Lovastatin inhibits Toll-like receptor 4 signaling in microglia by targeting its co-receptor myeloid differentiation protein 2 and attenuates neuropathic pain.

Brain Behav Immun. 2019 Sep 19;:

Authors: Peng Y, Zhang X, Zhang T, Grace PM, Li H, Wang Y, Li H, Chen H, Watkins LR, Hutchinson MR, Yin H, Wang X

Abstract
There is growing interest in drug repositioning to find new therapeutic indications for drugs already approved for use in people. Lovastatin is an FDA approved drug that has been used clinically for over a decade as a lipid-lowering medication. While lovastatin is classically considered to act as a hydroxymethylglutaryl (HMG)-CoA reductase inhibitor, the present series of studies reveal a novel lovastatin effect, that being as a Toll-like receptor 4 (TLR4) antagonist. Lovastatin selectively inhibits lipopolysaccharide (LPS)-induced TLR4-NF-κB activation without affecting signaling by other homologous TLRs. In vitro biophysical binding and cellular thermal shift assay (CETSA) show that lovastatin is recognized by TLR4's coreceptor myeloid differentiation protein 2 (MD-2). This finding is supported by molecular dynamics simulations that lovastatin targets the LPS binding pocket of MD-2 and lovastatin binding stabilizes the MD-2 conformation. In vitro studies of BV-2 microglial cells revealed that lovastatin inhibits multiple effects of LPS, including activation of NFkB; mRNA expression of tumor necrosis factor-a, interleukin-6 and cyclo-oxygenase 2; production of nitric oxide and reactive oxygen species; as well as phagocytic activity. Furthermore, intrathecal delivery of lovastatin over lumbosacral spinal cord of rats attenuated both neuropathic pain from sciatic nerve injury and expression of the microglial activation marker CD11 in lumbar spinal cord dorsal horn. Given the well-established role of microglia and proinflammatory signaling in neuropathic pain, these data are supportive that lovastatin, as a TLR4 antagonist, may be productively repurposed for treating chronic pain.

PMID: 31542403 [PubMed - as supplied by publisher]

Drug repositioning via matrix completion with multi-view side information.

IET Syst Biol. 2019 Oct;13(5):267-275

Authors: Hao Y, Cai M, Li L

Abstract
In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug-target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug-target prediction approach by matrix completion with multi-view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug-target prediction, they predict drug-target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug-target interactions for 26 FDA-approved drugs, and biologically discuss these targets using existing references.

PMID: 31538961 [PubMed - in process]

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 2019/09/20

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 2019/09/20

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 2019/09/19

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 2019/09/19

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.

Pharmacological approaches to tackle NCLs.

Biochim Biophys Acta Mol Basis Dis. 2019 Sep 12;:165553

Authors: Valerjans K, Maija D, Luis MD

Abstract
Neuronal ceroid lipofuscinoses, also collectively known as Batten disease, are a group of rare monogenic disorders caused by mutations in at least 13 different genes. They are characterized by the accumulation of lysosomal storage material and progressive neurological deterioration with dementia, epilepsy, retinopathy, motor disturbances, and early death [1]. Although the identification of disease-causing genes provides an important step for understanding the molecular mechanisms underlying neuronal ceroid lipofuscinoses, compared to other diseases, obstacles to the development of therapies for these rare diseases include less extensive physiopathology knowledge, limited number of patients to test treatments, and poor commercial interest from the industry. Current therapeutic strategies include enzyme replacement therapies, gene therapies targeting the brain and the eye, cell therapies, and pharmacological drugs that could modulate defective molecular pathways. In this review, we will focus in the emerging therapies based in the identification of small-molecules. Recent advances in high- throughput and high-content screening (HTS and HCS) using relevant cell-based assays and applying automation and imaging analysis algorithms, will allow the screening of a large number of compounds in lesser time. These approaches are particularly useful for drug repurposing for Batten disease, that takes the advantage to search for compounds that have already been tested in humans, thereby reducing significantly the resources needed for translation to clinics.

PMID: 31521819 [PubMed - as supplied by publisher]

Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies.

Cell Chem Biol. 2019 Sep 10;:

Authors: Ravikumar B, Timonen S, Alam Z, Parri E, Wennerberg K, Aittokallio T

Abstract
Owing to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome-profiling strategies. We implemented VirtualKinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential, against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false-discovery rate, when compared with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.

PMID: 31521622 [PubMed - as supplied by publisher]

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests.

