Literature-based prediction of novel drug indications considering relationships between entities.

Mol Biosyst. 2017 Jun 05;:

Authors: Jang G, Lee T, Lee BM, Yoon Y

Abstract
There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug-phenotype associations with therapeutic possibility. To validate the drug-phenotype associations predicted by our method, we make an enrichment comparison with known drug-phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

PMID: 28581007 [PubMed - as supplied by publisher]

Anti-obesogenic and hypolipidemic effects of a glucagon-like peptide-1 receptor agonist derived from the saliva of the Gila monster.

Toxicon. 2017 Jun 01;:

Authors: Alves PL, Abdalla FMF, Alponti RF, Silveira PF

Abstract
INTRODUCTION: Glucagon-like peptide-1 (GLP-1) receptor (R) agonists are a class of incretin mimetic drugs that have been used for the treatment of type 2 diabetes mellitus and also considered strong candidates for the treatment of obesity. The original prototypical drug in this class is the exenatide, a synthetic peptide with the same structure as the native molecule, exendin-4, found in the saliva of the Gila monster (Heloderma suspectum suspectum lizard).
OBJECTIVES: To identify and compare the anti-obesogenic, antidyslipidemic and antidiabetogenic effects of agonism in GLP-1R by exenatide on two distinct models of obesity: induced by hypothalamic injury (MSG) or high-calorie diet (DIO).
METHODS: To obtain MSG, neonatal rats were daily subcutaneously injected with 4 g monosodium glutamate/kg, for 10 consecutive days. To obtain DIO, 72-75 days old rats received hyperlipid food and 30% sucrose for drinking up to 142-145 days old. Untreated healthy rats with the same age were used as control. General biometric and metabolic parameters were measured.
RESULTS: MSG was characterized by decreased naso-anal length, food and fluid intake, plasma protein and glucose decay rate per minute after insulin administration (KITT), as well as increased Lee index (body mass(0.33)/naso-anal length), mass of retroperitoneal and periepididymal fat pads, glycemia, triglycerides (TG), LDL and VLDL. Exenatide ameliorated KITT and food and fluid intake, and it also restored glycemia in MSG. DIO was characterized by glucose intolerance, increased body mass, Lee index, fluid intake, mass of retroperitoneal and periepididymal fat pads, glycemia, glycated hemoglobin (HbA1c), TG, VLDL and total cholesterol, as well as decreased food intake and KITT. Exenatide restored glycemia, HbA1c, TG, VLDL, total cholesterol and body mass, and it also ameliorated food and fluid intake, KITT and mass of retroperitoneal fat pad in DIO.
CONCLUSIONS: The hypothalamic injury and the high-calorie diet induce dyslipidemia and glycemic dysregulation in addition to obesity in rats. The usual therapeutic dose of exenatide in humans is antidiabetogenic in both these obesity models, but is anti-obesogenic and hypolipidemic only in diet-induced obesity. Agonists of GLP-1R are promising anti-obesogenic and antidyslipidemic drugs in the early stages of the obesity, in which the integrity of the nervous system was unaffected.

PMID: 28579479 [PubMed - as supplied by publisher]

Macromolecular target prediction by self-organizing feature maps.

Expert Opin Drug Discov. 2017 Mar;12(3):271-277

Authors: Schneider G, Schneider P

Abstract
INTRODUCTION: Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

PMID: 27997811 [PubMed - indexed for MEDLINE]

Dopaminergic Regulation of Innate Immunity: a Review.

