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Pharmaceuticals 2018, 11(2), 57; doi:10.3390/ph11020057

Review
Changing Trends in Computational Drug Repositioning
1
Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, 240 Albert Sabin Way MLC 7024, Cincinnati, OH 45229, USA
2
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
3
Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH 45219, USA
*
Author to whom correspondence should be addressed.
Received: 30 April 2018 / Accepted: 2 June 2018 / Published: 5 June 2018

Abstract

:
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.
Keywords:
computational drug repositioning; drug repositioning; drug repurposing; machine learning; deep learning; crowdsourcing; open innovation; drug discovery

1. Introduction

The path to new drug discovery has always been a road full of twists and turns. De novo drug discovery in particular is an expensive, time-consuming, and high risk process. For instance, the total average cost of developing a new drug, as per an estimate, ranges from $2 billion to $3 billion and it takes at least 13–15 years to bring a drug to the market—starting from initial discovery to the approval stage [1]. Further, the process suffers from a high rate of attrition. About 10% of the drugs that enter into clinical trials get approved by regulatory agencies [2]. The remaining 90% of the drugs fail due to inefficacy or high toxicity due to the limited predictive value of preclinical studies [3]. Nearly 62% of the compounds fail in Phase II and approximately 45% attrition occurs in Phase III [4]. These attritions are due to insufficient R&D productivity in identifying the drug response on the target due to the limited availability of preclinical disease models which has raised concerns in the pharmaceutical industry [5]. Despite rapid technological advances and exponential increases in pharmaceutical R&D investments, the number of newly approved drugs continues to be the same [6]. To overcome these challenges and to potentially bypass this productivity gap, more and more companies are resorting to “drug repositioning” or “drug repurposing” (sometimes also referred to as drug reprofiling, drug retasking, or therapeutic switching) or simply identifying and developing new therapeutic uses for existing or abandoned pharmacotherapies [7]. The premise is that since most approved compounds have known bioavailability and safety profiles, proven formulation and manufacturing routes, and reasonably characterized pharmacology, repositioned drugs can enter clinical phases more rapidly and at a lower cost than novel compounds. Further, the 90% therapeutic development failure rate means there are many existing, partially developed therapeutic candidates that could be re-visited, explored further, and potentially repurposed for a new disease, common or rare. It is therefore not surprising that in recent years, of the new drugs that reach their first markets, repositioned drugs have taken up to a percentage of ~30%! For instance, of the 113 new drugs and biologics approved or launched in 2017, only seven were first-in-class agents (an approved and launched first drug with a novel mechanism of action) while 36 were repositioned drugs [8]. As per an estimate, this bypassing can potentially make a drug available for use in patients within 3–12 years with a total estimated cost of $40–80 million [9,10].
Most of the successful cases of drug repurposing have been serendipitous discoveries rather than systematic, hypothesis-driven outcomes. These include the accidental discovery of thalidomide as an agent for leprosy or the more notable example of sildenafil, an angina medication developed in 1989 subsequently marketed as Viagra®, a blockbuster drug to treat erectile dysfunction [12] (see Table 1 for additional examples of drug repositioning). De novo drug therapies for more than 8000 orphan or rare diseases are impossible to develop with the current R&D costs, however, drug repositioning with its premise of discovering hidden connections or building connections between a drug and disease hold promise for orphan disease therapy [13]. Further, revisiting the approved drugs for identifying new indications helps the pharmaceutical companies to extend the patent life of drugs, through application to adjacent diseases and also helps the company to protect the IP against competitors [14].
In-silico methods like data-mining, machine learning, and network-based approaches, offer an unprecedented opportunity to predict all possible drug repositioning candidates using available diverse and heterogeneous data sources from genomics and biomedical domains [15]. Indeed, predictive models have been built using these methods exploiting existing data such as protein targets, chemical structure, or phenotypic information such as profiles of side-effect, gene expression, etc. While the advances in computational sciences bring the possibility of applying novel algorithms and approaches to systems biology data, these datasets themselves have triggered fundamental research on more complex problems [16]. As a result of this hybrid approach of utilizing computational methods and experimental screenings, various modalities of drug repositioning methods have emerged. Computational drug repositioning methods focus on shared characteristics between two drugs and depending on what kind of drug discovery (drug-based or disease-based) [17], the methods can be classified in to target-based, expression-based, knowledge-based, chemical structure-based, pathway-based and mechanism of action-based [18]. In this review article, we briefly outline the recent progress in computational methods and strategies applied on the drug-disease data for drug repositioning investigations.

