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]

How Reliable Are Ligand-Centric Methods for Target Fishing?

Front Chem. 2016;4:15

Authors: Peón A, Dang CC, Ballester PJ

Abstract
Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict. The benchmark and a simple target prediction method to use as a performance baseline are available at http://ballester.marseille.inserm.fr/TF-benchmark.tar.gz.

PMID: 27148522 [PubMed]

Chloroquine-containing compounds: a patent review (2010 - 2014).

Expert Opin Ther Pat. 2015;25(9):1003-24

Authors: Njaria PM, Okombo J, Njuguna NM, Chibale K

Abstract
INTRODUCTION: Chloroquine (CQ) has been well known for its antimalarial effects since World War II. However, it is gradually being phased out from clinical use against malaria due to emergence of CQ-resistant Plasmodium falciparum strains. Besides low cost and tolerability, ongoing research has revealed interesting biochemical properties of CQ that have inspired its repurposing/repositioning in the management of various infectious/noninfectious diseases. Consequently, several novel compounds and compositions based on its scaffold have been studied and patented.
AREAS COVERED: In this review, patents describing CQ and its derivatives/compositions over the last 5 years are analyzed. The review highlights the rationale, chemical structures, biological evaluation and potential therapeutic application of CQ, its derivatives and compositions.
EXPERT OPINION: Repurposing efforts have dominantly focused on racemic CQ with no studies exploring the effect of the (R) and (S) enantiomers, which might potentially have additional benefits in other diseases. Additionally, evaluating other similarly acting antimalarials in clinical use and structural analogs could help maximize the intrinsic value of the 4-aminoquinolines. With regard to cancer therapy, successful repurposing of CQ-containing compounds will require linking the mode of action of these antimalarials with the signaling pathways that drive cancer cell proliferation to facilitate the development of a 4-amino-7-chloroquinoline that can be used as a synergistic partner in anticancer combination chemotherapy.

PMID: 26013494 [PubMed - indexed for MEDLINE]