Application of Atlas of Cancer Signalling Network in preclinical studies.

Brief Bioinform. 2018 May 03;:

Authors: Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, Kuperstein I

Abstract
Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases.

PMID: 29726961 [PubMed - as supplied by publisher]

PregOMICS-Leveraging systems biology and bioinformatics for drug repurposing in maternal-child health.

Am J Reprod Immunol. 2018 May 04;:e12971

Authors: Goldstein JA, Bastarache LA, Denny JC, Pulley JM, Aronoff DM

Abstract
Obstetric diseases remain underserved and understudied. Drug repurposing-utilization of a drug whose use is accepted in one condition for a different condition-could represent a rapid and low-cost way to identify new therapies that are known to be safe. In diseases of pregnancy, the known safety profile is a strong additional incentive. We describe the techniques and steps used in the use of 'omics data for drug repurposing. We illustrate these techniques using case studies of published drug repurposing projects. We provide a set of available databases with low barriers to entry which investigators can use to perform their own projects. The promise of 'omics techniques is unbiased screening, either of all drug targets or of all patients using particular drugs to find which are likely to alter disease risk or progression. However, we caution that reproducibility across the underlying studies, and thus the drugs suggested for repurposing, can be poor. We suggest that improved nosology, for example correlating patient clinical conditions with placental pathology, could yield more robust associations. We conclude that 'omics-driven drug repurposing represents a potential fruitful path to discover new, safe treatments of obstetric diseases.

PMID: 29726581 [PubMed - as supplied by publisher]

The Mu.Ta.Lig. Chemotheca: A Community-Populated Molecular Database for Multi-Target Ligands Identification and Compound-Repurposing.

Front Chem. 2018;6:130

Authors: Ortuso F, Bagetta D, Maruca A, Talarico C, Bolognesi ML, Haider N, Borges F, Bryant S, Langer T, Senderowitz H, Alcaro S

Abstract
For every lead compound developed in medicinal chemistry research, numerous other inactive or less active candidates are synthetized/isolated and tested. The majority of these compounds will not be selected for further development due to a sub-optimal pharmacological profile. However, some poorly active or even inactive compounds could live a second life if tested against other targets. Thus, new therapeutic opportunities could emerge and synergistic activities could be identified and exploited for existing compounds by sharing information between researchers who are working on different targets. The Mu.Ta.Lig (Multi-Target Ligand) Chemotheca database aims to offer such opportunities by facilitating information exchange among researchers worldwide. After a preliminary registration, users can (a) virtually upload structures and activity data for their compounds with corresponding, and eventually known activity data, and (b) search for other available compounds uploaded by the users community. Each piece of information about given compounds is owned by the user who initially uploaded it and multiple ownership is possible (this occurs if different users uploaded the same compounds or information pertaining to the same compounds). A web-based graphical user interface has been developed to assist compound uploading, compounds searching and data retrieval. Physico-chemical and ADME properties as well as substructure-based PAINS evaluations are computed on the fly for each uploaded compound. Samples of compounds that match a set of search criteria and additional data on these compounds could be requested directly from their owners with no mediation by the Mu.Ta.Lig Chemotheca team. Guest access provides a simplified search interface to retrieve only basic information such as compound IDs and related 2D or 3D chemical structures. Moreover, some compounds can be hidden to Guest users according to an owner's decision. In contrast, registered users have full access to all of the Chemotheca data including the permission to upload new compounds and/or update experimental/theoretical data (e.g., activities against new targets tested) related to already stored compounds. In order to facilitate scientific collaborations, all available data are connected to the corresponding owner's email address (available for registered users only). The Chemotheca web site is accessible at http://chemotheca.unicz.it.

PMID: 29725591 [PubMed]

Orlistat as a FASN inhibitor and multitargeted agent for cancer therapy.

Expert Opin Investig Drugs. 2018 May 03;:

Authors: Schcolnik-Cabrera A, Chávez-Blanco A, Domínguez-Gómez G, Taja-Chayeb L, Cardenas-Barcenas R, Trejo-Becerril C, Perez-Cardenas E, Gonzalez-Fierro A, Dueñas-González A