2. Approaches

In silico drug repurposing challenges that are drug-centric (i.e., discovering new indications for existing drugs) or disease-centric (i.e., identifying an effective drug as a potential treatment for disease) have the common challenge of either assessing the similarity or connections between drugs or between diseases [19]. Jin and Wong [18] reviewed a variety of approaches used as a basis for computational drug repurposing. These can be broadly categorized as knowledge-based and signature-based approaches.

2.1. Knowledge-Based Drug Repurposing

This repurposing method utilizes the available information on drug such as drug-targets, chemical structures, adverse effects, pathways etc. and builds computational models to predict unknown mechanisms, targets or new bio-markers for diseases [20,21,22,23,24]. In pathway-based approach, signaling pathways, metabolic pathways and protein-interaction networks data are used to compute the similarity or connections between drug and disease. The processed omics data, for example, from human patients or animal models of disease are used to reconstruct disease-specific pathways that can serve as key targets for novel therapeutic discovery or for repositioned drugs [25,26,27,28,29,30]. Target mechanism-based approaches on the other hand take into account known mechanism of action and target role : Here, the data available on signaling pathways, protein interactions and omics data are integrated to identify the potential mechanism of action (MoA) of drugs [31,32,33,34]. This in turn can enable find better and even specific drug targets and also for discover of an alternate medication for any disease.

2.2. Signature-Based Drug Repurposing

This method makes use of gene expression signatures by comparing drug gene expression profiles and disease gene expression profiles and is frequently referred to as ‘signature reversion’ method [35]. Gene expression based methods are effective in constructing a detailed map of connections between diseases and drug actions [36,37,38,39,40].
Connectivity Map (CMap) [83,84], NCBI’s Gene Expression (GEO) [75], and the relatively recent LINCS datasets [44] are also extensively explored in drug repositioning studies. Recent technical and technological advancements in molecular biology and exponential growth of biomedical data while presenting challenges have also opened up an array of opportunities to develop and apply novel and powerful computational approaches that can enable informed drug repositioning. The free availability of data repositories are further directing and catalyzing these efforts. In Table 2 we present some of the widely used open source drug- and disease-centric and related databases. These include, for instance, databases that provide information on the known targets, mechanism of action, gene expression, clinical status, ADMET properties, signaling pathways and disease-centric database which has omics data (transcriptomic, proteomic, genetic characteristics of diseases).

2.3. In Silico Methods for Drug Repositioning

In the following sections, we present an overview of some of the in silico methods—current and emerging—used for facilitating drug repositioning candidate discovery.

2.3.1. Machine Learning

Any machine learning workflow typically comprises of 4 steps: data pre-processing, feature extraction, model fitting and evaluation [85]. PREDICT, is a similarity based machine learning framework, integrating drug-drug similarity (based on drug-protein interactions, sequence and gene-ontology) and disease-disease similarity (disease-phenotype and human phenotype ontology) where the authors have used them as features applying logistic regression to predict similar drugs for similar diseases and they achieved AUC = 0.9 in predicting drug indications [86]. SPACE, another similarity-based method predicts anatomical therapeutic chemical classification of drugs by integrating multiple data sources using Logistic Regression [87]. Likewise, several such similarity based methods have been reported for predicting novel drug indications [88,89,90].
Deep learning, a large class of machine learning-based models composed of multiple processing layers representing data with a high level of abstraction are now being explored computational biology field for a wide-variety of applications including drug discovery [91,92]. The principal difference between conventional “shallow” learning (neural network with one or two hidden layers) and deep learning is that while the former does not deal with raw data and requires a feature extraction step to be performed before the learning process, the latter not only discovers intricate structure in large data sets but by using the backpropagation algorithm allows changing the internal parameters incrementally to compute the representation in each layer from the representation in the previous layer [92]. Deep learning-based approaches have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and are currently being explored in biomedical and genomic domains. Aliper and Plis, for example, used deep learning with gene expression data to learn drug therapeutic categories and found that deep neural networks surpassed SVM after 10 fold cross validation suggesting a working proof for applying deep learning for drug discovery and development [93]. Interestingly, Zhao and Cheong, compared deep neural networks (DNN) approach with SVM-based approach to predict psychiatric drug indications based on the expression profiles of drugs and reported that [37]. While more studies are needed to understand if DNN-based approaches indeed have the claimed benefits, there have been additional reports suggesting that deep learning-based approaches perform better than traditional machine learning algorithms in toxicity prediction by enabling multi-task learning [94,95].