J Neuroimmune Pharmacol. 2017 Jun 03;:

Authors: Pinoli M, Marino F, Cosentino M

Abstract
Dopamine (DA) is a neurotransmitter in the central nervous system as well as in peripheral tissues. Emerging evidence however points to DA also as a key transmitter between the nervous system and the immune system as well as a mediator produced and released by immune cells themselves. Dopaminergic pathways have received so far extensive attention in the adaptive branch of the immune system, where they play a role in health and disease such as multiple sclerosis, rheumatoid arthritis, cancer, and Parkinson's disease. Comparatively little is known about DA and the innate immune response, although DA may affect innate immune system cells such as dendritic cells, macrophages, microglia, and neutrophils. The present review aims at providing a complete and exhaustive summary of currently available evidence about DA and innate immunity, and to become a reference for anyone potentially interested in the fields of immunology, neurosciences and pharmacology. A wide array of dopaminergic drugs is used in therapeutics for non-immune indications, such as Parkinson's disease, hyperprolactinemia, shock, hypertension, with a usually favorable therapeutic index, and they might be relatively easily repurposed for immune-mediated disease, thus leading to innovative treatments at low price, with benefit for patients as well as for the healthcare systems.

PMID: 28578466 [PubMed - as supplied by publisher]

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review.

AAPS J. 2017 Jun 02;:

Authors: Cheng T, Hao M, Takeda T, Bryant SH, Wang Y

Abstract
The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

PMID: 28577120 [PubMed - as supplied by publisher]

Repurposing of nucleoside- and nucleobase-derivative drugs as antibiotics and biofilm inhibitors.

J Antimicrob Chemother. 2017 May 30;:

Authors: Yssel AEJ, Vanderleyden J, Steenackers HP

Abstract
There is an urgent need for new antibacterial drugs that are robust against the development of resistance. Drug repurposing is a cost-effective strategy to fast-track the drug development process. Here we examine why the nucleoside and nucleobase analogue drugs in particular present an attractive class for repurposing. Some of these drugs have already been evaluated for their potential as antibacterial agents. In addition to inhibiting bacterial growth and survival, some also act synergistically with antibiotics, and as such can enhance the therapeutic spectrum of currently available antibiotics. Furthermore, nucleoside and nucleobase analogue drugs can inhibit bacterial virulence and biofilm formation. Biofilms are known to impart antibiotic tolerance and are associated with chronic infections. Targeting biofilm formation thus renders pathogens more susceptible to antibiotic treatment and host immune defences. Moreover, specific analogues have properties that make them less susceptible to the development of resistance. Thus, nucleoside and nucleobase analogue drugs ought to be considered as new weapons in our fight against pathogenic bacteria.

PMID: 28575223 [PubMed - as supplied by publisher]

Statin and rottlerin small-molecule inhibitors restrict colon cancer progression and metastasis via MACC1.

PLoS Biol. 2017 Jun;15(6):e2000784

Authors: Juneja M, Kobelt D, Walther W, Voss C, Smith J, Specker E, Neuenschwander M, Gohlke BO, Dahlmann M, Radetzki S, Preissner R, von Kries JP, Schlag PM, Stein U

Abstract
MACC1 (Metastasis Associated in Colon Cancer 1) is a key driver and prognostic biomarker for cancer progression and metastasis in a large variety of solid tumor types, particularly colorectal cancer (CRC). However, no MACC1 inhibitors have been identified yet. Therefore, we aimed to target MACC1 expression using a luciferase reporter-based high-throughput screening with the ChemBioNet library of more than 30,000 compounds. The small molecules lovastatin and rottlerin emerged as the most potent MACC1 transcriptional inhibitors. They remarkably inhibited MACC1 promoter activity and expression, resulting in reduced cell motility. Lovastatin impaired the binding of the transcription factors c-Jun and Sp1 to the MACC1 promoter, thereby inhibiting MACC1 transcription. Most importantly, in CRC-xenografted mice, lovastatin and rottlerin restricted MACC1 expression and liver metastasis. This is-to the best of our knowledge-the first identification of inhibitors restricting cancer progression and metastasis via the novel target MACC1. This drug repositioning might be of therapeutic value for CRC patients.

PMID: 28570591 [PubMed - in process]

From the Viewpoint of Drug Metabolism Research.