J Chem Inf Model. 2019 Sep 13;:

Authors: Meyer JG, Liu S, Miller IJ, Coon JJ, Gitter A

Abstract
Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH 'Therapeutic Use' classes, can be predicted from only chemical structure. We evaluated two chemical structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used drug-induced transcriptomic changes as chemical representations. This suggests that a chemical's structure contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Further, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug repurposing opportunities, and demonstrate the effectiveness of this modeling strategy for multi-label classification.

PMID: 31518132 [PubMed - as supplied by publisher]

Multi-target drugs active against leishmaniasis: A paradigm of drug repurposing.

Eur J Med Chem. 2019 Aug 29;183:111660

Authors: Braga SS

Abstract
This mini-review focuses on leishmanicidal drugs that were sourced from small molecules previously approved for other diseases. The mechanisms of action of these molecules are herein explored, to probe the origins of their inter-species growth inhibitory activities. It is shown how the transversal action of the azoles - fluconazole, posaconazole and itraconazole - in both fungi and Leishmania is due to the occurrence of the same target, lanosterol 14-α-demethylase, in these two groups of species. In turn, the drugs miltefosine and amphotericin B are presented as truly multi-target agents, acting on small molecules, proteins, genes and even organelles. Steps towards future leishmanicidal drug candidates based on the multi-target strategy and on drug repurposing are also briefly presented.

PMID: 31514064 [PubMed - as supplied by publisher]

Internalization of Particulate Delivery Systems by Activated Microglia Influenced the Therapeutic Efficacy of Simvastatin Repurposing for Neuroinflammation.

Int J Pharm. 2019 Sep 09;:118690

Authors: Manickavasagam D, Oyewumi MO

Abstract
We recently evaluated the suitability of polymersome delivery systems in simvastatin repurposing for treating neuroinflammation. The goal of the current study is to elucidate the therapeutic impact of particulate internalization by activated microglia on the resultant anti-inflammatory properties. Thus, we investigated the endocytic mechanism(s) involved in uptake and transport of simvastatin-loaded polymersomes by BV2 microglia cells coupled with delineation of the intracellular pathway(s) involved in regulating anti-inflammatory effects. Our data indicated that internalization of polymersome delivery systems by activated microglial BV2 cells was important in the suppression of nitric oxide (NO), TNF-α and IL-6 production. Further, we observed that the lipid raft/caveolae pathway had the most influential effect on polymersome internalization by microglia cells while clathrin-mediated endocytosis did not play a major role. Enhancement of anti-inflammatory effects of simvastatin could be attributed to inhibition of ERK1/2, JNK and AKT signaling pathways and internalization of polymersome delivery systems in activated microglia. Taken together, our data provided insights into how the intracellular trafficking of delivery systems by microglial could be a useful tool in modulating the desired anti-inflammatory effects of drugs.

PMID: 31513872 [PubMed - as supplied by publisher]

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.

Bioinformatics. 2019 Jul 15;35(14):i191-i199

Authors: Iwata M, Yuan L, Zhao Q, Tabei Y, Berenger F, Sawada R, Akiyoshi S, Hamano M, Yamanishi Y

Abstract
MOTIVATION: Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.
RESULTS: Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 31510663 [PubMed - in process]

Drug repositioning based on bounded nuclear norm regularization.

Bioinformatics. 2019 Jul 15;35(14):i455-i463

Authors: Yang M, Luo H, Li Y, Wang J

Abstract
MOTIVATION: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations.
RESULTS: In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug-drug and disease-disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug-disease network, which integrates the drug-drug, drug-disease and disease-disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug-disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR.
AVAILABILITY AND IMPLEMENTATION: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 31510658 [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 2019/09/12

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.

9 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 2019/09/12

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.

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates.

Cancer Med. 2019 Sep 10;:

Authors: De Bastiani MA, Klamt F

Abstract
BACKGROUND: Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment.
METHODS: We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning.
RESULTS: The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival.
CONCLUSION: Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.

PMID: 31503425 [PubMed - as supplied by publisher]

Organogold drug Auranofin exhibits anti-melanogenic activity in B16F10 and MNT-1 melanoma cells.

