Effective Management of Advanced Angiosarcoma by the Synergistic Combination of Propranolol and Vinblastine-based Metronomic Chemotherapy: A Bench to Bedside Study.

EBioMedicine. 2016 Apr;6:87-95

Authors: Pasquier E, André N, Street J, Chougule A, Rekhi B, Ghosh J, Philip DS, Meurer M, MacKenzie KL, Kavallaris M, Banavali SD

Abstract
BACKGROUND: Angiosarcomas are rare malignant tumors of vascular origin that represent a genuine therapeutic challenge. Recently, the combination of metronomic chemotherapy and drug repositioning has been proposed as an attractive alternative for cancer patients living in developing countries.
METHODS: In vitro experiments with transformed endothelial cells were used to identify synergistic interactions between anti-hypertensive drug propranolol and chemotherapeutics. This led to the design of a pilot treatment protocol combining oral propranolol and metronomic chemotherapy. Seven consecutive patients with advanced/metastatic/recurrent angiosarcoma were treated with this combination for up to 12months, followed by propranolol-containing maintenance therapy.
FINDINGS: Gene expression analysis showed expression of ADRB1 and ADRB2 adrenergic receptor genes in transformed endothelial cells and in angiosarcoma tumors. Propranolol strongly synergized with the microtubule-targeting agent vinblastine in vitro, but only displayed additivity or slight antagonism with paclitaxel and doxorubicin. A combination treatment using bi-daily propranolol (40mg) and weekly metronomic vinblastine (6mg/m(2)) and methotrexate (35mg/m(2)) was designed and used in 7 patients with advanced angiosarcoma. Treatment was well tolerated and resulted in 100% response rate, including 1 complete response and 3 very good partial responses, based on RECIST criteria. Median progression-free and overall survival was 11months (range 5-24) and 16months (range 10-30), respectively.
INTERPRETATION: Our results provide a strong rationale for the combination of β-blockers and vinblastine-based metronomic chemotherapy for the treatment of advanced angiosarcoma. Furthermore, our study highlights the potential of drug repositioning in combination with metronomic chemotherapy in low- and middle-income country setting.
FUNDING: This study was funded by institutional and philanthropic grants.

PMID: 27211551 [PubMed - in process]

Drug repositioning in sarcomas and other rare tumors.

EBioMedicine. 2016 Apr;6:4-5

Authors: Lee AT, Huang PH, Pollack SM, Jones RL

PMID: 27211532 [PubMed - in process]

DNetDB: The human disease network database based on dysfunctional regulation mechanism.

BMC Syst Biol. 2016;10(1):36

Authors: Yang J, Wu SJ, Yang SY, Peng JW, Wang SN, Wang FY, Song YX, Qi T, Li YX, Li YY

Abstract
Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.

PMID: 27209279 [PubMed - as supplied by publisher]

Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data.

Mol Pharm. 2016 May 20;

Authors: Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A

Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7 and PC-3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. When applied to normalized gene expression data for "landmark genes," DNN showed cross-validation mean F1 scores of 0.397, 0.285 and 0.234 on 3-, 5- and 12-category classification problems, respectively. At the pathway level DNN performed best with cross-validation mean F1 scores of 0.701, 0.596 and 0.546 on the same tasks. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

PMID: 27200455 [PubMed - as supplied by publisher]

An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.

PLoS One. 2016;11(5):e0155811

Authors: Mullen J, Cockell SJ, Woollard P, Wipat A

Abstract
Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.

PMID: 27196054 [PubMed - as supplied by publisher]

'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities.

Database (Oxford). 2016;2016

Authors: Moosavinasab S, Patterson J, Strouse R, Rastegar-Mojarad M, Regan K, Payne PR, Huang Y, Lin SM

Abstract
The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce 'RE:fine Drugs', a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. 'RE:fine Drugs' demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug-Gene-Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.Database URL: http://drug-repurposing.nationwidechildrens.org.

PMID: 27189611 [PubMed - as supplied by publisher]

Learning disease relationships from clinical drug trials.

J Am Med Inform Assoc. 2016 May 17;

Authors: Haslam B, Perez-Breva L

Abstract
OBJECTIVE: Our objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge that can be accessed to draw conclusions about the underlying biology of diseases. We seek to demonstrate that this latent information can be uncovered from the whole body of clinical trials.
MATERIALS AND METHODS: We extract free-text metadata from 93 654 clinical drug trials and introduce a representation that allows us to compare different trials. We then construct a network of diseases using only the trial metadata. We view each trial as the summation of expert knowledge of biological mechanisms and medical evidence linking a disease to a drug believed to modulate the pathways of that disease. Our network representation allows us to visualize disease relationships based on this underlying information.
RESULTS: Our disease network shows surprising agreement with another disease network based on genetic data and on the Medical Subject Headings (MeSH) taxonomy, yet also contains unique disease similarities.
DISCUSSION AND CONCLUSION: The agreement of our results with other sources indicates that our premise regarding latent expert knowledge holds. The disease relationships unique to our network may be used to generate hypotheses for future biological and clinical research as well as drug repurposing and design. Our results provide an example of using experimental data on humans to generate biologically useful information and point to a set of new and promising strategies to link clinical outcomes data back to biological research.