2.3.2. Network Models

Network-based approaches have been extensively exploited in computational drug repositioning for identifying novel drug targets, interactions, and indications [96]. Typically, in these models, the nodes in the networks represent either drug, disease, or gene products and edges represent the interactions or relationships between them. These networks are either knowledge-based or computationally inferred using multiple data resources and have various representations such as drug-drug, drug-target, drug-disease, disease-disease, disease-gene, disease-drug, protein-protein interactions, and transcriptional networks [97]. Cheng and Liu computed similarities—drug-based, target-based, and network-based—to predict drug-target interaction in a bi-partite network and found that network based inference method performed best with an average ROC AUC of 0.96 [21]. Similar homogenous or bipartite network models have been incorporated using phenotype data such as side-effect [98,99,100], transcriptional [101,102,103], drug-disease [104,105] and signaling pathway data [25].
Integrating heterogeneous data also provides diverse information and has the potential to unveil hidden or unknown drug-disease relationships based on the guilt-by-association principle. Most of the similarity-based methods are either drug-centric or disease-centric networks, with relatively few approaches that built a drug-disease heterogeneous network using compendia of gene annotations and network clustering to identify drug repositioning candidates [105,106]. Luo and Zhao, built a similar network-based framework using heterogeneous data through a network diffusion process and used the diffusion distributions to derive the prediction scores of drug-target interactions [107]. Recently, Himmelstein et al. integrated data from 29 public resources to identify dug repositioning candidates and predicted the probability of repositioning for 209,168 drug-disease pairs [108].

2.3.3. Mining Electronic Health Records for Drug Repurposing

Electronic health records (EHR) of the patients which provide medications details along with patient history can also be mined to identify drug repositioning candidates. Applying natural language processing on EHRs, for instance, reveals post-market, additional adverse drug events which are not found in clinical trials [109]. These side-effects information can be potentially used for drug-repositioning and validation [23]. Mining EHR records for example helped in identifying that metformin, a most commonly prescribed medication for type II diabetes, can also be repurposed for cancer treatment [110]. The relevance and accuracy of the model’s prediction needs to be assessed in discovering a drug whose indications are unknown. The validity of novel drug prediction can be evaluated by comparing the predicted targets in ClinicalTrials.gov, PubMed abstracts or EHR records. The performance of the model can be evaluated by computing area under the ROC curve (AUC ROC) and Precision Recall (PR) curve. Sensitivity is a metric to measure the proportion of true positive identified correctly and Specificity is the proportion of negatives correctly identified as negatives. Due to the large unannotated drug-indication pairs as false positives, the sensitivity and specificity estimates are poor and creates substantial imbalance of true positives and true negatives. 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].

2.4. Open Innovation—Crowd Sourcing

Crowd sourcing is a collaborative approach of delegating tasks to the crowd where the variety of expertise available generates new insights or hypothesis with the available data. This paradigm has been taken advantage in a multitude areas from diverse domains including health care and genomics. The open source drug discovery process enables faster translation of research to results with a clear definition on specific problem, task decomposition and immediate feedback loop [112,113,114,115]. Pharmaceutical companies, due to the limitations in R&D business model and man power often are focused on specific diseases which may or may not include rare and neglected diseases. Hence, few pharmaceutical and non-profit companies have used crowdsourcing platforms and embraced a wide-variety of innovative solutions [116,117] directing towards discussing the scientific enigmas. Several open innovation platforms have been established in order to build industry-academia partnerships and to explore science and business opportunities with mutual benefit (Table 3).

National Center for Advancing Translational Sciences (NCATS)—NIH-Academia-Industry Partnerships Initiative

The National Institutes of Health (NIH), as part of the new therapeutic uses program, launched (i) NCATS’ NIH-Industry Partnerships initiative in 2012 to foster collaboration between pharmaceutical companies and the biomedical research community; and (ii) bench-to clinical repurposing initiative to test the utility of crowdsourcing efforts or computational approaches for drug repurposing.
The focus of the match-making NIH-industry partnerships projects is to match researchers with open assets from pharmaceutical assets to fuel and accelerate drug repurposing candidate discovery. Through this initiative, NCATS supports and advances research on a wide range of common and rare (including neglected) diseases. Current industry partners in this initiative include: AstraZeneca, AbbVie, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Janssen Pharmaceuticals, MedImmune, Mereo BioPharma, Pfizer, and Sanofi. The participating companies make a number of partially developed assets available to academic researchers to crowdsource repurposing ideas. Projects using most of these assets can go directly into Phase II clinical trials, while some may require additional pre-clinical investigations or a Phase I clinical trial (e.g., testing in target populations to determine dosing, assess safety and tolerability).
Through the bench-to-clinic repurposing program, NCATS supports pre-clinical studies, clinical feasibility studies or proof-of-concept clinical trials to assess the utility of computational approaches or crowdsourcing efforts in discovering drug repurposing candidates. Table 4 lists the new therapeutic uses projects funded by NIH-NCATS through these two programs (additional details can be found at https://ncats.nih.gov/ntu/projects).

