Update on drug-repurposing: is it useful for tackling antimicrobial resistance?

Future Microbiol. 2019 Aug 01;:

Authors: Kaul G, Shukla M, Dasgupta A, Chopra S

PMID: 31368794 [PubMed - as supplied by publisher]

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

J Med Chem. 2019 Jul 31;:

Authors: Li X, Li Z, Wu X, Xiong Z, Yang T, Fu Z, Liu X, Tan X, Zhong F, Wan X, Wang D, Ding X, Yang R, Hou H, Li C, Liu H, Chen K, Jiang H, Zheng M

Abstract
The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90, and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1,410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted "off-target" activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity, and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.

PMID: 31364850 [PubMed - as supplied by publisher]

The leukotriene signaling pathway: a druggable target in Alzheimer's disease.

Drug Discov Today. 2019 02;24(2):505-516

Authors: Michael J, Marschallinger J, Aigner L

Abstract
The underlying pathology of Alzheimer's disease (AD) is complex and includes, besides amyloid beta (Aβ) plaque depositions and neurofibrillary tangles, brain atrophy and neurodegeneration, neuroinflammation, impaired neurogenesis, vascular and blood-brain barrier (BBB) disruptions, neurotransmitter disbalances, and others. Here, we hypothesize that such complex pathologies can only be targeted efficiently through pleiotropic approaches. One interesting drug target is the leukotriene pathway, which mediates various aspects of AD pathology. Approaching this pathway at different levels with genetic and pharmacological tools demonstrated beneficial outcomes in several in vivo studies using different mouse models of AD. Here, we review the current literature on the leukotriene signaling pathway as a target for drug development in AD.

PMID: 30240876 [PubMed - indexed for MEDLINE]

Lost medicines: a longer view of the pharmaceutical industry with the potential to reinvigorate discovery.

Drug Discov Today. 2019 02;24(2):382-389

Authors: Kinch MS, Kinch GA, Griesenauer RH

Abstract
It is widely understood that the 1962 Kefauver-Harris Amendment to the Food, Drug and Cosmetics Act ushered in the modern regulation of medicines requiring a combination of safety and efficacy. However, fewer appreciate the amendment was applied retroactively to virtually all medicines sold in the USA. For various reasons, many medicines faded into history. Here, we identify and analyze >1600 medicines (including over-the-counter drugs) and their innovators prior to the enactment of Kefauver-Harris. We report 880 of these past medicines are no longer accessible. This project also reveals new insight into the pharmaceutical enterprise, which reveals an industry already mature and beginning to retract before enactment of the legislation. Beyond its historical implications, the recollection of these medicines could offer potential starting points for the future development of much-needed drugs.

PMID: 30223039 [PubMed - indexed for MEDLINE]

Genome-wide scan identifies opioid overdose risk locus close to MCOLN1.

Addict Biol. 2019 Jul 30;:e12811

Authors: Cheng Z, Yang BZ, Zhou H, Nunez Y, Kranzler HR, Gelernter J

Abstract
The United States is experiencing the worst opioid overdose (OpOD) crisis in its history. We carried out a genome-wide association study on OpOD severity among 3 477 opioid-exposed individuals, 1 019 of whom experienced OpODs, including 2 032 European Americans (EAs) (653 overdose cases), and 1 445 African Americans (AAs) (366 overdose cases). Participants were scored 1 to 4 based on their reported overdose status and the number of times that medical treatment was required. Genome-wide association study (GWAS) of EAs and AAs separately resulted in two genome-wide significant (GWS) signals in AAs but none in EAs. The first signal was represented by three closely mapped variants (rs115208233, rs116181528, and rs114077267) located near mucolipin 1 (MCOLN1) and patatin-like phospholipase domain containing 6 (PNPLA6), and the other signal was represented by rs369098800 near dead-box helicase 18 (DDX18). There were no additional GWS signals in the trans-population meta-analysis, so that post-GWAS analysis focused on these loci. In network analysis, MCOLN1 was coexpressed with PNPLA6, but only MCOLN1-associated genes were enriched in functional categories relevant to OpOD, including calcium and cation channel activities; no enrichment was observed for PNPLA6-associated genes. Drug repositioning analysis was carried out in the connectivity map (CMap) database for MCOLN1 (PNPLA6 was not available in CMap) and showed that the opioid agonist drug-induced expression profile is similar to that of MCOLN1 overexpression and yielded the highest-ranked expression profile of 83 drug classes. Thus, MCOLN1 may be a risk gene for OpOD, but replication is needed. This knowledge could be helpful in the identification of drug targets for preventing OpOD.

PMID: 31362332 [PubMed - as supplied by publisher]

A Web Tool for Ranking Candidate Drugs Against a Selected Disease Based on a Combination of Functional and Structural Criteria.