Abstract
INTRODUCTION: Cancer cells have increased glycolysis and glutaminolysis. Their third feature is increased de novo lipogenesis. As such, fatty acid (FA) synthesis enzymes are over-expressed in cancer and their depletion causes antitumor effects. As fatty acid synthase (FASN) plays a pivotal role in this process, it is an attractive target for cancer therapy. Areas covered: This is a review of the lipogenic phenotype of cancer and how this phenomenon can be exploited for cancer therapy using inhibitors of FASN, with particular emphasis on orlistat as a repurposing drug. Expert opinion: Disease stabilization only has been observed with a highly selective FASN inhibitor used as a single agent in clinical trials. It is too early to say whether the absence of tumor responses other than stabilization results because even full inhibition of FASN is not enough to elicit antitumor responses. The FASN inhibitor orlistat is a "dirty" drug with target-off actions upon at least seven targets with a proven role in tumor biology. The development of orlistat formulations suited for its intravenous administration is a step ahead to shed light on the concept that drug promiscuity can or not be a virtue.

PMID: 29723075 [PubMed - as supplied by publisher]

Repurposing the Selective Oestrogen Receptor Modulator Tamoxifen for the Treatment of Duchenne Muscular Dystrophy.

Chimia (Aarau). 2018 Apr 25;72(4):238-240

Authors: Gayi E, Neff LA, Ismail HM, Ruegg UT, Scapozza L, Dorchies OM

Abstract
Drug discovery is a long, expensive and risky process. Evaluating drugs that have already been proved safe for use in humans and testing them for a new indication greatly reduces the time and monetary costs involved in finding treatments for life-threatening conditions. Here tamoxifen, a drug that is used for the treatment of breast cancer, is investigated in a mouse model of Duchenne muscular dystrophy. Tamoxifen was efficacious in countering the symptoms of the disease without affecting the underlying genetic cause. Based on these results, tamoxifen has been tested in other forms of muscle disease with success. Drug repurposing may not only be a cost-effective manner for treating a variety of diseases, it may also help us uncover common mechanisms between conditions that were previously thought to be unrelated.

PMID: 29720316 [PubMed - in process]

Looking Back, Looking Forward at Halogen Bonding in Drug Discovery.

Molecules. 2017 Aug 24;22(9):

Authors: Mendez L, Henriquez G, Sirimulla S, Narayan M

Abstract
Halogen bonding has emerged at the forefront of advances in improving ligand: receptor interactions. In particular the newfound ability of this extant non-covalent-bonding phenomena has revolutionized computational approaches to drug discovery while simultaneously reenergizing synthetic approaches to the field. Here we survey, via examples of classical applications involving halogen atoms in pharmaceutical compounds and their biological hosts, the unique advantages that halogen atoms offer as both Lewis acids and Lewis bases.

PMID: 28837116 [PubMed - indexed for MEDLINE]

Virtual screening and repositioning of inconclusive molecules of beta-lactamase Bioassays-A data mining approach.

Comput Biol Chem. 2017 Oct;70:65-88

Authors: Gad A, Manuel AT, K R J, John L, R S, V G SP, U C AJ

Abstract
This study focuses on the best possible way forward in utilizing inconclusive molecules of PubChem bioassays AID 1332, AID 434987 and AID 434955, which are related to beta-lactamase inhibitors of Mycobacterium tuberculosis (Mtb). The inadequacy in the experimental methods that were observed during the invitro screening resulted in an inconclusive dataset. This could be due to certain moieties present within the molecules. In order to reconsider such molecules, insilico methods can be suggested in place of invitro methods For instance, datamining and medicinal chemistry methods: have been adopted to prioritise the inconclusive dataset into active or inactive molecules. These include the Random Forest algorithm for dataminning, Lilly MedChem rules for virtually screening out the promiscuity, and Self Organizing Maps (SOM) for clustering the active molecules and enlisting them for repositioning through the use of artificial neural networks. These repositioned molecules could then be prioritized for downstream drug discovery analysis.

PMID: 28822333 [PubMed - indexed for MEDLINE]

Repurposing HAMI3379 to Block GPR17 and Promote Rodent and Human Oligodendrocyte Differentiation.

