Exploiting Drug-disease relationships for computational drug repositioning

Stanford University, Stanford, CA, USA.
Briefings in Bioinformatics (Impact Factor: 9.62). 06/2011; 12(4):303-11. DOI: 10.1093/bib/bbr013
Source: PubMed


Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

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    • "One may also focus on the prediction of drug-target associations, with the hope that hypothesised links generated from domain knowledge will allow us to complete a drug-target-disease pathway and infer a novel use for an existing drug. As well as highlighting potential drug repositioning opportunities, D-T interaction identification also allows potential adverse side effects to be analysed (Fakhraei et al., 2014;Dudley et al., 2011). In vitro approaches to identifying D-T interactions are no different to other aspects of drug development and remain costly and time consuming (Ding et al., 2014). "
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    ABSTRACT: Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.
    Full-text · Article · Jan 2016 · PeerJ
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    • "Secondly, the experimental process requires enormous time and cost to retain enough expression data to use in research. Clinical information may provide new opportunities to directly connect chemicals to clinical therapeutic effects in complex physiological systems because clinical information not only indicates the phenotypic states of disease-conditions, but also reflects the end-physiological results of chemical impacts on human biological activity[13,14]. One of the strategies for drug repositioning with clinical information is the side-effects-based approach[15,16]. "
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    ABSTRACT: Background: Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems. Methods: Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug's effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease-drug pair by the calculation of complementarity (anti-correlation) and association between clinical states ("up" or "down") of disease signatures and clinical effects ("up", "down" or "association) of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study. Results: The statistical significance of our prediction results is supported through two benchmark datasets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease-drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P<0.009 in Comparative Toxicogenomics Database and Clinical Trials) and presented evidences for those with already published literature. Conclusion: The results show that electronic clinical information is a feasible data resource and utilizing the complementarity (anti-correlated relationships) between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research. It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid.
    Full-text · Article · Dec 2015 · Journal of Biomedical Informatics
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    • "There have been many approaches proposed for drug repositioning, which could be categorized in different ways. Dudley et al [16] reviewed computational methods for drug repositioning and categorized the methods into two classes: drug-based and disease-based, based on whether drug or disease perspective initiates the discovery. In another review paper [17], Hurle et al. reviewed computational techniques for systematic analysis of transcriptomics, side effects, and genetics data. "

    Full-text · Conference Paper · Nov 2015
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