Comput Struct Biotechnol J. 2019;17:939-945

Authors: Karatzas E, Minadakis G, Kolios G, Delis A, Spyrou GM

Abstract
Drug repurposing techniques allow existing drugs to be tested against diseases outside their initial spectrum, resulting in reduced cost and eliminating the long time-frames of new drug development. In silico drug repurposing further speeds up the process either by proposing drugs suitable to invert the transcriptomic profile of a disease or by indicating drugs based on their common targets or structural similarity with other drugs with similar mode of action. Such methods usually return a number of potential repurposed drugs that need to be tested against the disease in in vitro, pre-clinical and clinical studies. Thus, it is crucial to have a more sophisticated candidate drug ranking in order to start testing from the most promising chemical substances. As a means to enhance the above decision process, we present CoDReS (Composite Drug Reranking Scoring), a drug (re-)ranking web-based tool, which combines an initial drug ranking (i.e. repurposing score or hypothesis/potentiality score) with a functional score of each drug considered in conjunction with the disease under study as well as with a structural score derived from potential drugability violations. Furthermore, a structural similarity clustering is applied on the considered drugs and a handful of structural exemplars are suggested for further in vitro and in vivo validation. The user is able to filter the results further, through structural similarity examination of the candidate drugs with drugs that have failed against the queried disease where related clinical trials have been carried out. CoDReS is publicly available online at http://bioinformatics.cing.ac.cy/codres.

PMID: 31360332 [PubMed]

A selective membrane-targeting repurposed antibiotic with activity against persistent methicillin-resistant Staphylococcus aureus.

Proc Natl Acad Sci U S A. 2019 Jul 29;:

Authors: Kim W, Zou G, Hari TPA, Wilt IK, Zhu W, Galle N, Faizi HA, Hendricks GL, Tori K, Pan W, Huang X, Steele AD, Csatary EE, Dekarske MM, Rosen JL, Ribeiro NQ, Lee K, Port J, Fuchs BB, Vlahovska PM, Wuest WM, Gao H, Ausubel FM, Mylonakis E

Abstract
Treatment of Staphylococcus aureus infections is complicated by the development of antibiotic tolerance, a consequence of the ability of S. aureus to enter into a nongrowing, dormant state in which the organisms are referred to as persisters. We report that the clinically approved anthelmintic agent bithionol kills methicillin-resistant S. aureus (MRSA) persister cells, which correlates with its ability to disrupt the integrity of Gram-positive bacterial membranes. Critically, bithionol exhibits significant selectivity for bacterial compared with mammalian cell membranes. All-atom molecular dynamics (MD) simulations demonstrate that the selectivity of bithionol for bacterial membranes correlates with its ability to penetrate and embed in bacterial-mimic lipid bilayers, but not in cholesterol-rich mammalian-mimic lipid bilayers. In addition to causing rapid membrane permeabilization, the insertion of bithionol increases membrane fluidity. By using bithionol and nTZDpa (another membrane-active antimicrobial agent), as well as analogs of these compounds, we show that the activity of membrane-active compounds against MRSA persisters positively correlates with their ability to increase membrane fluidity, thereby establishing an accurate biophysical indicator for estimating antipersister potency. Finally, we demonstrate that, in combination with gentamicin, bithionol effectively reduces bacterial burdens in a mouse model of chronic deep-seated MRSA infection. This work highlights the potential repurposing of bithionol as an antipersister therapeutic agent.

PMID: 31358625 [PubMed - as supplied by publisher]

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2019/07/30

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Induction of endoplasmic reticulum stress and inhibition of colon carcinogenesis by the anti-helmintic drug rafoxanide.

Cancer Lett. 2019 Jul 24;:

Authors: Laudisi F, Di Grazia A, De Simone V, Cherubini F, Colantoni A, Ortenzi A, Franzè E, Dinallo V, Di Fusco D, Monteleone I, Fearon ER, Monteleone G, Stolfi C

Abstract
Colorectal cancer (CRC) remains one of the leading causes of mortality worldwide. Drug repositioning is a promising approach for new cancer therapies, as it provides the opportunity to rapidly advance potentially promising agents into clinical trials. The FDA-approved anti-helminthic drug rafoxanide was recently reported to antagonize the oncogenic function of the BRAF V600E mutant protein, commonly found in CRCs, as well as to inhibit the proliferation of skin cancer cells. These observations prompted us to investigate the potential anti-cancer effects of rafoxanide in CRC models. We found rafoxanide inhibited proliferation in CRC cells, but not in normal colonic epithelial cells. Rafoxanide's anti-proliferative action was associated with marked reduction in cyclin D1 protein levels and accumulation of cells in the G0/G1 phase. These effects relied on selective induction of the endoplasmic reticulum stress (ERS) response in CRC cells and were followed by caspase-dependent cell death. Systemic administration of rafoxanide to ApcMin/+ mice induced to develop CRCs caused ERS activation, proliferation inhibition and apoptosis induction in the neoplastic cells. Collectively, our data suggest rafoxanide might be repurposed as an anti-cancer drug for the treatment of CRC.