2.5. Open Source Software

The open source movement has created a substantial value in pursuing towards “state-of-the-art” research over the last decade with the help of reusable and generic software libraries for data processing [124,125]. Jupyter Notebook for instance is the modern data analysis tool for reproducible computational research that supports open source languages like Python, Julia, C++, R and several other languages and provides rich features for interactive computing, visualization, and documentation [126]. Structured data tools like Scikit-learn [127], R-Programming, Orange [128] and Weka [129] are useful for mining, analysis, learning and statistical computing. For the high dimensional un-structured data such as images, text, or audio outputs, deep learning tools like TensorFlow [130], Keras [131], PyTorch [132], CNTK [133], and Matlab [134] that take advantage of multi-GPU accelerated training are increasingly used. Gephi [135] and Cytoscape [136] are other popular tools used primarily for bimolecular interaction networks, omics-data integration, clustering and visualization. In Table 5, we summarize few such used tools used in computational drug discovery and repositioning.

3. Discussion

Drug repositioning acts as a viable strategy for a cost-effective de novo drug discovery. Although in silico methods have proven to be successful in addressing the problem of repurposing, some challenges continue to be addressed. One of the principal issues is the missing drug-disease indication data. Marking the missing indications as true negatives or ignoring them from training can potentially compromise the predictive power of the computational model for drug repurposing candidate discovery. Second, the lack of a true gold standard dataset for drug repositioning makes it difficult for in silico methods to evaluate results. As a result, common performance metrics such as sensitivity, specificity, and precision are used to assess the utility of computational drug repurposing algorithms. Third, existing computational methods tend to be predominantly one-sided (e.g., drug-centric or disease-centric). However, the integration of multi-omic data with similarity measures have been shown to have better predictive performance with identification of novel therapeutic compounds [105,108].
The sea of biomedical information (see Table 2), in which small molecule and gene/protein structural, functional and process knowledge—both in normal and disease states—is embedded consists of unstructured free-text as in publications and structured or semi-structured relational databases. Transforming information from these silos into actionable knowledge is facilitated by establishing connectivity among the subsets taken from these multiple heterogeneous and diverse domains. For example, a pharmacomodule consisting of a group of genes, biological processes, pathways, phenotypes, small molecules (approved drugs or investigational compounds), and a group of drug-induced or related adverse events forms a meaningful multi-domain module when the interdependency among most of the pairs of subsets are supported by scientific evidence (literature or databases). These pharmacomodules can potentially take us closer to answering the how question about the underlying a hypothetical mechanism of action or phenomena. An informed answer to the how question holds the premise to generate better and informed drug repositioning hypotheses. Growing scientific evidence [7] suggests that any compound found to be safe in humans is likely to have multiple therapeutic uses. However, almost all successful drug reposition crossovers so far have been the result of either accidental occurrences or informed guesses. Given that this “back-to-basics” approach for repositioning is growing in popularity [8], there is an urgent need for more efficient and systematic computational approaches to first systematize the available genomic and pharmacological databases for representation and knowledge discovery and then use these databases and pattern discovery tools to identify the potential new uses for existing drugs. What is needed clearly is a paradigm shift in the approaches—genomic, biopharmacological, and computational—for a more informed systematic drug rediscovery (“systematic serendipity”) taking into account all of the data resources. Originally coined by Eugene Garfield, “systematic serendipity” refers to the organized process of discovering previously unknown scientific relations using citation databases, leading to better possibilities for a collaboration of human serendipity with computer supported knowledge discovery [150].
The credibility of published research will improve the discoveries in science if the provided compendium has an evidence for the accuracy and reproducibility of the results. Reproducibility particularly is a major issue especially when scientific papers publish unexpected, positive results and other researchers or an independent research group is unable to replicate the same results even after using same or similar methods as reported by the original study [151,152]. It has been estimated that the irreproducible research costs up to $28 billion per year [153]! Providing the code and data used to obtain the claimed and reported results is always a better strategy than mere describing them in natural language in the paper and can be eventually an incremental step towards a better science [154,155,156]. The recent Findability, Accessibility, Interoperability, Reusability (FAIR) data principles go beyond the mere reuse of data by individuals but rather enhance the ability of machines to support and find and use the data automatically. These include any efforts that support discovery and reproducibility through good data management practices such as good data management, maintenance of the data flow, and sharing relevant tools or pipelines used in the research [157]. The recent Datasets2Tools project is in compliance with these principles and enables users to search for contributed canned analyses, datasets and tools [158]. Computational science research can be replicated effectively using tools like code version control software like Github [159] and transferable computational environments like Docker [160]. Over the past few years, the reproducibility issue is being taken seriously and many journals insist on providing code and data when submitting the paper.
In summary, emerging and advanced novel computational methods and crowdsourcing-based approaches that enable the joint analysis of genomic, biomedical and pharmacological data hold the premise to facilitate informed, efficient, and systematic drug repositioning. Whether this premise expedites drug development pipelines and how much of it translates into novel therapeutic discovery and impacts public health, especially catering to unmet needs (e.g., rare and neglected diseases), positively remains to be seen.