Cell Chem Biol. 2018 Apr 18;:

Authors: Merten N, Fischer J, Simon K, Zhang L, Schröder R, Peters L, Letombe AG, Hennen S, Schrage R, Bödefeld T, Vermeiren C, Gillard M, Mohr K, Lu QR, Brüstle O, Gomeza J, Kostenis E

Abstract
Identification of additional uses for existing drugs is a hot topic in drug discovery and a viable alternative to de novo drug development. HAMI3379 is known as an antagonist of the cysteinyl-leukotriene CysLT2 receptor, and was initially developed to treat cardiovascular and inflammatory disorders. In our study we identified HAMI3379 as an antagonist of the orphan G protein-coupled receptor GPR17. HAMI3379 inhibits signaling of recombinant human, rat, and mouse GPR17 across various cellular backgrounds, and of endogenous GPR17 in primary rodent oligodendrocytes. GPR17 blockade by HAMI3379 enhanced maturation of primary rat and mouse oligodendrocytes, but was without effect in oligodendrocytes from GPR17 knockout mice. In human oligodendrocytes prepared from inducible pluripotent stem cells, GPR17 is expressed and its activation impaired oligodendrocyte differentiation. HAMI3379, conversely, efficiently favored human oligodendrocyte differentiation. We propose that HAMI3379 holds promise for pharmacological exploitation of orphan GPR17 to enhance regenerative strategies for the promotion of remyelination in patients.

PMID: 29706593 [PubMed - as supplied by publisher]

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.

Acta Biotheor. 2018 Apr 26;:

Authors: Le DH, Nguyen-Ngoc D

Abstract
Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.

PMID: 29700660 [PubMed - as supplied by publisher]

IMPACT web portal: oncology database integrating molecular profiles with actionable therapeutics.

BMC Med Genomics. 2018 Apr 20;11(Suppl 2):26

Authors: Hintzsche JD, Yoo M, Kim J, Amato CM, Robinson WA, Tan AC

Abstract
BACKGROUND: With the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs.
RESULTS: IMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes.
CONCLUSION: IMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT .

PMID: 29697364 [PubMed - in process]

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

Bioinformatics. 2018 Apr 24;:

Authors: Zhou X, Wang M, Katsyv I, Irie H, Zhang B

Abstract
Motivation: Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods.
Results: We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs.
Availability: The EMUDRA R package is available at doi:10.7303/syn11510888.
Contact: bin.zhang@mssm.edu or zhangb@hotmail.com.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 29688306 [PubMed - as supplied by publisher]

Community-driven roadmap for integrated disease maps.

Brief Bioinform. 2018 Apr 23;:

Authors: Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R

Abstract
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

PMID: 29688273 [PubMed - as supplied by publisher]

DR2DI: a powerful computational tool for predicting novel drug-disease associations.

J Comput Aided Mol Des. 2018 Apr 23;:

Authors: Lu L, Yu H

Abstract
Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI .

PMID: 29687309 [PubMed - as supplied by publisher]

Hyphenated 3D-QSAR statistical model-drug repurposing analysis for the identification of potent neuraminidase inhibitor.

Cell Biochem Biophys. 2018 Apr 23;:

Authors: Rohini K, Shanthi V

Abstract
The Influenza A virus is one of the principle causes of respiratory illness in human. The surface glycoprotein of the influenza virus, neuraminidase (NA), has a vital role in the release of new viral particle and spreads infection in the respiratory tract. It has been long recognized as a valid drug target for influenza A virus infection. Oseltamivir is used as a standard drug of choice for the treatment of influenza. However, the emergence of mutants with novel mutations has increased the resistance to potent NA inhibitor. In the present investigation, we have employed computer-assisted combinatorial techniques in the screening of 8621 molecules from Drug Bank to find potent NA inhibitors. A three-dimensional pharmacophore model was generated from the previously reported 28 carbocylic influenza NA inhibitors along with oseltamivir using PHASE module of Schrödinger Suite. The model generated consists of one hydrogen bond acceptor (A), one hydrogen bond donors (D), one hydrophobic group (H), and one positively charged group (P), ADHP. The hypothesis was further validated for its integrity and significance using enrichment analysis. Subsequently, an atom-based 3D-QSAR model was built using the common pharmacophore hypothesis (CPH). The developed 3D-QSAR model was found to be statistically significant with R2 value of 0.9866 and Q2 value of 0.7629. Further screening was accomplished using three-stage docking process using the Glide algorithm. The resultant lead molecules were examined for its drug-like properties using the Qikprop algorithm. Finally, the calculated pIC50 values of the lead compounds were validated by the AutoQSAR algorithm. Overall, the results from our analysis highlights that lisinopril (DB00722) is predicted to bind better with NA than currently approved drug. In addition, it has the best match in binding geometry conformations with the existing NA inhibitor. Note that the antiviral activity of lisinopril is reported in the literature. However, our paper is the first report on lisinopril activity against influenza A virus infection. These results are envisioned to help design the novel NA inhibitors with an increased antiviral efficacy.