Yakugaku Zasshi. 2017;137(6):697-705

Authors: Nakajima M

Abstract
 Since more than 70% of clinically used drugs are excreted from the body through metabolic processes, drug metabolism is a key determinant of pharmacokinetics, drug response and drug toxicity. Much progress has been made in understanding drug-drug interactions via the inhibition or induction of cytochrome P450s (P450, CYP), as well as the effects of genetic polymorphisms of P450s on pharmacokinetics, and this has facilitated the progress of optimized pharmacotherapy in the clinic. Now, similar information is needed for non-CYP enzymes, especially concerning Phase I enzymes, based on advanced basic and clinical studies. Recently, it was revealed that post-transcriptional regulation by microRNAs or RNA editing plays a significant role in regulating the expression of drug-metabolizing enzymes, thus conferring variability in the detoxification and metabolic activation of drugs or chemicals. Changes in the expression profile of microRNAs in tissues or body fluids can be a biomarker of drug response and toxicity; therefore, such studies could also be useful for drug repositioning. In addition, microRNAs are involved in pharmacogenetics, because single nucleotide polymorphisms in microRNA binding sites of mRNAs, or microRNAs themselves, may cause changes in gene expression. Some microRNA-related polymorphisms could be biomarkers of the clinical outcome of pharmacotherapy. In this review article, recent progress and future directions for drug metabolism studies are discussed.

PMID: 28566576 [PubMed - in process]

DrugSig: A resource for computational drug repositioning utilizing gene expression signatures.

PLoS One. 2017;12(5):e0177743

Authors: Wu H, Huang J, Zhong Y, Huang Q

Abstract
Computational drug repositioning has been proved as an effective approach to develop new drug uses. However, currently existing strategies strongly rely on drug response gene signatures which scattered in separated or individual experimental data, and resulted in low efficient outputs. So, a fully drug response gene signatures database will be very helpful to these methods. We collected drug response microarray data and annotated related drug and targets information from public databases and scientific literature. By selecting top 500 up-regulated and down-regulated genes as drug signatures, we manually established the DrugSig database. Currently DrugSig contains more than 1300 drugs, 7000 microarray and 800 targets. Moreover, we developed the signature based and target based functions to aid drug repositioning. The constructed database can serve as a resource to quicken computational drug repositioning. Database URL: http://biotechlab.fudan.edu.cn/database/drugsig/.

PMID: 28562632 [PubMed - in process]

Pharmacotherapeutic Targeting of G Protein-Coupled Receptors in Oncology: Examples of Approved Therapies and Emerging Concepts.

Drugs. 2017 Jun;77(9):951-965

Authors: Lappano R, Maggiolini M

Abstract
G protein-coupled receptors (GPCRs) are involved in numerous physio-pathological processes, including the stimulation of cancer progression. In this regard, it should be mentioned that although GPCRs may represent major pharmaceutical targets, only a few drugs acting as GPCR inhibitors are currently used in anti-tumor therapies. For instance, certain pro-malignancy effects mediated by GPCRs are actually counteracted by the use of small molecules and peptides that function as receptor antagonists or inverse agonists. Recently, humanized monoclonal antibodies targeting GPCRs have also been developed. Here, we review the current GPCR-targeted therapies for cancer treatment, summarizing the clinical studies that led to their official approval. We provide a broad overview of the mechanisms of action of the available anti-cancer drugs targeting gonadotropin-releasing hormone, somatostatin, chemokine, and Smoothened receptors. In addition, we discuss the anti-tumor potential of novel non-approved molecules and antibodies able to target some of the aforementioned GPCRs in different experimental models and clinical trials. Likewise, we focus on the repurposing in cancer patients of non-oncological GPCR-based drugs, elucidating the rationale behind this approach and providing clinical evidence on their safety and efficacy.

PMID: 28401445 [PubMed - indexed for MEDLINE]

Old and new applications of non-anticoagulant heparin.