Author Contributions

J.K.Y. and A.G.J. conceived the outline, reviewed the literature and wrote the manuscript; S.Y. and Y.W. participated in the discussions and provided the edits to some sections of the paper.

Acknowledgments

This research was supported in part by the NIH NHLBI’s 1R21 HL133539 (A.G.J.) and 1R21 HL135368 (A.G.J.).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Scannell, J.W.; Blanckley, A.; Boldon, H.; Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 2012, 11, 191–200. [Google Scholar] [PubMed]
  2. Akhondzadeh, S. The Importance of Clinical Trials in Drug Development. Avicenna J. Med. Biotechnol. 2016, 8, 151. [Google Scholar] [PubMed]
  3. Plenge, R.M.; Scolnick, E.M.; Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 2013, 12, 581–594. [Google Scholar] [CrossRef] [PubMed]
  4. Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 2004, 3, 1–5. [Google Scholar] [CrossRef] [PubMed]
  5. Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve RD productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 2010, 9, 203–214. [Google Scholar] [CrossRef] [PubMed]
  6. Booth, B.; Zemmel, R. Opinion/Outlook: Prospects for productivity. Nat. Rev. Drug Discov. 2004, 3, 451–456. [Google Scholar] [CrossRef] [PubMed]
  7. Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 2004, 3, 673–683. [Google Scholar] [CrossRef] [PubMed]
  8. Graul, A.I.; Cruces, E.; Stringer, M. The year’s new drugs & biologics, 2013: Part I. Drugs of Today 2014, 50, 51–100. [Google Scholar] [PubMed]
  9. Hurle, M.R.; Yang, L.; Xie, Q.; Rajpal, D.K.; Sanseau, P.; Agarwal, P. Computational drug repositioning: From data to therapeutics. Clin. Pharmacol. Ther. 2013, 93, 335–341. [Google Scholar] [CrossRef] [PubMed]
  10. Papapetropoulos, A.; Szabo, C. Inventing new therapies without reinventing the wheel: The power of drug repurposing. Br. J. Pharmacol. 2018, 2016–2018. [Google Scholar] [CrossRef] [PubMed]
  11. Padhy, B.M.; Gupta, Y.K. Drug repositioning: Re-investigating existing drugs for new therapeutic indications. J. Postgrad. Med. 2011, 57, 153–160. [Google Scholar] [CrossRef] [PubMed]
  12. Nosengo, N. Can you teach old drugs new tricks? Nature 2016, 534, 314–316. [Google Scholar] [CrossRef] [PubMed]
  13. Sardana, D.; Zhu, C.; Zhang, M.; Gudivada, R.C.; Yang, L.; Jegga, A.G. Drug repositioning for orphan diseases. Brief. Bioinform. 2011, 12, 346–356. [Google Scholar] [CrossRef] [PubMed]
  14. Cha, Y.; Erez, T.; Reynolds, I.J.; Kumar, D.; Ross, J.; Koytiger, G.; Kusko, R.; Zeskind, B.; Risso, S.; Kagan, E.; et al. Drug repurposing from the perspective of pharmaceutical companies. Br. J. Pharmacol. 2017. [Google Scholar] [CrossRef] [PubMed]
  15. Li, J.; Zheng, S.; Chen, B.; Butte, A.J.; Swamidass, S.J.; Lu, Z. A survey of current trends in computational drug repositioning. Brief. Bioinform. 2016, 17, 2–12. [Google Scholar] [CrossRef] [PubMed]
  16. Prathipati, P.; Mizuguchi, K. Systems Biology Approaches to a Rational Drug Discovery Paradigm. Curr. Top. Med. Chem. 2015, 16, 1009–1025. [Google Scholar] [CrossRef]
  17. Li, Y.Y.; Jones, S.J.M. Drug repositioning for personalized medicine. Genome Med. 2012, 4, 27. [Google Scholar] [PubMed]
  18. Jin, G.; Wong, S.T.C. Toward better drug repositioning: Prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today 2014, 19, 637–644. [Google Scholar] [CrossRef] [PubMed]
