Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.

Gastroenterology. 2016 Oct 20;:

Authors: Wooden B, Goossens N, Hoshida Y, Friedman SL

Abstract
Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data, from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiologic function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biologic processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology, and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology, to complement traditional means of diagnostic and therapeutic discovery.

PMID: 27773806 [PubMed - as supplied by publisher]

PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT.

Pac Symp Biocomput. 2016;21:321-32

Authors: Gligorijević V, Malod-Dognin N, Pržulj N

Abstract
According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets.We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation.

PMID: 26776197 [PubMed - indexed for MEDLINE]

Identification of Potential Therapeutics to Conquer Drug Resistance in Salmonella typhimurium: Drug Repurposing Strategy.

BioDrugs. 2016 Oct 19;

Authors: Preethi B, Shanthi V, Ramanathan K

Abstract
BACKGROUND: Salmonella typhimurium is the main cause of gastrointestinal illness in humans, and treatment options are decreasing because drug-resistant strains have emerged.
OBJECTIVE: The objective of this study was to use computational drug repurposing to identify a novel candidate with an effective mechanism of action to circumvent the drug resistance.
METHODS: We used the Mantra 2.0 database to initially screen drug candidates that share similar gene expression profiles to those of quinolones. Data were further reduced using pharmacophore mapping theory. Finally, we employed molecular-simulation studies to calculate the binding affinity of the screened candidates with DNA gyrase, alongside an analysis of side effects.
RESULTS: A total of 16 drug candidates from the Mantra 2.0 database were screened. The pharmacophoric features of the screened candidates were examined and nalidixic acid features compared using the PharamGist program. A total of 11 compounds with the highest pharmacophore score were considered for binding energy calculation. Finally, we analysed the side effects of the eight drug candidates that showed significant binding affinity in the simulation study.
CONCLUSION: Overall, flufenamic acid and sulconazole may be potential drug candidates that could be studied in vitro to assess their resistance profile against Salmonella enterica Typhimurium.

PMID: 27761807 [PubMed - as supplied by publisher]

Identifying and Tackling Emergent Vulnerability in Drug-Resistant Mycobacteria.

ACS Infect Dis. 2016 Sep 9;2(9):592-607

Authors: Padiadpu J, Baloni P, Anand K, Munshi M, Thakur C, Mohan A, Singh A, Chandra N

Abstract
The global mechanisms and associated molecular alterations that occur in drug-resistant mycobacteria are poorly understood. To address this, we obtain genomics data and then construct a genome-scale response network in isoniazid-resistant Mycobacterium smegmatis and apply a network-mining algorithm. Through this, we decipher global alterations in an unbiased manner and identify emergent vulnerabilities in resistant bacilli, of which redox response was prominent. Using phenotypic profiling, we find that resistant bacilli exhibit collateral sensitivity to several compounds that block antioxidant responses. We find that nanogram/milliliter concentrations of ebselen, vancomycin, and phenylarsine oxide, in combination with isoniazid, are highly effective against Mycobacterium tuberculosis H37Rv and three clinical drug-resistant strains. Dynamic measurements of cytoplasmic redox potential revealed a surprisingly diminished capacity of clinical drug-resistant strains to counteract oxidative stress, providing a mechanistic basis for efficient and synergistic mycobactericidal activity of the drug combinations. Ebselen and vancomycin appear to be promising repurposable drugs.

PMID: 27759382 [PubMed - in process]

Drug Repositioning in Inflammatory Bowel Disease Based on Genetic Information.

