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

ABSTRACT 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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Available from: Atul Butte, Aug 27, 2015
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    • "The key challenge when moving from traditional QSAR to systems wide analysis of chemical effects is how to relate structural features to genome-wide cellular responses. Integration of chemical structures with genome wide responses has become a major research direction in Chemical Systems Biology (Dudley et al., 2011; Iskar et al., 2012; Xie et al., 2012). Keiser et al. (2009) studied structural similarities between ligand sets while Klabunde et al. (2005) used protein-ligand complexes to predict off-targets. "
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    ABSTRACT: Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.Availability and implementation: Source Code implementing the method is available at: or samuel.kaski@aalto.fiSupplementary Information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 12/2013; 30(17). DOI:10.1093/bioinformatics/btu456 · 4.62 Impact Factor
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    • "Approved compounds are attractive because they have been extensively studied and have by definition already successfully passed clinical trials, where most drugs fail because of safety or efficacy issues. There is increasing number of approaches to predict repurposing opportunities using computational methods [see Dudley et al. (2011) or Andronis et al. (2011) for recent reviews]. Most methods operate on the profiles of physicochemical descriptors derived from molecular structures (Haupt and Schroeder, 2011). "
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    ABSTRACT: Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds. In order to computationally generate repositioning hypotheses, we used the Web Ontology Language (OWL) to formally define the semantics of over 20'000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System (FTC) and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System (ATC). We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case. A web application built on the top of the resource is freely available at The source code of the project is available at Supplementary data are available at Bioinformatics online.
    Bioinformatics 10/2013; 30(6). DOI:10.1093/bioinformatics/btt628 · 4.62 Impact Factor
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    • "and STAT!Ref (AHFS) (, and because " at present, there is not a comprehensive and systematic representation of known drugs indications that would enable a fine-scale delineation of types of drug-disease relationships " [18]. The ranking of drugs for each DrugBank TC can allow the choice of top ranked " false positive " drugs as natural candidates for drug repositioning, while a pure classification approach cannot provide such preferential candidates. "
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    ABSTRACT: Drug repositioning is a challenging computational problem involving the integration of heterogeneous sources of biomolecular data and the design of label ranking algorithms able to exploit the overall topology of the underlying pharmacological network. In this context we propose a novel semi-supervised drug ranking problem: prioritizing drugs in integrated bio-chemical networks according to specific DrugBank therapeutic categories. Algorithms for drug repositioning usually perform the inference step into an inhomogeneous similarity space induced by the relationships existing between drugs and a second type of entity (e.g. disease, target, ligand set), thus making unfeasible a drug ranking within a homogeneous pharmacological space. To deal with this problem, we designed a general framework based on bipartite network projections by which homogeneous pharmacological networks can be constructed and integrated from heterogeneous and complementary sources of chemical, biomolecular and clinical information. Moreover, we present a novel algorithmic scheme based on kernelized score functions that adopts both local and global learning strategies to effectively rank drugs in the integrated pharmacological space using different network combination methods. Detailed experiments with more than $80$ DrugBank therapeutic categories involving about 1300 FDA approved drugs show the effectiveness of the proposed approach.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 05/2013; 10(6). DOI:10.1109/TCBB.2013.62 · 1.54 Impact Factor
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