  19. Liu, Z.; Fang, H.; Reagan, K.; Xu, X.; Mendrick, D.L.; Slikker, W.; Tong, W. In silico drug repositioning: What we need to know. Drug Discov. Today 2013, 18, 110–115. [Google Scholar] [CrossRef] [PubMed]
  20. Emig, D.; Ivliev, A.; Pustovalova, O.; Lancashire, L.; Bureeva, S.; Nikolsky, Y.; Bessarabova, M. Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach. PLoS ONE 2013, 8. [Google Scholar] [CrossRef] [PubMed]
  21. Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 2012, 8. [Google Scholar] [CrossRef] [PubMed]
  22. Zhao, S.; Li, S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS ONE 2010, 5. [Google Scholar] [CrossRef] [PubMed]
  23. Yang, L.; Agarwal, P. Systematic drug repositioning based on clinical side-effects. PLoS ONE 2011, 6. [Google Scholar] [CrossRef] [PubMed]
  24. Kinnings, S.L.; Liu, N.; Buchmeier, N.; Tonge, P.J.; Xie, L.; Bourne, P.E. Drug discovery using chemical systems biology: Repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput. Biol. 2009, 5. [Google Scholar] [CrossRef] [PubMed]
  25. Jadamba, E.; Shin, M. A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network. BioMed Res. Int. 2016, 2016. [Google Scholar] [CrossRef] [PubMed]
  26. Li, J.; Lu, Z. Pathway-based drug repositioning using causal inference. BMC Bioinform. 2013, 14. [Google Scholar] [CrossRef] [PubMed]
  27. Kotelnikova, E.; Yuryev, A.; Mazo, I.; Daraselia, N. Computational approaches for drug repositioning and combination therapy design. J. Bioinform. Comput. Biol. 2010, 8, 593–606. [Google Scholar] [CrossRef] [PubMed]
  28. Cramer, P.; Cirrito, J.; Wesson, D. ApoE-Directed Therapeutics Rapidly Clear beta-Amyloid and Reverse Deficits in AD Mouse Models. Science 2012, 335, 1503–1506. [Google Scholar] [CrossRef] [PubMed]
  29. Sivachenko, A.; Kalinin, A.; Yuryev, A. Pathway Analysis for Design of Promiscuous Drugs and Selective Drug Mixtures. Curr. Drug Discov. Technol. 2006, 3, 269–277. [Google Scholar] [CrossRef] [PubMed]
  30. Strittmatter, W.J. Old drug, new hope for Alzheimer’s disease. Science 2012, 335, 1447–1448. [Google Scholar] [CrossRef] [PubMed]
  31. Jin, G.; Zhao, H.; Zhou, X.; Wong, S.T.C. An enhanced Petri-Net model to predict synergistic effects of pairwise drug combinations from gene microarray data. Bioinformatics 2011, 27. [Google Scholar] [CrossRef] [PubMed]
  32. Jin, G.; Fu, C.; Zhao, H.; Cui, K.; Chang, J.; Wong, S.T.C. A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy. Cancer Res. 2012, 72, 33–44. [Google Scholar] [CrossRef] [PubMed]
  33. Iskar, M.; Zeller, G.; Blattmann, P.; Campillos, M.; Kuhn, M.; Kaminska, K.H.; Runz, H.; Gavin, A.C.; Pepperkok, R.; Van Noort, V.; et al. Characterization of drug-induced transcriptional modules: Towards drug repositioning and functional understanding. Mol. Syst. Biol. 2013, 9. [Google Scholar] [CrossRef] [PubMed]
  34. Gaiteri, C.; Ding, Y.; French, B.; Tseng, G.C.; Sibille, E. Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 2014, 13, 13–24. [Google Scholar] [CrossRef] [PubMed]
  35. Iorio, F.; Rittman, T.; Ge, H.; Menden, M.; Saez-Rodriguez, J. Transcriptional data: A new gateway to drug repositioning? Drug Discov. Today 2013, 18, 350–357. [Google Scholar] [CrossRef] [PubMed]
  36. Sirota, M.; Dudley, J.T.; Kim, J.; Chiang, A.P.; Morgan, A.A.; Sweet-Cordero, A.; Sage, J.; Butte, A.J. Discovery and preclinical validation of drug indications using compendia of public gene expression data (Science Translational Medicine (2011) 3, (102er)). Sci. Transl. Med. 2011, 3, 96ra77. [Google Scholar] [CrossRef] [PubMed]