Inflamm Bowel Dis. 2016 Nov;22(11):2562-2570

Authors: Collij V, Festen EA, Alberts R, Weersma RK

Abstract
BACKGROUND: Currently, 200 genetic risk loci have been identified for inflammatory bowel disease (IBD). Although these findings have significantly advanced our insight into IBD biology, there has been little progress in translating this knowledge toward clinical practice, like more cost-efficient drug development. Our aim was to use genetic knowledge to identify drugs that warrant further investigation in IBD treatment.
METHODS: We hypothesized that proteins encoded by IBD candidate genes are potential IBD drug targets because genetic information can increase successful drug identification. We identified drugs that target the proteins encoded by IBD candidate genes using the DrugBank. We included proteins that are in direct protein-protein interaction with proteins encoded by IBD risk genes. Promising potential IBD drugs were selected based on a manual literature search of all identified drugs (PubMed, ClinicalTrials.gov).
RESULTS: We have identified 113 drugs that could potentially be used in IBD treatment. Fourteen are known IBD drugs, 48 drugs have been, or are being investigated in IBD, 19 are being used or being investigated in other inflammatory disorders treatment, and 32 are investigational new drugs that have not yet been registered for clinical use.
CONCLUSIONS: We confirm that proteins encoded by IBD candidate genes are targeted by approved IBD therapies. Furthermore, we show that Food and Drug Administration-approved drugs could possibly be repositioned for IBD treatment. We also identify investigational new drugs that warrant further investigation for IBD treatment. Incorporating this process in IBD drug development will improve the utilization of genetic data and could lead to the improvement of IBD treatment.

PMID: 27753694 [PubMed - in process]

Doxycycline is an NF-κB inhibitor that induces apoptotic cell death in malignant T-cells.

Oncotarget. 2016 Oct 6;:

Authors: Alexander-Savino CV, Hayden MS, Richardson C, Zhao J, Poligone B

Abstract
Cutaneous T-cell Lymphoma (CTCL) is a rare non-Hodgkin's lymphoma that can affect the skin, blood, and lymph nodes, and can metastasize at late stages. Novel therapies that target all affected disease compartments and provide longer lasting responses while being safe are needed. One potential therapeutic target is NF-κB, a regulator of immune responses and an important participant in carcinogenesis and cancer progression. As a transcription factor, NF-κB targets genes that promote cell proliferation and survival. Constitutive or aberrant activation of NF-κB is encountered in many types of cancer, including CTCL.Recently, while analyzing gene-expression profiles of a variety of small molecule compounds that target NF-κB, we discovered the tetracycline family of antibiotics, including doxycycline, to be potent inhibitors of the NF-κB pathway. Doxycycline is well-tolerated, safe, and inexpensive; and is commonly used as an antibiotic and anti-inflammatory for the treatment a multitude of medical conditions.In our current study, we show that doxycycline induces apoptosis in a dose dependent manner in multiple different cell lines from patients with the two most common subtypes of CTCL, Mycosis Fungoides (MF) and Sézary Syndrome (SS). Similar results were found using primary CD4+ T cells from a patient with SS. Doxycycline inhibits TNF induced NF-κB activation and reduces expression of NF-κB dependent anti-apoptotic proteins, such as BCL2α. Furthermore, we have identified that doxycycline induces apoptosis through reactive oxygen species.

PMID: 27732942 [PubMed - as supplied by publisher]

Identifying candidate agents for lung adenocarcinoma by walking the human interactome.

Onco Targets Ther. 2016;9:5439-5450

Authors: Sun Y, Zhang R, Jiang Z, Xia R, Zhang J, Liu J, Chen F

Abstract
Despite recent advances in therapeutic strategies for lung cancer, mortality is still increasing. Therefore, there is an urgent need to identify effective novel drugs. In the present study, we implement drug repositioning for lung adenocarcinoma (LUAD) by a bioinformatics method followed by experimental validation. We first identified differentially expressed genes between LUAD tissues and nontumor tissues from RNA sequencing data obtained from The Cancer Genome Atlas database. Then, candidate small molecular drugs were ranked according to the effect of their targets on differentially expressed genes of LUAD by a random walk with restart algorithm in protein-protein interaction networks. Our method identified some potentially novel agents for LUAD besides those that had been previously reported (eg, hesperidin). Finally, we experimentally verified that atracurium, one of the potential agents, could induce A549 cells death in non-small-cell lung cancer-derived A549 cells by an MTT assay, acridine orange and ethidium bromide staining, and electron microscopy. Furthermore, Western blot assays demonstrated that atracurium upregulated the proapoptotic Bad and Bax proteins, downregulated the antiapoptotic p-Bad and Bcl-2 proteins, and enhanced caspase-3 activity. It could also reduce the expression of p53 and p21(Cip1/Waf1) in A549 cells. In brief, the candidate agents identified by our approach may provide greater insights into improving the therapeutic status of LUAD.