Int J Cardiol. 2016 Jun;212 Suppl 1:S14-21

Authors: Cassinelli G, Naggi A

Abstract
The aim of this chapter is to provide an overview of non-anticoagulant effects of heparins and their potential use in new therapeutic applications. Heparin and heparin derivatives have been tested in inflammatory, pulmonary and reproductive diseases, in cardiovascular, nephro- and neuro-tissue protection and repair, but also as agents against angiogenesis, atheroschlerosis, metastasis, protozoa and viruses. Targeting and inhibition of specific mediators involved in the inflammatory process, promoting some of the above mentioned pathologies, are reported along with recent studies of heparin conjugates and oral delivery systems. Some reports from the institute of the authors, such as those devoted to glycol-split heparins are also included. Among the members and derivatives of this class, several are undergoing clinical trials as antimetastatic and antimalarial agents and for the treatment of labour pain and severe hereditary anaemia. Other heparins, whose therapeutic targets are non-anticoagulant such as nephropathies, retinopathies and cystic fibrosis are also under investigation.

PMID: 27264866 [PubMed - indexed for MEDLINE]

LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning.

Bioinformatics. 2017 Apr 15;33(8):1187-1196

Authors: Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z

Abstract
Motivation: : Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem.
Results: : In this research, we present a novel method for indication prediction of both new drugs and approved drugs. This method is based on Laplacian regularized sparse subspace learning (LRSSL), which integrates drug chemical information, drug target domain information and target annotation information. Experimental results show that the proposed method outperforms several recent approaches for predicting drug-disease associations. Some drug therapeutic effects predicted by the method could be validated by database records or literatures. Moreover, with L1-norm constraint, important drug features have been extracted from multiple drug feature profiles. Case studies suggest that the extracted drug features could be beneficial to interpretation of the predicted results.
Availability and Implementation: https://github.com/LiangXujun/LRSSL.
Contact: proteomics@csu.edu.cn.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28096083 [PubMed - indexed for MEDLINE]

Repurposing Drugs for Cognition in Schizophrenia.

Clin Pharmacol Ther. 2017 Feb;101(2):191-193

Authors: Yang YS, Marder SR, Green MF

Abstract
Currently approved treatments for schizophrenia only minimally affect the cognitive features of the illness that are the most closely related to disability. Hence, there is now considerable effort to repurpose drugs for schizophrenia, and to seek agents that can improve cognition by targeting receptor systems other than the dopaminergic system. The results of these studies have been mixed thus far; however, this continues to be a high-priority area of schizophrenia research and an important unmet need.

PMID: 27706797 [PubMed - indexed for MEDLINE]

Low-dose methotrexate in myeloproliferative neoplasm models.

Haematologica. 2017 May 26;:

Authors: Chinnaiya K, Lawson MA, Thomas S, Haider MT, Down J, Chantry AD, Hughes D, Green A, Sayers JR, Snowden JA, Zeidler MP

PMID: 28550185 [PubMed - as supplied by publisher]

Drug repurposing to target proteostasis and prevent neurodegeneration: accelerating translational efforts.

Brain. 2017 Jun 01;140(6):1544-1547

Authors: Mercado G, Hetz C

PMID: 28549133 [PubMed - in process]

Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Artif Intell Med. 2017 Mar;77:53-63

Authors: Yu L, Zhao J, Gao L

Abstract
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks remain largely incomplete and the relationships between drugs and diseases conform to the triangularly balanced structure. Therefore, based on tissue specificities of diseases, we apply the triangularly balanced theory and the module distance defined for incomplete interaction networks to build drug-disease associations. Our method is named as TTMD (Tissue specificity, Triangle balance theory and Module Distance). Firstly, we combine three different drug similarity networks. Then, in the tissue-specific PPI network of a disease, we calculate its similarities with drugs using module distance. Finally, breast cancer and hepatocellular carcinoma (HCC) are taken as case studies. In the top-5% of predicted associations, 96.9% and 90.3% results match with known associations in Comparative Toxicogenomics Database (CTD) for breast cancer and hepatocellular carcinoma respectively. Clinical verification, literature mining and KEGG pathways enrichment analysis are further conducted for the top-5% newly predicted associations. Overall, TTMD is an effective approach for predicting new drug indications for tissue-specific diseases and provides potential values for the treatments of complex diseases.