PMID: 27189012 [PubMed - as supplied by publisher]

Enhancing the enrichment of pharmacophore-based target prediction for the polypharmacological profiles of drugs.

J Chem Inf Model. 2016 May 17;

Authors: Wang X, Pan C, Gong J, Liu X, Li H

Abstract
PharmMapper is a web server for drug targets identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effects risk evaluation. But due to the inherent non-discriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit scores matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit scores distribution of the background database, thus enables estimating the probability of finding a given target by random with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed and confirmed the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reactions (ADRs) related off-targets.

PMID: 27187084 [PubMed - as supplied by publisher]

Using reverse docking for target identification and its applications for drug discovery.

Expert Opin Drug Discov. 2016 May 17;

Authors: Lee A, Lee K, Kim D

Abstract
Introduction In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. Areas covered In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. Expert opinion There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.

PMID: 27186904 [PubMed - as supplied by publisher]

Systems Medicine approaches to improving understanding, treatment, and clinical management of Neuroendocrine Prostate Cancer.

Curr Pharm Des. 2016 May 13;

Authors: Yadav KK, Khader S, Readhead B, Yadav SS, Li L, Kasarksis A, Tewari AK, Dudley JT

Abstract
BACKGROUND: Prostate cancer is the most commonly diagnosed cancer in men. More than 200,000 new cases are added each year in the US, translating to a lifetime risk of 1 in 7 men. Neuroendocrine prostate cancer (NEPC) is an aggressive and treatment-resistant form of prostate cancer. A subset of patients treated with aggressive androgen deprivation therapy (ADT) present with NEPC. Patients with NEPC have a reduced 5-year overall survival rate of 12.6%. Knowledge integration from genetic, epigenetic, biochemical and therapeutic studies suggests NEPC as an indicative mechanism of resistance development to various forms of therapy.
METHODS: In this perspective, we discuss various experimental, computational and risk prediction methodologies that can be utilized to identify novel therapies against NEPC. We reviewed literature from PubMed using key terms, and computationally analyzed publicly available genomics data to present different possibilities for developing systems medicine based therapeutic and curative models to understand and target prostate cancer and specifically NEPC.
RESULTS: Our study includes gene-set analyses, network analyses, genomics and phenomics aided drug development, microRNA and peptide-based therapeutics, pathway modeling, drug repositioning and cancer immunotherapies. We also discuss the application of cancer risk estimations and mining of electronic medical records to develop personalized risk predictions models for NEPC. Preemptive stratification of patients who are at risk of evolving NEPC phenotypes using predictive models could also help to design and deliver better therapies.
CONCLUSIONS: Collectively, understanding the mechanism of NEPC evolution from prostate cancer using systems biology approaches would help in devising better treatment strategies and is critical and unmet clinical need.

PMID: 27174811 [PubMed - as supplied by publisher]

Toward Repositioning Niclosamide for Antivirulence Therapy of Pseudomonas aeruginosa Lung Infections: Development of Inhalable Formulations through Nanosuspension Technology.

Mol Pharm. 2015 Aug 3;12(8):2604-17

Authors: Costabile G, d'Angelo I, Rampioni G, Bondì R, Pompili B, Ascenzioni F, Mitidieri E, d'Emmanuele di Villa Bianca R, Sorrentino R, Miro A, Quaglia F, Imperi F, Leoni L, Ungaro F

Abstract
Inhaled antivirulence drugs are currently considered a promising therapeutic option to treat Pseudomonas aeruginosa lung infections in cystic fibrosis (CF). We have recently shown that the anthelmintic drug niclosamide (NCL) has strong quorum sensing (QS) inhibiting activity against P. aeruginosa and could be repurposed as an antivirulence drug. In this work, we developed dry powders containing NCL nanoparticles that can be reconstituted in saline solution to produce inhalable nanosuspensions. NCL nanoparticles were produced by high-pressure homogenization (HPH) using polysorbate 20 or polysorbate 80 as stabilizers. After 20 cycles of HPH, all formulations showed similar properties in the form of needle-shape nanocrystals with a hydrodynamic diameter of approximately 450 nm and a zeta potential of -20 mV. Nanosuspensions stabilized with polysorbate 80 at 10% w/w to NCL (T80_10) showed an optimal solubility profile in simulated interstitial lung fluid. T80_10 was successfully dried into mannitol-based dry powder by spray drying. Dry powder (T80_10 DP) was reconstituted in saline solution and showed optimal in vitro aerosol performance. Both T80_10 and T80_10 DP were able to inhibit P. aeruginosa QS at NCL concentrations of 2.5-10 μM. NCL, and these formulations did not significantly affect the viability of CF bronchial epithelial cells in vitro at microbiologically active concentrations (i.e., ≤10 μM). In vivo acute toxicity studies in rats confirmed no observable toxicity of the NCL T80_10 DP formulation upon intratracheal administration at a concentration 100-fold higher than the anti-QS activity concentration. These preliminary results suggest that NCL repurposed in the form of inhalable nanosuspensions has great potential for the local treatment of P. aeruginosa lung infections as in the case of CF patients.