PMID: 27729798 [PubMed - in process]

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

PLoS Comput Biol. 2016 Oct;12(10):e1005135

Authors: Lim H, Poleksic A, Yao Y, Tong H, He D, Zhuang L, Meng P, Xie L

Abstract
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

PMID: 27716836 [PubMed - in process]

Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles.

J Chem Inf Model. 2015 Dec 28;55(12):2705-16

Authors: Iwata H, Sawada R, Mizutani S, Kotera M, Yamanishi Y

Abstract
The identification of beneficial drug combinations is a challenging issue in pharmaceutical and clinical research toward combinatorial drug therapy. In the present study, we developed a novel computational method for large-scale prediction of beneficial drug combinations using drug efficacy and target profiles. We designed an informative descriptor for each drug-drug pair based on multiple drug profiles representing drug-targeted proteins and Anatomical Therapeutic Chemical Classification System codes. Then, we constructed a predictive model by learning a sparsity-induced classifier based on known drug combinations from the Orange Book and KEGG DRUG databases. Our results show that the proposed method outperforms the previous methods in terms of the accuracy of high-confidence predictions, and the extracted features are biologically meaningful. Finally, we performed a comprehensive prediction of novel drug combinations for 2,639 approved drugs, which predicted 142,988 new potentially beneficial drug-drug pairs. We showed several examples of successfully predicted drug combinations for a variety of diseases.

PMID: 26624799 [PubMed - indexed for MEDLINE]

Structural Basis of Metallo-β-Lactamase Inhibition by Captopril Stereoisomers.

Antimicrob Agents Chemother. 2015 Oct 19;60(1):142-50

Authors: Brem J, van Berkel SS, Zollman D, Lee SY, Gileadi O, McHugh PJ, Walsh TR, McDonough MA, Schofield CJ

Abstract
β-Lactams are the most successful antibacterials, but their effectiveness is threatened by resistance, most importantly by production of serine- and metallo-β-lactamases (MBLs). MBLs are of increasing concern because they catalyze the hydrolysis of almost all β-lactam antibiotics, including recent-generation carbapenems. Clinically useful serine-β-lactamase inhibitors have been developed, but such inhibitors are not available for MBLs. l-Captopril, which is used to treat hypertension via angiotensin-converting enzyme inhibition, has been reported to inhibit MBLs by chelating the active site zinc ions via its thiol(ate). We report systematic studies on B1 MBL inhibition by all four captopril stereoisomers. High-resolution crystal structures of three MBLs (IMP-1, BcII, and VIM-2) in complex with either the l- or d-captopril stereoisomer reveal correlations between the binding mode and inhibition potency. The results will be useful in the design of MBL inhibitors with the breadth of selectivity required for clinical application against carbapenem-resistant Enterobacteriaceae and other organisms causing MBL-mediated resistant infections.

PMID: 26482303 [PubMed - indexed for MEDLINE]

Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells.

PeerJ. 2016;4:e2478

Authors: Huang CH, Ciou JS, Chen ST, Kok VC, Chung Y, Tsai JJ, Kurubanjerdjit N, Huang CF, Ng KL

Abstract
BACKGROUND: Abnormal proliferation of vascular smooth muscle cells (VSMC) is a major cause of cardiovascular diseases (CVDs). Many studies suggest that vascular injury triggers VSMC dedifferentiation, which results in VSMC changes from a contractile to a synthetic phenotype; however, the underlying molecular mechanisms are still unclear.
METHODS: In this study, we examined how VSMC responds under mechanical stress by using time-course microarray data. A three-phase study was proposed to investigate the stress-induced differentially expressed genes (DEGs) in VSMC. First, DEGs were identified by using the moderated t-statistics test. Second, more DEGs were inferred by using the Gaussian Graphical Model (GGM). Finally, the topological parameters-based method and cluster analysis approach were employed to predict the last batch of DEGs. To identify the potential drugs for vascular diseases involve VSMC proliferation, the drug-gene interaction database, Connectivity Map (cMap) was employed. Success of the predictions were determined using in-vitro data, i.e. MTT and clonogenic assay.
RESULTS: Based on the differential expression calculation, at least 23 DEGs were found, and the findings were qualified by previous studies on VSMC. The results of gene set enrichment analysis indicated that the most often found enriched biological processes are cell-cycle-related processes. Furthermore, more stress-induced genes, well supported by literature, were found by applying graph theory to the gene association network (GAN). Finally, we showed that by processing the cMap input queries with a cluster algorithm, we achieved a substantial increase in the number of potential drugs with experimental IC50 measurements. With this novel approach, we have not only successfully identified the DEGs, but also improved the DEGs prediction by performing the topological and cluster analysis. Moreover, the findings are remarkably validated and in line with the literature. Furthermore, the cMap and DrugBank resources were used to identify potential drugs and targeted genes for vascular diseases involve VSMC proliferation. Our findings are supported by in-vitro experimental IC50, binding activity data and clinical trials.
CONCLUSION: This study provides a systematic strategy to discover potential drugs and target genes, by which we hope to shed light on the treatments of VSMC proliferation associated diseases.

