Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition.

BMC Bioinformatics. 2019 Mar 29;20(Suppl 3):134

Authors: Wu G, Liu J, Yue X

Abstract
BACKGROUND: In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples.
RESULTS: In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD.
CONCLUSIONS: The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

PMID: 30925858 [PubMed - in process]

The steroid derivative 6-aminocholestanol inhibits the DEAD-box helicase eIF4A (LieIF4A) from the Trypanosomatid parasite Leishmania by perturbing the RNA and ATP binding sites.

Mol Biochem Parasitol. 2018 12;226:9-19

Authors: Abdelkrim YZ, Harigua-Souiai E, Barhoumi M, Banroques J, Blondel A, Guizani I, Tanner NK

Abstract
The antifungal agent 6-aminocholestanol targets the production of ergosterol, which is the principle sterol in many fungi and protozoans; ergosterol serves many of the same roles as cholesterol in animals. We found that it also is an effective inhibitor of the translation-initiation factor eIF4AI from mouse (eIF4AIMus) and the Trypanosomatid parasite Leishmania (LieIF4A). The eIF4A proteins belong to the DEAD-box family of RNA helicases, which are ATP-dependent RNA-binding proteins and RNA-dependent ATPases. DEAD-box proteins contain a commonly-shared core structure consisting of two linked domains with structural homology to that of recombinant protein A (RecA) and that contain conserved motifs that are involved in RNA and ATP binding, and in the enzymatic activity. The compound inhibits both the ATPase and helicase activities by perturbing ATP and RNA binding, and it is capable of binding other proteins containing nucleic acid-binding sites as well. We undertook kinetic analyses and found that the Leishmania LieIF4A protein binds 6-aminocholestanol with a higher apparent affinity than for ATP, although multiple binding sites were probably involved. Competition experiments with the individual RecA-like domains indicate that the primary binding sites are on RecA-like domain 1, and they include a cavity that we previously identified by molecular modeling of LieIF4A that involve conserved RNA-binding motifs. The compound affects the mammalian and Leishmania proteins differently, which indicates the binding sites and affinities are not the same. Thus, it is possible to develop drugs that target DEAD-box proteins from different organisms even when they are implicated in the same biological process.

PMID: 30365976 [PubMed - indexed for MEDLINE]

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Cell Chem Biol. 2018 05 17;25(5):611-618.e3

Authors: Simm J, Klambauer G, Arany A, Steijaert M, Wegner JK, Gustin E, Chupakhin V, Chong YT, Vialard J, Buijnsters P, Velter I, Vapirev A, Singh S, Carpenter AE, Wuyts R, Hochreiter S, Moreau Y, Ceulemans H

Abstract
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

PMID: 29503208 [PubMed - indexed for MEDLINE]

Encircling the regions of the pharmacogenomic landscape that determine drug response.

Genome Med. 2019 Mar 26;11(1):17

Authors: Fernández-Torras A, Duran-Frigola M, Aloy P

Abstract
BACKGROUND: The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.
METHODS: To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.
RESULTS: We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.
CONCLUSIONS: Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

PMID: 30914058 [PubMed - in process]

Repurposing of the CDK inhibitor PHA-767491 as a NRF2 inhibitor drug candidate for cancer therapy via redox modulation.

Invest New Drugs. 2018 08;36(4):590-600

Authors: Liu HY, Tuckett AZ, Fennell M, Garippa R, Zakrzewski JL

Abstract
Oxidative stress and cellular response mechanisms such as NRF2-mediated antioxidant responses play differential roles in healthy and diseased cells. Constant generation and elimination of high levels of reactive oxygen species is a hallmark of many cancer cell types; this phenomenon is not observed during steady state of healthy cells. Manipulation of NRF2 transcriptional activity and the cellular redox homeostasis therefore has potential to be therapeutically exploitable for cancer therapy by preferentially targeting cancer cells for induction of oxidative stress. We found that the NRF2 inhibitor brusatol triggered increased oxidative stress while compromising viability and proliferation of multiple myeloma cells. Using a repurposing approach we discovered that the Cdc7/CDK9 inhibitor PHA-767491 is also a potent inhibitor of NRF2 transcriptional activity. The molecule was identified by high throughput screening of a library of about 5900 drug-like molecules. Screening assays included two cell-based assays using HepG2 hepatocellular carcinoma cells: a) A NRF2 nuclear translocation assay, and b) A NRF2 luciferase reporter assay. Validation assays were performed in multiple myeloma cells and included detection of mitochondrial superoxide levels and MTS assays. We found that PHA-767491 treatment of multiple myeloma cells was associated with inhibition of nuclear translocation of NRF2, increased mitochondrial superoxide levels and inhibition of cell growth. Our findings suggest that PHA-767491 is a promising drug candidate for cancer therapy with NRF2 inhibitory potency contributing to its anti-cancer properties.

PMID: 29297149 [PubMed - indexed for MEDLINE]

In Silico Drug-Target Profiling.