  37. Zhao, K.; So, H.-C. A machine learning approach to drug repositioning based on drug expression profiles: Applications in psychiatry. arXiv, 2017. [Google Scholar]
  38. Wang, Y.; Yella, J.; Chen, J.; McCormack, F.X.; Madala, S.K.; Jegga, A.G. Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers. BMC Pulm. Med. 2017, 17. [Google Scholar] [CrossRef] [PubMed]
  39. Claerhout, S.; Lim, J.Y.; Choi, W.; Park, Y.-Y.; Kim, K.; Kim, S.-B.; Lee, J.-S.; Mills, G.B.; Cho, J.Y. Gene Expression Signature Analysis Identifies Vorinostat as a Candidate Therapy for Gastric Cancer. PLoS ONE 2011, 6, e24662. [Google Scholar] [CrossRef] [PubMed]
  40. Chang, M.; Smith, S.; Thorpe, A.; Barratt, M.J.; Karim, F. Evaluation of phenoxybenzamine in the CFA model of pain following gene expression studies and connectivity mapping. Mol. Pain 2010, 6. [Google Scholar] [CrossRef] [PubMed]
  41. Cai, M.C.; Xu, Q.; Pan, Y.J.; Pan, W.; Ji, N.; Li, Y.B.; Liu, H.J.J.K.; Ji, Z.L. ADReCS: An ontology database for aiding standardization and hierarchical Classification of adverse drug reaction terms. Nucleic Acids Res. 2015, 43, D907–D913. [Google Scholar] [CrossRef] [PubMed]
  42. Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; et al. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40. [Google Scholar] [CrossRef] [PubMed]
  43. Williams, A.J. Internet-based tools for communication and collaboration in chemistry. Drug Discov. Today 2008, 13, 502–506. [Google Scholar] [CrossRef] [PubMed]
  44. Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K.; et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017, 171, 1437–1452. [Google Scholar] [CrossRef] [PubMed]
  45. Mattingly, C.J.; Colby, G.T.; Forrest, J.N.; Boyer, J.L. The Comparative Toxicogenomics Database (CTD). Environ. Health Perspect. 2003, 111, 793. [Google Scholar] [CrossRef] [PubMed]
  46. National Institutes of Health: Health & Human Services DailyMed. Available online: http://dailymed.nlm.nih.gov/dailymed/index.cfm (accessed on 25 April 2018).
  47. Griffith, M.; Griffith, O.L.; Coffman, A.C.; Weible, J.V.; Mcmichael, J.F.; Spies, N.C.; Koval, J.; Das, I.; Callaway, M.B.; Eldred, J.M.; et al. DGIdb: Mining the druggable genome. Nat. Methods 2013, 10, 1209–1210. [Google Scholar] [CrossRef] [PubMed]
  48. DrugBank DrugBank. Available online: http://www.drugbank.ca (accessed on 22 April 2018).
  49. Ursu, O.; Holmes, J.; Knockel, J.; Bologa, C.G.; Yang, J.J.; Mathias, S.L.; Nelson, S.J.; Oprea, T.I. DrugCentral: Online drug compendium. Nucleic Acids Res. 2017, 45, D932–D939. [Google Scholar] [CrossRef] [PubMed]
  50. Pihan, E.; Colliandre, L.; Guichou, J.F.; Douguet, D. E-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design. Bioinformatics 2012, 28, 1540–1541. [Google Scholar] [CrossRef] [PubMed]
  51. Yang, W.; Soares, J.; Greninger, P.; Edelman, E.J.; Lightfoot, H.; Forbes, S.; Bindal, N.; Beare, D.; Smith, J.A.; Thompson, I.R.; et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013, 41. [Google Scholar] [CrossRef] [PubMed]
  52. Koscielny, G.; An, P.; Carvalho-Silva, D.; Cham, J.A.; Fumis, L.; Gasparyan, R.; Hasan, S.; Karamanis, N.; Maguire, M.; Papa, E.; et al. Open Targets: A platform for therapeutic target identification and Validation. Nucleic Acids Res. 2017, 45, D985–D994. [Google Scholar] [CrossRef] [PubMed]
