A review of validation strategies for computational drug repositioning

Brown, Adam S.; Patel, Chirag

doi:10.1093/bib/bbw110

Cited by 56 publications

(36 citation statements)

References 21 publications

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“…Drug repurposing may have a transformative impact on precision medicine, 21 fast-tracking novel treatment strategy using agents that would otherwise not be implicated by conventional approaches. Strategies for drug repurposing include a chemogenomic library in phenotypic screen, 22 in silico computational drug repositioning, 23 pairwise combinatorial screen of drugs against molecular targets of interest 24 and novel in vivo model systems such as zebrafish. 25 The use of cytotoxic docetaxel chemotherapy is well established in metastatic prostate cancer, but only results in a modest survival benefit.…”

Section: Discussionmentioning

confidence: 99%

Repurposing screen identifies mebendazole as a clinical candidate to synergise with docetaxel for prostate cancer treatment

et al. 2019

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BACKGROUND: Docetaxel chemotherapy in prostate cancer has a modest impact on survival. To date, efforts to develop combination therapies have not translated into new treatments. We sought to develop a novel therapeutic strategy to tackle chemoresistant prostate cancer by enhancing the efficacy of docetaxel. METHODS: We performed a drug-repurposing screen by using murine-derived prostate cancer cell lines driven by clinically relevant genotypes. Cells were treated with docetaxel alone, or in combination with drugs (n = 857) from repurposing libraries, with cytotoxicity quantified using High Content Imaging Analysis. RESULTS: Mebendazole (an anthelmintic drug that inhibits microtubule assembly) was selected as the lead drug and shown to potently synergise docetaxel-mediated cell killing in vitro and in vivo. Dual targeting of the microtubule structure was associated with increased G2/M mitotic block and enhanced cell death. Strikingly, following combined docetaxel and mebendazole treatment, no cells divided correctly, forming multipolar spindles that resulted in aneuploid daughter cells. Liposomes entrapping docetaxel and mebendazole suppressed in vivo prostate tumour growth and extended progression-free survival. CONCLUSIONS: Docetaxel and mebendazole target distinct aspects of the microtubule dynamics, leading to increased apoptosis and reduced tumour growth. Our data support a new concept of combined mebendazole/docetaxel treatment that warrants further clinical evaluation.

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Section: Discussionmentioning

confidence: 99%

Repurposing screen identifies mebendazole as a clinical candidate to synergise with docetaxel for prostate cancer treatment

et al. 2019

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“…In a recent review, Brown and Patel suggest that using sensitivity-validation alone is ideal since it does not need the true negatives. The authors further suggest that investigators should test their model performance with cross-validation to prevent over-fitting and weak predictive performance [ 111 ].…”

Section: Approachesmentioning

confidence: 99%

Changing Trends in Computational Drug Repositioning

et al. 2018

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Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated—“The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of “serendipity”, discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules—pharmacologically relevant sub-networks of biomolecules and/or pathways—from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

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“…For instance, DrugBank contains information about only the FDA-approved indications for drugs, and missesit misses off-label uses and late-stage clinical trials. On the other hand, Comparative Toxicogenomics Database contains literatureannotated links between drugs and both approved and investigational indications, and contains drug-indication pairs that have subsequently failed to receive FDA approval [81].…”

Section: Sourcesmentioning

confidence: 99%

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

et al. 2018

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Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes.One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks. Data Science Pipelineimprove the classification of diseases [2]. However, the use of these sources raised new problems such as their fragmentation, heterogeneity, availability and different conceptualization of their data [3,4].Recent developments in network theory provide a way to address this challenge by representing these complex relationships as a collection of linked nodes [5]. Complex networks theory is a statistical physics interpretation of the old graph theory, aimed at describing and understanding the structures created by the relationships between the elements of a complex system [6][7][8][9]. Those elements are represented by nodes, pairwise connected by links whenever a relationship is observed between the corresponding elements. The resulting structure can then be described by means of a plethora of topological metrics [10], or be used as a base for modelling the system. The application of this field to biological problems has been named "network biology", while its use in biomedical problems is known as "network medicine" [11]. Some applications of biological networks are protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network or phylogenetic trees [12].Following this approach, disease networks express the relationship between diseases as nodes and edges in a graph in, where D represents the set of diseases (nodes) and W the set of their relationships (edges) based upon their similarity. The meaning of similarity varies depending on the data used to build the network, which may be biological (genes or common proteins) or phenotypic (comorbidity, similar symptoms) [13], among other approaches. As will be explained throughout this article, the concept of disease network is not limited to diseasedisease connections (homogeneous networks), but also to relations between the disease and other factors such as its symptoms, its associ...

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A review of validation strategies for computational drug repositioning

Cited by 56 publications

References 21 publications

Repurposing screen identifies mebendazole as a clinical candidate to synergise with docetaxel for prostate cancer treatment

Repurposing screen identifies mebendazole as a clinical candidate to synergise with docetaxel for prostate cancer treatment

Changing Trends in Computational Drug Repositioning

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

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