PMID: 31351087 [PubMed - as supplied by publisher]

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit.

Molecules. 2019 Jul 25;24(15):

Authors: Xuan P, Zhao L, Zhang T, Ye Y, Zhang Y

Abstract
Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.

PMID: 31349692 [PubMed - in process]

10 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"drug repositioning" OR "drug repurposing"

These pubmed results were generated on 2019/07/25

PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Repurposing antihypertensive drugs for the prevention of Alzheimer's disease: a Mendelian randomization study.

Int J Epidemiol. 2019 Jul 23;:

Authors: Walker VM, Kehoe PG, Martin RM, Davies NM

Abstract
BACKGROUND: Evidence concerning the potential repurposing of antihypertensives for Alzheimer's disease prevention is inconclusive. We used Mendelian randomization, which can be more robust to confounding by indication and patient characteristics, to investigate the effects of lowering systolic blood pressure, via the protein targets of different antihypertensive drug classes, on Alzheimer's disease.
METHODS: We used summary statistics from genome-wide association studies of systolic blood pressure and Alzheimer's disease in a two-sample Mendelian randomization analysis. We identified single-nucleotide polymorphisms (SNPs) that mimic the action of antihypertensive protein targets and estimated the effect of lowering systolic blood pressure on Alzheimer's disease in three ways: (i) combining the protein targets of antihypertensive drug classes, (ii) combining all protein targets and (iii) without consideration of the protein targets.
RESULTS: There was limited evidence that lowering systolic blood pressure, via the protein targets of antihypertensive drug classes, affected Alzheimer's disease risk. For example, the protein targets of calcium channel blockers had an odds ratio (OR) per 10 mmHg lower systolic blood pressure of 1.53 [95% confidence interval (CI): 0.94 to 2.49; p = 0.09; SNPs = 17]. We also found limited evidence for an effect when combining all protein targets (OR per 10 mmHg lower systolic blood pressure: 1.14; 95% CI: 0.83 to 1.56; p = 0.41; SNPs = 59) and without consideration of the protein targets (OR per 10 mmHg lower systolic blood pressure: 1.04; 95% CI: 0.95 to 1.13; p = 0.45; SNPs = 153).
CONCLUSIONS: Mendelian randomization suggests that lowering systolic blood pressure via the protein targets of antihypertensive drugs is unlikely to affect the risk of developing Alzheimer's disease. Consequently, if specific antihypertensive drug classes do affect the risk of Alzheimer's disease, they may not do so via systolic blood pressure.

PMID: 31335937 [PubMed - as supplied by publisher]

Editorial: Drug Repurposing.

Front Med (Lausanne). 2019;6:154

Authors: Pantziarka P, André N

PMID: 31334237 [PubMed]

Chemoproteomics Reveals the Antiproliferative Potential of Parkinson's Disease Kinase Inhibitor LRRK2-IN-1 by Targeting PCNA Protein.

Mol Pharm. 2018 08 06;15(8):3252-3259

Authors: Li W, Zhou Y, Tang G, Wong NK, Yang M, Tan D, Xiao Y

Abstract
LRRK2-IN-1, one of the first selective inhibitors of leucine-rich repeat kinase 2 (LRRK2), was serendipitously found to exhibit potent antiproliferative activity in several types of human cancer cells. In this study, we employed a chemoproteomic strategy utilizing a photoaffinity probe to identify the cellular target(s) of LRRK2-IN-1 underlying its anticancer activity. LRRK2-IN-1 was found to induce cell cycle arrest as well as cancer cell death by specifically binding to human proliferating cell nuclear antigen (PCNA) in cancer cells. Our current findings suggest the potential of LRRK2-IN-1 as a novel pharmacological molecule for scrutinizing cell physiology and furnish a logical foundation for the future development of therapeutic reagents for cancer.

PMID: 29993254 [PubMed - indexed for MEDLINE]

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

J Biomed Inform. 2018 06;82:189-199

Authors: Bakal G, Talari P, Kakani EV, Kavuluru R

Abstract
BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach.
OBJECTIVE: To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs.
METHODS: We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios.
RESULTS: Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset.
CONCLUSIONS: We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

PMID: 29763706 [PubMed - indexed for MEDLINE]

Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression.