Methods Mol Biol. 2019;1953:89-103

Authors: Trosset JY, Cavé C

Abstract
Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

PMID: 30912017 [PubMed - in process]

Oxidative Stress in Microbial Diseases: Pathogen, Host, and Therapeutics.

Oxid Med Cell Longev. 2019;2019:8159562

Authors: Novaes RD, Teixeira AL, de Miranda AS

PMID: 30774746 [PubMed - indexed for MEDLINE]

Commentary on: A Novel Method for Lower Blepharoplasty: Repositioning of the Orbital Septum Using Inverted T-Shaped Plication.

Aesthet Surg J. 2018 06 13;38(7):714-716

Authors: Hamra ST

PMID: 29697743 [PubMed - indexed for MEDLINE]

Transcriptome Guided Drug Combination Suppresses Proliferation of Breast Cancer Cells.

Bull Exp Biol Med. 2019 Mar 22;:

Authors: Shkurnikov MY, Poloznikov AA, Nikulin SV, Schumacher U, Wicklein D, Stürken C, Galatenko VV, Alekseev BY

Abstract
One of actively developing trends in modern pharmacology is the use of the transcriptome analysis for drug repositioning. We have previously detected two molecular markers of relapses in patients with malignant breast tumors: ELOVL5 and IGFBP6. Poor prognosis is associated with low expression of these markers. Here we analyze the effects of simvastatin and a new potential proteasome inhibitor K7174 inducing expression of IGFBP6 and EVOVL5 on the proliferation of breast cancer cells MDA-MB-231 and DU4475. Compound K7174 potentiates the inhibitory effect of simvastatin on the proliferation of DU4475 cells characterized by low expression of ELOVL5-IGFBP6 pair, but not on the proliferation of MDA-MB-231 cells with high expression of these markers.

PMID: 30903492 [PubMed - as supplied by publisher]

7 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 2019/03/22

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.

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 2019/03/21

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.

Spectrum of candidate molecules against Chikungunya virus - an insight into the antiviral screening platforms.

Expert Rev Anti Infect Ther. 2019 Mar 19;:

Authors: Bhat SM, Mudgal PP, N S, Arunkumar G

Abstract
INTRODUCTION: Chikungunya disease has undergone a phenomenal transition in its status from being recognized as a sporadic infection to acquiring a global prominence over the last couple of decades. The causative agent behind the explosive worldwide epidemics is the re-emerging pathogen, Chikungunya virus (CHIKV). Areas covered: The current review discusses all the possible avenues of antiviral research towards combating CHIKV infection. Aspects of antiviral drug discovery such as antiviral targets, candidate molecules screened, and the various criteria to be a potential inhibitor are all discussed at length. Existing antiviral drug screening tools for CHIKV and their applications are thoroughly described. Clinical trial status of agents with therapeutic potential has been updated with special mention of candidate molecules under patent approval. Databases such as PubMed, Google Scholar, ScienceDirect, Google Patent, and Clinical Trial Registry platforms were referred. Expert opinion: The massive outbreaks of Chikungunya viral disease in the recent past and the serious health concerns imposed thereby have driven the search for effective therapeutics. The greatest challenge being the non-availability of robust, reproducible, cost-effective and biologically accurate assay models. Nevertheless, there is a need to identify good models mimicking the appropriate microenvironment of an infectious setting.

PMID: 30889372 [PubMed - as supplied by publisher]

Antiviral candidates for treating hepatitis E virus infection.

Antimicrob Agents Chemother. 2019 Mar 18;:

Authors: Netzler NE, Tuipulotu DE, Vasudevan SG, Mackenzie JM, White PA

Abstract
Globally, hepatitis E virus (HEV) causes significant morbidity and mortality each year. Despite this burden, there are no specific antivirals available to treat HEV patients, and the only licensed vaccine is not available outside of China. Ribavirin and interferon-α are used to treat chronic HEV infections, however severe side effects and treatment failure are commonly reported. Therefore, this study aimed to identify potential antivirals for further development to combat HEV infection. We selected 16 compounds from the nucleoside and non-nucleoside antiviral classes that range in developmental status from late preclinical to FDA-approved, and evaluated them as potential antivirals for HEV infection, using genotype 1 replicon luminescence studies and replicon RNA quantification. Two potent inhibitors of HEV replication included NITD008 (EC50 0.03 μM, CC50 >100 μM) and GPC-N114 (EC50 1.07 μM, CC50 >100 μM), and both drugs reduced replicon RNA levels in cell culture (>50% reduction with either 10 μM GPC-N114 or 2.50 μM NITD008). Furthermore, GPC-N114 and NITD008 were synergistic in combinational treatment (combination index 0.4) against HEV replication, allowing for dose reduction indices of 20.42 and 8.82 at 50% inhibition, respectively. Sofosbuvir has previously exhibited mixed results against HEV as an antiviral, both in vitro and in a handful of clinical applications, however in this study it was effective against the HEV genotype 1 replicon (EC50 1.97 μM, CC50 >100 μM) and reduced replicon RNA levels (47.2% reduction at 10 μM). Together these studies indicate drug repurposing may be a promising pathway for development of antivirals against HEV infection.

