting the TNKS2 protein. Subsequently, cell cycle and apoptosis assay by flow cytometry showed the TNKS2 inhibitor can obviously induce cell cycle arrest, apoptosis, and mitotic cell death. CONCLUSIONS Tankyrase 2 (TNKS2), a member of the multifunctional poly(ADP-ribose) polymerases (PARPs), could be a very useful protein target for the treatment of osteosarcoma.

Abstract: Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing 'hits' with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug-Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure-activity relationship, and lead compound identification studies.

Abstract: Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

Abstract: The worldwide prevalence of infections caused by antibiotic-resistant Gram-negative bacteria poses a serious threat to public health due to the limited therapeutic alternatives. Cationic peptides represent a large family of antibiotics and have attracted interest due to their diverse chemical structures and potential for combating drug-resistant Gram-negative pathogens. Here, we analyze 7395 bacterial genomes to investigate their capacity for biosynthesis of cationic nonribosomal peptides with activity against Gram-negative bacteria. Applying this approach, we identify two novel compounds (brevicidine and laterocidine) showing bactericidal activities against antibiotic-resistant Gram-negative pathogens, such as Pseudomonas aeruginosa and colistin-resistant Escherichia coli, and an apparently low risk of resistance. The two peptides show efficacy against E. coli in a mouse thigh infection model. These findings may contribute to the discovery and development of Gram-negative antibiotics.

Abstract: Qingdai, a traditional Chinese medicine (TCM) used for the treatment of chronic myeloid leukemia (CML) with good efficacy, has been used in China for decades. However, due to the complexity of traditional Chinese medicinal compounds, the pharmacological mechanism of Qingdai needs further research. In this study, we investigated the pharmacological mechanisms of Qingdai in the treatment of CML using network pharmacology approaches. First, components in Qingdai that were selected by pharmacokinetic profiles and biological activity predicted putative targets based on a combination of 2D and 3D similarity measures with known ligands. Then, an interaction network of Qingdai putative targets and known therapeutic targets for the treatment of chronic myeloid leukemia was constructed. By calculating the 4 topological features (degree, betweenness, closeness, and coreness) of each node in the network, we identified the candidate Qingdai targets according to their network topological importance. The composite compounds of Qingdai and the corresponding candidate major targets were further validated by a molecular docking simulation. Seven components in Qingdai were selected and 32 candidate Qingdai targets were identified; these were more frequently involved in cytokine-cytokine receptor interaction, cell cycle, p53 signaling pathway, MAPK signaling pathway, and immune system-related pathways, which all play important roles in the progression of CML. Finally, the molecular docking simulation showed that 23 pairs of chemical components and candidate Qingdai targets had effective binding. This network-based pharmacology study suggests that Qingdai acts through the regulation of candidate targets to interfere with CML and thus regulates the occurrence and development of CML.

Abstract: Motivation: Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level. Results: We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis, non-small cell lung cancer, prostate cancer and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease. Availability and implementation: The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract: Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.

Abstract: Pharmacovigilance (PV) databases record the benefits and risks of different drugs, as a means to ensure their safe and effective use. Creating and maintaining such resources can be complex, since a particular medication may have divergent effects in different individuals, due to specific patient characteristics and/or interactions with other drugs being administered. Textual information from various sources can provide important evidence to curators of PV databases about the usage and effects of drug targets in different medical subjects. However, the efficient identification of relevant evidence can be challenging, due to the increasing volume of textual data. Text mining (TM) techniques can support curators by automatically detecting complex information, such as interactions between drugs, diseases and adverse effects. This semantic information supports the quick identification of documents containing information of interest (e.g., the different types of patients in which a given adverse drug reaction has been observed to occur). TM tools are typically adapted to different domains by applying machine learning methods to corpora that are manually labelled by domain experts using annotation guidelines to ensure consistency. We present a semantically annotated corpus of 597 MEDLINE abstracts, PHAEDRA, encoding rich information on drug effects and their interactions, whose quality is assured through the use of detailed annotation guidelines and the demonstration of high levels of inter-annotator agreement (e.g., 92.6% F-Score for identifying named entities and 78.4% F-Score for identifying complex events, when relaxed matching criteria are applied). To our knowledge, the corpus is unique in the domain of PV, according to the level of detail of its annotations. To illustrate the utility of the corpus, we have trained TM tools based on its rich labels to recognise drug effects in text automatically. The corpus and annotation guidelines are available at: http://www.nactem.ac.uk/PHAEDRA/ .

Abstract: There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can only be revealed after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which may be associated with potential side effects of these therapeutics. The current approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by mRNA, cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.