PMID: 29687225 [PubMed - as supplied by publisher]

Drug Repositioning to Alleviate Systemic Inflammatory Response Syndrome Caused by Gram-Negative Bacterial Outer Membrane Vesicles.

Adv Healthc Mater. 2018 Apr 23;:e1701476

Authors: Kim JH, Lee J, Park KS, Hong SW, Gho YS

Abstract
Sepsis is characterized by systemic inflammatory response syndrome (SIRS) accompanied with infection. Gram-negative bacteria can evoke sepsis by activating the host immune system, such as the release of IL-6 and TNF-α, through their virulence factors. Outer membrane vesicles (OMVs), nanosized bilayered proteolipids derived from Gram-negative bacteria, harbor various virulence factors and are shown to induce SIRS. Here, drugs are repositioned to alleviate SIRS caused by Gram-negative bacterial OMVs. Using novel OMV-based drug screening systems, a total of 178 commercially available drugs are primarily screened, and a total of 18 repositioned drug candidates are found to effectively block IL-6 and TNF-α production from OMV-stimulated macrophages. After excluding the compounds which are previously known to intervene sepsis or which show cytotoxicity to macrophages, the compounds which show dose-dependency in inhibiting the release of IL-6 and TNF-α by the OMV-stimulated macrophages in vitro and which reduce OMV-induced SIRS in vivo are selected. Salbutamol, a β2 adrenergic receptor agonist, is selected as a novel candidate to alleviate OMV-induced SIRS. This study sheds light on using Gram-negative bacterial OMVs in exploring novel candidate compounds to alleviate inflammatory diseases including sepsis.

PMID: 29683274 [PubMed - as supplied by publisher]

The polypharmacology of natural products.

Future Med Chem. 2018 Apr 20;:

Authors: Ho TT, Tran QT, Chai CL

Abstract
The once-popular approach of using natural products as a prime source for medicinal chemistry and drug discovery has waned considerably in the past two decades due to the advent of high-throughput screening of small molecule mega libraries. However, the growing appreciation of network pharmacology as the next drug-discovery paradigm suggests that natural products and their unique polypharmacology offer significant advantages for finding novel therapeutics particularly for the treatment of complex and multifactorial diseases. Drug discovery process is awaiting the revitalization of interest in natural products and their derivatives. The current challenge is how to decipher this natural chemical diversity.

PMID: 29673257 [PubMed - as supplied by publisher]

Tumor progression: the neuronal input.

Ann Transl Med. 2018 Mar;6(5):89

Authors: Arese M, Bussolino F, Pergolizzi M, Bizzozero L, Pascal D

Abstract
One of the challenges of cancer is its heterogeneity and rapid capacity to adapt. Notwithstanding significant progress in the last decades in genomics and precision medicine, new molecular targets and therapies appear highly necessary. One way to approach this complex problem is to consider cancer in the context of its cellular and molecular microenvironment, which includes nerves. The peripheral nerves, the topic of this review, modulate the biological behavior of the cancer cells and influence tumor progression, including the events related to the metastatic spread of the disease. This mechanism involves the release of neurotransmitters directly into the microenvironment and the activation of the corresponding membrane receptors. While this fact appears to complicate further the molecular landscape of cancer, the neurotransmitters are highly investigated molecules, and often are already targeted by well-developed drugs, a fact that can help finding new therapies at a fraction of the cost and time needed for new medicines (through the so-called drug repurposing). Moreover, the modulation of tumor progression by neurotransmitters can probably explain the long-recognized effects of psychological factors on the burden of cancer. We begin with an introduction on the tumor-nervous-connections and a description of the perineural invasion and neoneurogenesis, the two most important interaction patterns of cancer and nerves. Next, we discuss the most recent data that unequivocally demonstrate the necessity of the nervous system for tumor onset and growth. We introduce the molecular players of the tumor-nervous-connections by citing the role of three main families: neurotropic factors, axon guidance molecules, and neurotransmitters. Finally, we review the role the most important neurotransmitters in tumor biology and we conclude by analyzing the significance of the presented data for cancer therapy, with all the potential advantages and caveats.

PMID: 29666812 [PubMed]

Identification of circadian clock modulators from existing drugs.