PMID: 25974285 [PubMed - indexed for MEDLINE]

Unexploited Antineoplastic Effects of Commercially Available Anti-Diabetic Drugs.

Pharmaceuticals (Basel). 2016;9(2)

Authors: Papanagnou P, Stivarou T, Tsironi M

Abstract
The development of efficacious antitumor compounds with minimal toxicity is a hot research topic. Numerous cancer cell targeted agents are evaluated daily in laboratories for their antitumorigenicity at the pre-clinical level, but the process of their introduction into the market is costly and time-consuming. More importantly, even if these new antitumor agents manage to gain approval, clinicians have no former experience with them. Accruing evidence supports the idea that several medications already used to treat pathologies other than cancer display pleiotropic effects, exhibiting multi-level anti-cancer activity and chemosensitizing properties. This review aims to present the anticancer properties of marketed drugs (i.e., metformin and pioglitazone) used for the management of diabetes mellitus (DM) type II. Mode of action, pre-clinical in vitro and in vivo or clinical data as well as clinical applicability are discussed here. Given the precious multi-year clinical experience with these non-antineoplastic drugs their repurposing in oncology is a challenging alternative that would aid towards the development of therapeutic schemes with less toxicity than those of conventional chemotherapeutic agents. More importantly, harnessing the antitumor function of these agents would save precious time from bench to bedside to aid the fight in the arena of cancer.

PMID: 27164115 [PubMed - as supplied by publisher]

Polypharmacology in Drug Development: A Minireview of Current Technologies.

ChemMedChem. 2016 May 6;

Authors: Tan Z, Chaudhai R, Zhang S

Abstract
Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.

PMID: 27154144 [PubMed - as supplied by publisher]

Drug repositioning based on comprehensive similarity measures and Bi-Random Walk algorithm.

Bioinformatics. 2016 May 5;

Authors: Luo H, Wang J, Li M, Luo J, Peng X, Wu FX, Pan Y

Abstract
MOTIVATION: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity respectively, while not taking into account the influence of known drug-disease association information on the similarity measures.
RESULTS: In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug-disease interactions. Based on the drug-disease heterogeneous network, Bi-Random walk algorithm is adopted to predict novel potential drug-disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically.
AVAILABILITY: http://github.com//bioinfomaticsCSU/MBiRW CONTACT: jxwang@mail.csu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27153662 [PubMed - as supplied by publisher]

Drug Repurposing for Terminal-Stage Cancer Patients.

Am J Public Health. 2016 Jun;106(6):e3

Authors: Cvek B

PMID: 27153031 [PubMed - as supplied by publisher]

Repurposing drugs for treatment of tuberculosis: a role for non-steroidal anti-inflammatory drugs.

Br Med Bull. 2016 May 5;

Authors: Maitra A, Bates S, Shaik M, Evangelopoulos D, Abubakar I, McHugh TD, Lipman M, Bhakta S

Abstract
INTRODUCTION: The number of cases of drug-resistant Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has risen rapidly in recent years. This has led to the resurgence in repurposing existing drugs, such as non-steroidal anti-inflammatory drugs (NSAIDs), for anti-TB treatment.
SOURCES OF DATA: Evidence from novel drug screening in vitro, in vivo, pharmacokinetic/pharmacodynamics analyses and clinical trials has been used for the preparation of this systematic review of the potential of NSAIDs for use as an adjunct in new TB chemotherapies.
AREAS OF AGREEMENT: Certain NSAIDs have demonstrated inhibitory properties towards actively replicating, dormant and drug-resistant clinical isolates of M. tuberculosis cells.
AREAS OF CONTROVERSY: NSAIDs are a diverse class of drugs, which have reported off-target activities, and their endogenous antimicrobial mechanism(s) of action is still unclear.
GROWING POINTS: It is essential that clinical trials of NSAIDs continue, in order to assess their suitability for addition to the current TB treatment regimen. Repurposing molecules such as NSAIDs is a vital, low-risk strategy to combat the trend of rapidly increasing antibiotic resistance.