PMID: 27703845 [PubMed - in process]

Cystic fibrosis transmembrane conductance regulator modulators in cystic fibrosis: current perspectives.

Clin Pharmacol. 2016;8:127-140

Authors: Schmidt BZ, Haaf JB, Leal T, Noel S

Abstract
Mutations of the CFTR gene cause cystic fibrosis (CF), the most common recessive monogenic disease worldwide. These mutations alter the synthesis, processing, function, or half-life of CFTR, the main chloride channel expressed in the apical membrane of epithelial cells in the airway, intestine, pancreas, and reproductive tract. Lung disease is the most critical manifestation of CF. It is characterized by airway obstruction, infection, and inflammation that lead to fatal tissue destruction. In spite of great advances in early and multidisciplinary medical care, and in our understanding of the pathophysiology, CF is still considerably reducing the life expectancy of patients. This review highlights the current development in pharmacological modulators of CFTR, which aim at rescuing the expression and/or function of mutated CFTR. While only Kalydeco® and Orkambi® are currently available to patients, many other families of CFTR modulators are undergoing preclinical and clinical investigations. Drug repositioning and personalized medicine are particularly detailed in this review as they represent the most promising strategies for restoring CFTR function in CF.

PMID: 27703398 [PubMed - in process]

Drug repurposing for glioblastoma based on molecular subtypes.

J Biomed Inform. 2016 Sep 30;:

Authors: Chen Y, Xu R

Abstract
A recent multi-platform analysis by The Cancer Genome Atlas identified four distinct molecular subtypes for glioblastoma (GBM) and demonstrated that the subtypes correlate with clinical phenotypes and treatment responses. In this study, we developed a computational drug repurposing approach to predict GBM drugs based on the molecular subtypes. Our approach leverages the genomic signature for each GBM subtype, and integrates the human cancer genomics with mouse phenotype data to identify the opportunity of reusing the FDA-approved agents to treat specific GBM subtypes. Specifically, we first constructed the phenotype profile for each GBM subtype using their genomic signatures. For each approved drug, we also constructed a phenotype profile using the drug target genes. Then we developed an algorithm to match and prioritize drugs based on their phenotypic similarities to the GBM subtypes. Our approach is highly generalizable for other disorders if provided with a list of disorder-specific genes. We first evaluated the approach in predicting drugs for the whole GBM. For a combined set of approved, potential and off-label GBM drugs, we achieved a median rank of 9.3%, which is significantly higher (p<e(-7)) than 45.7% for a recent approach that also uses the mouse phenotype data. Then we applied the approach on GBM subtypes. Analysis result shows the variations of enriched pathways, associated phenotypes and prioritized drugs across different subtypes. We ranked the first-line chemotherapy for GBM in different positions for each subtypes, and the rank variation was consistent with the previous finding on different drug responses among subtypes. In summary, this study makes an effort towards translating the molecular stratification into better survival for GBM.

PMID: 27697594 [PubMed - as supplied by publisher]

Design of efficient computational workflows for in silico drug repurposing.

Drug Discov Today. 2016 Sep 27;:

Authors: Vanhaelen Q, Mamoshina P, Aliper AM, Artemov A, Lezhnina K, Ozerov I, Labat I, Zhavoronkov A

Abstract
Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability, and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based, and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.

PMID: 27693712 [PubMed - as supplied by publisher]

Drug Repurposing: New Treatments for Zika Virus Infection?