PMID: 28545612 [PubMed - in process]

AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

PLoS One. 2017;12(5):e0178347

Authors: Fang J, Wang L, Li Y, Lian W, Pang X, Wang H, Yuan D, Wang Q, Liu AL, Du GH

Abstract
Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

PMID: 28542505 [PubMed - in process]

Drug target prediction by multi-view low rank embedding.

IEEE/ACM Trans Comput Biol Bioinform. 2017 May 18;:

Authors: Li L, Cai M

Abstract
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has been shown that integrating multi-view representations of drugs and proteins can strengthen the prediction ability. For example, a drug can be represented by its chemical structure, or by its chemical response in different cells. A protein can be represented by its sequence, or by its gene expression values in different cells. The docking of drugs and proteins based on their structure can be considered as one view (structural view), and the chemical performance of them based on gene expression and drug response can be considered as another view (chemical view). In this work, we first propose a single-view approach of SLRE based on low rank embedding for an arbitrary view, and then extend it to a multi-view approach of MLRE, which could integrate both views. Our experiments show that our methods perform significantly better than baseline methods including single-view methods and multi-view methods. We finally report predicted drug target interactions for 30 FDA-approved drugs.

PMID: 28541222 [PubMed - as supplied by publisher]

Network mirroring for drug repositioning.

BMC Med Inform Decis Mak. 2017 May 18;17(Suppl 1):55

Authors: Park S, Lee DG, Shin H

Abstract
BACKGROUND: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in 'Drug Repositioning' where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions. Given the amount of drugs and diseases, however, the one-by-one procedure may be time consuming and expensive.
METHODS: In this study, we propose a machine learning based approach for efficiently selecting candidate diseases and drugs. We assume that if two diseases are similar, then a drug for one disease can be effective against the other disease too. For the procedure, we first construct two disease networks; one with disease-protein association and the other with disease-drug information. If two networks are dissimilar, in a sense that the edge distribution of a disease node differ, it indicates high potential for repositioning new candidate drugs for that disease. The Kullback-Leibler divergence is employed to measure difference of connections in two constructed disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked diseases.
RESULTS: The results showed that F-measure of the proposed method was 0.75, outperforming 0.5 of greedy searching for the entire diseases. For the utility of the proposed method, it was applied to dementia and verified 75% accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia.
CONCLUSION: This research has novelty in that it discovers drugs with high potential of repositioning based on disease networks with the quantitative measure. Through the study, it is expected to produce profound insights for possibility of undiscovered drug repositioning.

PMID: 28539121 [PubMed - in process]

Chromone as a privileged scaffold in drug discovery - recent advances.

J Med Chem. 2017 May 24;:

Authors: Reis J, Gaspar A, Milhazes N, Borges FM

Abstract
The use of privileged structures in drug discovery has proven to be an effective strategy allowing the generation of innovative hits/leads and successful optimization processes. Chromone is actually recognized as a privileged structure and a valid template for the design of novel compounds with potential pharmacological interest, particularly in the field of neurodegenerative, inflammatory and infectious diseases as well as diabetes and cancer. Within this framework, this review provides the reader with a literature update following the preceding article entitled Chromone: a valid scaffold in Medicinal Chemistry. The review is mainly focused on the biological interest of chromones, including those isolated from natural sources. Moreover, as drug repurposing is becoming an attractive drug discovery approach, the opening repurposing studies on chromone-based drugs are also reported.

PMID: 28537720 [PubMed - as supplied by publisher]