PMID: 27151954 [PubMed - as supplied by publisher]

Management and Treatment of Dengue and Chikungunya - Natural Products to the Rescue.

Comb Chem High Throughput Screen. 2016 May 6;

Authors: Suroowan S, Mahomoodally F, Ragoo L

Abstract
Neglected tropical diseases (NTDs) flourish mostly in impoverished developing nations of the world. It is estimated that NTDs plague up to 1 billion people every year thereby inducing a massive economic and health burden worldwide. Following explosive outbreaks mostly in Asia, Latin America, Europe and the Indian Ocean, two common NTDs namely, Chikungunya and Dengue both transmitted by an infected mosquito vector principally Aedes aegypti have emerged as a major public health threat. Given the limitations of conventional medicine in specifically targeting the Chikungunya and Dengue virus (CHIKV and DENV), natural products present an interesting avenue to explore in the quest of developing novel anti; mosquito, CHIKV and DENV agents. In this endeavor, a number of plant extracts, isolated compounds, essential oils and seaweeds have shown promising larvicidal and insecticidal activity against some mosquito vectors as well as anti CHIKV and DENV activity in-vitro. Other natural products that have depicted good potential against these diseases include; the symbiotic bacterial genus Wolbachia which can largely reduce the life span and infectivity of mosquito vectors and the marine Cyanobacterium Trichodesmium erythraeum which has shown anti-CHIKV activity at minimal cytotoxic level. The impetus of modern drug discovery approaches such as high throughput screening, drug repositioning, synthesis and computer-aided drug design will undeniably enhance the process of developing more stable lead molecules from natural products which have shown promising antiviral activity in-vitro.

PMID: 27151484 [PubMed - as supplied by publisher]

DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing.

BMC Bioinformatics. 2016;17(1):202

Authors: Issa NT, Kruger J, Wathieu H, Raja R, Byers SW, Dakshanamurthy S

Abstract
BACKGROUND: The targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation.
RESULTS: We present a systems polypharmacology platform entitled DrugGenEx-Net (DGE-NET). DGE-NET predicts empirical drug-target (DT) interactions, integrates interaction pairs into a multi-tiered network analysis, and ultimately predicts disease-specific drug polypharmacology through systems-based gene expression analysis. Incorporation of established biological network annotations for protein target-disease, -signaling pathway, -molecular function, and protein-protein interactions enhances predicted DT effects on disease pathophysiology. Over 50 drug-disease and 100 drug-pathway predictions are validated. For example, the predicted systems pharmacology of the cholesterol-lowering agent ezetimibe corroborates its potential carcinogenicity. When disease-specific gene expression analysis is integrated, DGE-NET prioritizes known therapeutics/experimental drugs as well as their contra-indications. Proof-of-concept is established for immune-related rheumatoid arthritis and inflammatory bowel disease, as well as neuro-degenerative Alzheimer's and Parkinson's diseases.
CONCLUSIONS: DGE-NET is a novel computational method that predicting drug therapeutic and counter-therapeutic indications by uniquely integrating systems pharmacology with gene expression analysis. DGE-NET correctly predicts various drug-disease indications by linking the biological activity of drugs and diseases at multiple tiers of biological action, and is therefore a useful approach to identifying drug candidates for re-purposing.

PMID: 27151405 [PubMed - in process]

Sharpening nature's tools for efficient tuberculosis control: A review of the potential role and development of host-directed therapies and strategies for targeted respiratory delivery.

Adv Drug Deliv Rev. 2016 May 2;

Authors: O'Connor G, Gleeson LE, Fagan-Murphy A, Cryan SA, O'Sullivan MP, Keane J

Abstract
Centuries since it was first described, tuberculosis (TB) remains a significant global public health issue. Despite ongoing holistic measures implemented by health authorities and a number of new oral treatments reaching the market, there is still a need for an advanced, efficient TB treatment. An adjunctive, host-directed therapy designed to enhance endogenous pathways and hence compliment current regimens could be the answer. The integration of drug repurposing, including synthetic and naturally occurring compounds, with a targeted drug delivery platform is an attractive development option. In order for a new anti-tubercular treatment to be produced in a timely manner, a multidisciplinary approach should be taken from the outset, including stakeholders from academia, the pharmaceutical industry, and regulatory bodies keeping the patient as the key focus. Pre-clinical considerations for the development of a targeted host-directed therapy are discussed here.

PMID: 27151307 [PubMed - as supplied by publisher]

Inhibition of Cholesterol Esterification in the Adrenal Gland by ATR101/PD132301-2, A Promising Case of Drug Repurposing.

Endocrinology. 2016 May;157(5):1719-1721

Authors: Kroiss M, Fassnacht M

PMID: 27149038 [PubMed - as supplied by publisher]