Trends Mol Med. 2016 Sep 27;:

Authors: Cheng F, Murray JL, Rubin DH

Abstract
To date, no antiviral agents have been approved for treating Zika virus (ZIKV) infection. Two recent drug-repurposing studies published in Cell Host & Microbe and Nature Medicine demonstrated that screening FDA-approved drugs for antiviral activity is a promising strategy for identifying therapeutics with novel activity against ZIKV infection.

PMID: 27692879 [PubMed - as supplied by publisher]

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics.

Expert Opin Drug Discov. 2016 Sep 30;

Authors: Ung MH, Varn FS, Cheng C

Abstract
INTRODUCTION: Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development.
AREAS COVERED: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis.
EXPERT OPINION: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

PMID: 27689915 [PubMed - as supplied by publisher]

Repurposing N,N'-bis-(arylamidino)-1,4-piperazinedicarboxamidines: An unexpected class of potent inhibitors of cholinesterases.

Eur J Med Chem. 2016 Sep 19;125:430-434

Authors: Loesche A, Wiese J, Sommerwerk S, Simon V, Brandt W, Csuk R

Abstract
Drug repurposing (=drug repositioning) is an effective way to cut costs for the development of new therapeutics and to reduce the time-to-market time-span. Following this concept a small library of compounds was screened for their ability to act as inhibitors of acetyl- and butyrylcholinesterase. Picloxydine, an established antiseptic, was shown to be an inhibitor for both enzymes. Systematic variation of the aryl substituents led to analogs possessing almost the same good properties as gold standard galantamine hydrobromide.

PMID: 27689726 [PubMed - as supplied by publisher]

Case-specific potentiation of glioblastoma drugs by pterostilbene.

Oncotarget. 2016 Sep 28;

Authors: Schmidt L, Baskaran S, Johansson P, Padhan N, Matuszewski D, Green LC, Elfineh L, Wee S, Häggblad M, Martens U, Westermark B, Forsberg-Nilsson K, Uhrbom L, Claesson-Welsh L, Andäng M, Sintorn IM, Lundgren B, Lönnstedt I, Krona C, Nelander S

Abstract
Glioblastoma multiforme (GBM, astrocytoma grade IV) is the most common malignant primary brain tumor in adults. Addressing the shortage of effective treatment options for this cancer, we explored repurposing of existing drugs into combinations with potent activity against GBM cells. We report that the phytoalexin pterostilbene is a potentiator of two drugs with previously reported anti-GBM activity, the EGFR inhibitor gefitinib and the antidepressant sertraline. Combinations of either of these two compounds with pterostilbene suppress cell growth, viability, sphere formation and inhibit migration in tumor GBM cell (GC) cultures. The potentiating effect of pterostilbene was observed to a varying degree across a panel of 41 patient-derived GCs, and correlated in a case specific manner with the presence of missense mutation of EGFR and PIK3CA and a focal deletion of the chromosomal region 1p32. We identify pterostilbene-induced cell cycle arrest, synergistic inhibition of MAPK activity and induction of Thioredoxin interacting protein (TXNIP) as possible mechanisms behind pterostilbene's effect. Our results highlight a nontoxic stilbenoid compound as a modulator of anticancer drug response, and indicate that pterostilbene might be used to modulate two anticancer compounds in well-defined sets of GBM patients.

PMID: 27689322 [PubMed - as supplied by publisher]

MeSHDD: Literature-based drug-drug similarity for drug repositioning.

J Am Med Inform Assoc. 2016 Sep 27;

Authors: Brown AS, Patel CJ

Abstract
OBJECTIVE: Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.
METHODS: We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.
RESULTS: We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.Conclusion Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/).

PMID: 27678460 [PubMed - as supplied by publisher]

A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL.

J Biomed Semantics. 2016;7(1):59

Authors: Mutowo P, Bento AP, Dedman N, Gaulton A, Hersey A, Lomax J, Overington JP

Abstract
BACKGROUND: The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases.
DESCRIPTION: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms. We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The 'ChEMBL protein target slim' provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab ( https://www.ebi.ac.uk/chembl/target/browser ). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website ( http://geneontology.org/page/go-slim-and-subset-guide ).
CONCLUSIONS: We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways.

PMID: 27678076 [PubMed - as supplied by publisher]