EMBO Mol Med. 2018 Apr 17;:

Authors: Tamai TK, Nakane Y, Ota W, Kobayashi A, Ishiguro M, Kadofusa N, Ikegami K, Yagita K, Shigeyoshi Y, Sudo M, Nishiwaki-Ohkawa T, Sato A, Yoshimura T

Abstract
Chronic circadian disruption due to shift work or frequent travel across time zones leads to jet-lag and an increased risk of diabetes, cardiovascular disease, and cancer. The development of new pharmaceuticals to treat circadian disorders, however, is costly and hugely time-consuming. We therefore performed a high-throughput chemical screen of existing drugs for circadian clock modulators in human U2OS cells, with the aim of repurposing known bioactive compounds. Approximately 5% of the drugs screened altered circadian period, including the period-shortening compound dehydroepiandrosterone (DHEA; also known as prasterone). DHEA is one of the most abundant circulating steroid hormones in humans and is available as a dietary supplement in the USA Dietary administration of DHEA to mice shortened free-running circadian period and accelerated re-entrainment to advanced light-dark (LD) cycles, thereby reducing jet-lag. Our drug screen also revealed the involvement of tyrosine kinases, ABL1 and ABL2, and the BCR serine/threonine kinase in regulating circadian period. Thus, drug repurposing is a useful approach to identify new circadian clock modulators and potential therapies for circadian disorders.

PMID: 29666146 [PubMed - as supplied by publisher]

Antiviral activity of gemcitabine against human rhinovirus in vitro and in vivo.

Antiviral Res. 2017 Sep;145:6-13

Authors: Song JH, Kim SR, Heo EY, Lee JY, Kim DE, Cho S, Chang SY, Yoon BI, Seong J, Ko HJ

Abstract
Rhinovirus, a major causative agent of the common cold, is associated with exacerbation of asthma and chronic obstructive pulmonary disease. Currently, there is no antiviral treatment or vaccine for human rhinovirus (HRV). Gemcitabine (2',2'-difluorodeoxycytidine, dFdC) is a deoxycytidine analog with antiviral activity against rhinovirus, as well as enterovirus 71, in vitro. However, the antiviral effects of gemcitabine in vivo have not been investigated. In the current study, we assessed whether gemcitabine mediated antiviral effects in the murine HRV infection model. Intranasal administration of gemcitabine significantly lowered pulmonary viral load and inflammation by decreasing proinflammatory cytokines, including TNF-α and IL-1β, and reduction in the number of lung-infiltrating lymphocytes. Interestingly, we found that the addition of UTP and CTP significantly attenuated the antiviral activity of gemcitabine. Thus the limitation of UTP and CTP by the addition of gemcitabine may inhibit the viral RNA synthesis. These results suggest that gemcitabine, an antineoplastic drug, can be repositioned as an antiviral drug to inhibit HRV infection.

PMID: 28705625 [PubMed - indexed for MEDLINE]

Triclosan is an aminoglycoside adjuvant for the eradication of Pseudomonas aeruginosa biofilms.

Antimicrob Agents Chemother. 2018 Apr 16;:

Authors: Maiden MM, Hunt AMA, Zachos MP, Gibson JA, Hurwitz ME, Mulks MH, Waters CM

Abstract
One of the most important clinical obstacles in cystic fibrosis (CF) is antibiotic treatment failure due to biofilms produced by Pseudomonas aeruginosa The ability of this pathogen to survive eradication by tobramycin and pathoadapt into a hyper-biofilm state leading to chronic infections is key to its success. Retrospective studies have demonstrated that preventing this pathoadaptation by improving eradication is essential to extend the lives of CF patients. To identify adjuvants that enhance tobramycin eradication of P. aeruginosa, we performed a high-throughput screen of 6,080 compounds from four drug repurposing libraries. We identified that the Food and Drug Administration (FDA) approved compound, triclosan, combined with tobramycin resulted in a 100-fold reduction of viable cells within biofilms at six hours, but neither compound alone had significant antimicrobial activity against biofilms. This synergistic treatment significantly accelerated killing of biofilms compared to tobramycin treatment alone, and the combination was effective against 6/7 CF clinical isolates compared to tobramycin treatment alone including a tobramycin resistant strain. Further, triclosan and tobramycin killed persister cells, causing a 100-fold reduction by 8-hrs and complete eradication by 24-hrs. Triclosan also enhances tobramycin killing of multiple Burkholderia cenocepacia and Staphylococcus aureus clinical isolates grown as biofilms. Additionally, triclosan synergized with other aminoglycosides such as gentamicin or streptomycin. Triclosan is a well-tolerated aminoglycoside adjuvant shown to be safe for human use that could improve treatment of biofilm-based infections.

PMID: 29661867 [PubMed - as supplied by publisher]