PLoS One. 2019;14(7):e0220121

Authors: Landry AP, Balas M, Spears J, Zador Z

Abstract
BACKGROUND: It is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture.
METHODS: We applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture.
RESULTS: Network analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation.
CONCLUSIONS: Our results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.

PMID: 31329646 [PubMed - in process]

No transcriptional evidence for active Nav channels in two classes of cancer cell.

Channels (Austin). 2019 Dec;13(1):311-320

Authors: Hompoonsup S, Chambers D, Doherty P, Williams G

Abstract
Voltage-gated sodium channel (Nav) expression in non-excitable cells has raised questions regarding their non-canonical roles. Interestingly, a growing body of evidence also points towards the prevalence of aberrant Nav expression in malignant tumors, potentially opening a new therapeutic window. In this study, the transcriptional consequences of channel inhibition were investigated in non-small cell lung carcinoma H460 and neuroblastoma SH-SYSY cell lines, that both express Nav1.7. Channel activity was blocked by the application of both selective, ProTx-II, and non-selective, tetrodotoxin, inhibitors. Global gene expression profiling did not point to any statistically significant inhibition-associated perturbation of the transcriptome. A small subset of genes that showed relatively consistent changes across multiple treatments were further assayed in the context of a multiplex bead expression array which failed to recapitulate the changes seen in the global array. We conclude that there is no robust transcriptional signature associated with the inhibition of two sodium channel expressing cancer cell lines and consequently sodium channel inhibition will not lend itself to therapeutic approaches such as transcription-based drug repurposing.

PMID: 31329011 [PubMed - in process]

Evaluating New Compounds to Treat Burkholderia pseudomallei Infections.

Front Cell Infect Microbiol. 2018;8:210

Authors: Ross BN, Myers JN, Muruato LA, Tapia D, Torres AG

Abstract
Burkholderia pseudomallei is the causative agent of melioidosis, a disease that requires long-term treatment regimens with no assurance of bacterial clearance. Clinical isolates are intrinsically resistant to most antibiotics and in recent years, isolates have been collected that display resistance to frontline drugs. With the expanding global burden of B. pseudomallei, there is a need to identify new compounds or improve current treatments to reduce risk of relapse. Using the Pathogen Box generated by Medicines for Malaria Venture, we screened a library of 400 compounds for bacteriostatic or bactericidal activity against B. pseudomallei K96243 and identified seven compounds that exhibited inhibitory effects. New compounds found to have function against B. pseudomallei were auranofin, rifampicin, miltefosine, MMV688179, and MMV688271. An additional two compounds currently used to treat melioidosis, doxycycline and levofloxacin, were also identified in the screen. We determined that the minimal inhibitory concentrations (MIC) for levofloxacin, doxycycline, and MMV688271 were below 12 μg/ml for 5 strains of B. pseudomallei. To assess persister frequency, bacteria were exposed to 100x MIC of each compound. Auranofin, MMV688179, and MMV688271 reduced the bacterial population to an average of 4.53 × 10-6% compared to ceftazidime, which corresponds to 25.1% survival. Overall, our data demonstrates that auranofin, MMV688197, and MMV688271 have the potential to become repurposed drugs for treating melioidosis infections and the first evidence that alternative therapeutics can reduce B. pseudomallei persistence.

PMID: 30013953 [PubMed - indexed for MEDLINE]

Insights into Computational Drug Repurposing for Neurodegenerative Disease.

Trends Pharmacol Sci. 2019 Jul 17;:

Authors: Paranjpe MD, Taubes A, Sirota M

Abstract
Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

PMID: 31326236 [PubMed - as supplied by publisher]

A zebrafish drug screening platform boosts the discovery of novel therapeutics for spinal cord injury in mammals.

Sci Rep. 2019 Jul 19;9(1):10475

Authors: Chapela D, Sousa S, Martins I, Cristóvão AM, Pinto P, Corte-Real S, Saúde L

Abstract
Spinal cord injury (SCI) is a complex condition, with limited therapeutic options, that results in sensory and motor disabilities. To boost discovery of novel therapeutics, we designed a simple and efficient drug screening platform. This innovative approach allows to determine locomotor rescue properties of small molecules in a zebrafish (Danio rerio) larval spinal cord transection model. We validated our screening platform by showing that Riluzole and Minocycline, two molecules that are in clinical trials for SCI, promote rescue of the locomotor function of the transected larvae. Further validation of the platform was obtained through the blind identification of D-Cycloserine, a molecule scheduled to enter phase IV clinical trials for SCI. Importantly, we identified Tranexamic acid and further showed that this molecule maintains its locomotor recovery properties in a rodent female contusion model. Our screening platform, combined with drug repurposing, promises to propel the rapid translation of novel therapeutics to improve SCI recovery in humans.

PMID: 31324865 [PubMed - in process]