PMID: 30885901 [PubMed - as supplied by publisher]

Tracing MYC Expression for Small Molecule Discovery.

Cell Chem Biol. 2019 Feb 21;:

Authors: Steinberger J, Robert F, Hallé M, Williams DE, Cencic R, Sawhney N, Pelletier D, Williams P, Igarashi Y, Porco JA, Rodriguez AD, Kopp B, Bachmann B, Andersen RJ, Pelletier J

Abstract
Our inability to effectively "drug" targets such as MYC for therapeutic purposes requires the development of new approaches. We report on the implementation of a phenotype-based assay for monitoring MYC expression in multiple myeloma cells. The open reading frame (ORF) encoding an unstable variant of GFP was engineered immediately downstream of the MYC ORF using CRISPR/Cas9, resulting in co-expression of both proteins from the endogenous MYC locus. Using fluorescence readout as a surrogate for MYC expression, we implemented a pilot screen in which ∼10,000 compounds were prosecuted. Among known MYC expression inhibitors, we identified cardiac glycosides and cytoskeletal disruptors to be quite potent. We demonstrate the power of CRISPR/Cas9 engineering in establishing phenotype-based assays to identify gene expression modulators.

PMID: 30880156 [PubMed - as supplied by publisher]

Clinical cosmeceutical repurposing of melatonin in androgenic alopecia using nanostructured lipid carriers prepared with antioxidant oils.

Expert Opin Drug Deliv. 2018 10;15(10):927-935

Authors: Hatem S, Nasr M, Moftah NH, Ragai MH, Geneidi AS, Elkheshen SA

Abstract
BACKGROUND: The present work aims to formulate nanostructured lipid carriers (NLCs) exhibiting high skin deposition and high inherent antioxidant potential to repurpose the use of melatonin hormone and some antioxidant oils in the treatment of androgenic alopecia (AGA).
RESEARCH DESIGN AND METHODS: NLCs were characterized for their size, charge, drug entrapment, anti-oxidant potential, physical stability, in vitro release, surface morphology, and ex-vivo skin deposition. Their merits were clinically tested on patients suffering from AGA by calculating the degree of improvement, conduction of hair pull test, histometric assessment, and dermoscopic evaluation.
RESULTS: Results revealed that melatonin NLCs showed nanometer size, negatively charged surface, high entrapment efficiency, and high anti-oxidant potential, in addition to sustained release for 6 h. Furthermore, NLCs displayed good storage stability and they were able to increase the skin deposition of melatonin 4.5-folds in stratum corneum, 7-folds in epidermis, and 6.8-folds in the dermis compared to melatonin solution. Melatonin NLCs displayed more clinically desirable results compared to the melatonin solution in AGA patients, manifested by increased hair density and thickness and decreased hair loss.
CONCLUSIONS: The aforementioned system was shown to be a very promising treatment modality for AGA, which is worthy of futuristic experimentation.

PMID: 30169980 [PubMed - indexed for MEDLINE]

Can cardiovascular drugs support cancer treatment? The rationale for drug repurposing.

Drug Discov Today. 2019 Mar 13;:

Authors: Regulska K, Regulski M, Karolak B, Murias M, Stanisz B

Abstract
Research on the concept of biological overlap between cardiovascular and oncological diseases is gaining momentum. In fact, in both conditions, the malfunction of common regulatory mechanisms, such as the renin-angiotensin system (RAS), sympathetic nervous system (SNS), coagulation cascade, sodium-potassium ATP-ases, and mevalonate pathway, occurs. Thus, targeting these mechanisms with well-known cardiology drugs, including angiotensin-converting enzyme inhibitors (ACE-Is), angiotensin receptor blockers (ARBs), β-adrenergic receptor blockers, statins, cardiac glycosides (CGs), and low-molecular-weight heparins (LMWHs), could be a novel, promising adjuvant strategy in cancer management. Thus, here we discuss the idea of repurposing cardiology drugs in oncology based on available preclinical and clinical data.

PMID: 30878563 [PubMed - as supplied by publisher]

Machine learning on adverse drug reactions for pharmacovigilance.

Drug Discov Today. 2019 Mar 12;:

Authors: Lee CY, Chen YP

Abstract
Adverse drug reactions are an unresolved issue that can result in mortality, morbidity and substantial healthcare costs. Many conventional machine learning methods have been used for predicting post-marketing drug side-effects. However, owing to the complex chemical structures of certain drugs and the nonlinear and imbalanced nature of biological data, some side-effects might not be detected. Motivated by the drug discovery research studies that have shown that deep learning outperformed machine learning methods over prediction tasks, we proposed: (i) to exploit the unsupervised deep learning approaches to predict ADRs; (ii) to use a two-stage framework to predict personalized ADRs and repurpose the drugs. This work demonstrates that the proposed framework shows promise in providing more-accurate prediction of side-effects and drug repurposing.

PMID: 30876845 [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 2019/03/16

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.

8 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 2019/03/15

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.

7 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 2019/03/14

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.