Arch Dermatol Res. 2019 Sep 09;:

Authors: Goenka S, Simon SR

Abstract
Auranofin (AF) is an organogold FDA-approved drug for treating rheumatism and has been repurposed for several pharmacological applications based on its anti-bacterial, anti-fungal and anti-inflammatory activities. To the best of our knowledge, there has been no study on effects of AF on melanogenesis yet. Hence, in this work, we studied the effect of AF on melanogenesis using B16F10 mouse melanoma cells and validated results in MNT-1 human melanoma cells. Melanogenesis assay was conducted with concentrations of AF determined to be nontoxic in B16F10 cells as well as HaCaT human epidermal cell line for a duration of 48 h, followed by various assays to delineate mechanisms of melanogenesis inhibition. Ultrastructural analysis was conducted to study further if AF affected melanosome maturation and protein levels of a key melanogenic protein, tyrosinase, and the maturation signaling molecule, cyclic adenosine monophosphate (cAMP), was estimated. Our results demonstrate that AF at nontoxic concentrations of 0.25-1 µM significantly inhibited melanin synthesis in a dose-dependent manner with significant inhibition of 32.85% at 1 µM. The study of mechanisms of melanogenesis inhibition revealed that AF inhibited tyrosinase activity in lysates of B16F10 cells but did not show a direct effect on purified mushroom tyrosinase activity or on copper chelation in a cell-free system, nor did it affect levels of B16F10 tyrosinase protein levels. However, AF significantly down-regulated cAMP levels, inhibited cellular ROS and increased number of melanosomes in immature stages, and also exhibited anti-melanogenic activity in B16F10-HaCaT cocultures. Furthermore, AF showed anti-melanogenic efficacy in MNT-1 cell monocultures and cocultures with an inhibition of intracellular tyrosinase activity. In summary, our results demonstrate a proof-of-principle for AF as a depigmenting agent for hyperpigmentation disorders and adjuvant for melanoma therapeutics.

PMID: 31501921 [PubMed - as supplied by publisher]

Methylxanthines: Potential Therapeutic Agents for Glioblastoma.

Pharmaceuticals (Basel). 2019 Sep 07;12(3):

Authors: Pérez-Pérez D, Reyes-Vidal I, Chávez-Cortez EG, Sotelo J, Magaña-Maldonado R

Abstract
Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Currently, treatment is ineffective and the median overall survival is 20.9 months. The poor prognosis of GBM is a consequence of several altered signaling pathways that favor the proliferation and survival of neoplastic cells. One of these pathways is the deregulation of phosphodiesterases (PDEs). These enzymes participate in the development of GBM and may have value as therapeutic targets to treat GBM. Methylxanthines (MXTs) such as caffeine, theophylline, and theobromine are PDE inhibitors and constitute a promising therapeutic anti-cancer agent against GBM. MTXs also regulate various cell processes such as proliferation, migration, cell death, and differentiation; these processes are related to cancer progression, making MXTs potential therapeutic agents in GBM.

PMID: 31500285 [PubMed]

Binding of acarbose, an anti-diabetic drug to lysozyme: a combined structural and thermodynamic study.

J Biomol Struct Dyn. 2018 10;36(13):3354-3361

Authors: Dileep KV, Nithiyanandan K, Remya C

PMID: 28984494 [PubMed - indexed for MEDLINE]

A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy of Teprenone in Patients with Alzheimer's Disease.

J Alzheimers Dis. 2019 Sep 03;:

Authors: Yokoyama S, Yoshinaga T, Matsuzaki J, Suzuki H

Abstract
BACKGROUND: Teprenone (geranylgeranylacetone), an anti-ulcer agent, has been reported to inhibit amyloid-β increase, senile plaque formation, and neuronal degeneration, and improve memory in mouse models of Alzheimer's disease (AD).
OBJECTIVE: We conducted a randomized, double-blind, placebo-controlled study to ascertain teprenone's therapeutic ability for AD.
METHODS: Patients with mild to moderate AD, with a Mini-Mental State Examination (MMSE) score of 13 to 26, were randomly allocated into two groups depending on the administered drug: donepezil +  placebo (placebo group) and donepezil + teprenone (teprenone group). The primary and secondary endpoints included changes in scores of the Japanese version of the AD Assessment Scale-cognitive subscale (ADAS-J cog) and other evaluations, respectively, including MMSE scores, during a 12-month period after the first administration.
RESULTS: Forty-two and thirty-seven patients were allocated to the teprenone and placebo groups, respectively. ADAS-J cog score changes were not different between groups (placebo, 0.6±0.8; teprenone, 0.4±0.8; p = 0.861). However, MMSE scores significantly improved in the teprenone group (placebo, - 1.2±0.5; teprenone, 0.2±0.5; p = 0.044). Subgroup analysis considering the severity of medial temporal area atrophy revealed that this improvement by teprenone was significant in patients with mild (p = 0.013) but not with severe atrophy (p = 0.611). Adverse events were observed in 17.8 and 10.4% of patients in the placebo and teprenone group, respectively.
CONCLUSION: Teprenone may be effective for AD when administered before atrophy progression in the medial temporal areas.
TRIAL REGISTRATION: UMIN ID: UMIN000016843.

PMID: 31498121 [PubMed - as supplied by publisher]