  53. Hewett, M.; Oliver, D.E.; Rubin, D.L.; Easton, K.L.; Stuart, J.M.; Altman, R.B.; Klein, T.E. PharmGKB: The pharmacogenetics knowledge base. Nucleic Acids Res. 2002, 30, 163–165. [Google Scholar] [CrossRef] [PubMed]
  54. Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 2015, 58, 4066–4072. [Google Scholar] [CrossRef] [PubMed]
  55. Cowley, G.S.; Weir, B.A.; Vazquez, F.; Tamayo, P.; Scott, J.A.; Rusin, S.; East-Seletsky, A.; Ali, L.D.; Gerath, W.F.J.; Pantel, S.E.; et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 2014, 1. [Google Scholar] [CrossRef] [PubMed]
  56. Von Eichborn, J.; Murgueitio, M.S.; Dunkel, M.; Koerner, S.; Bourne, P.E.; Preissner, R. PROMISCUOUS: A database for network-based drug-repositioning. Nucleic Acids Res. 2011, 39. [Google Scholar] [CrossRef] [PubMed]
  57. Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; et al. PubChem substance and compound databases. Nucleic Acids Res. 2016, 44, D1202–D1213. [Google Scholar] [CrossRef] [PubMed]
  58. Kuhn, M.; Letunic, I.; Jensen, L.J.; Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016, 44, D1075–D1079. [Google Scholar] [CrossRef] [PubMed]
  59. Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 2008, 36. [Google Scholar] [CrossRef] [PubMed]
  60. Dunkel, M.; Günther, S.; Ahmed, J.; Wittig, B.; Preissner, R. SuperPred: Drug classification and target prediction. Nucleic Acids Res. 2008, 36. [Google Scholar] [CrossRef] [PubMed]
  61. Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic Target Database. Nucleic Acids Res. 2002, 30, 412–415. [Google Scholar] [CrossRef] [PubMed]
  62. Wishart, D.; Arndt, D.; Pon, A.; Sajed, T.; Guo, A.C.; Djoumbou, Y.; Knox, C.; Wilson, M.; Liang, Y.; Grant, J.; et al. T3DB: The toxic exposome database. Nucleic Acids Res. 2015, 43, D928–D934. [Google Scholar] [CrossRef] [PubMed]
  63. Uhlén, M.; Fagerberg, L.; Hallström, B.M.; Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, Å.; Kampf, C.; Sjöstedt, E.; Asplund, A.; et al. Proteomics. Tissue-based map of the human proteome. Science 2015, 347, 1260419. [Google Scholar] [CrossRef] [PubMed]
  64. Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999, 27, 29–34. [Google Scholar] [CrossRef] [PubMed]
  65. Akbarian, S.; Liu, C.; Knowles, J.A.; Vaccarino, F.M.; Farnham, P.J.; Crawford, G.E.; Jaffe, A.E.; Pinto, D.; Dracheva, S.; Geschwind, D.H.; et al. The PsychENCODE project. Nat. Neurosci. 2015, 18, 1707–1712. [Google Scholar] [CrossRef] [PubMed]
  66. Sunkin, S.M.; Ng, L.; Lau, C.; Dolbeare, T.; Gilbert, T.L.; Thompson, C.L.; Hawrylycz, M.; Dang, C. Allen Brain Atlas: An integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 2013, 41, D996–D1008. [Google Scholar] [CrossRef] [PubMed]
  67. Parkinson, H. ArrayExpress--a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2004, 33, D553–D555. [Google Scholar] [CrossRef] [PubMed]
  68. Barretina, J.; Caponigro, G.; Stransky, N.; Venkatesan, K.; Margolin, A.A; Kim, S.; Wilson, C.J.; Lehár, J.; Kryukov, G.V; Sonkin, D.; et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity Supp. Nature 2012, 483, 603–607. [Google Scholar] [CrossRef] [PubMed]
  69. Forbes, S.A.; Beare, D.; Gunasekaran, P.; Leung, K.; Bindal, N.; Boutselakis, H.; Ding, M.; Bamford, S.; Cole, C.; Ward, S.; et al. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015, 43, D805–D811. [Google Scholar] [CrossRef] [PubMed]
  70. Mailman, M.D.; Feolo, M.; Jin, Y.; Kimura, M.; Tryka, K.; Bagoutdinov, R.; Hao, L.; Kiang, A.; Paschall, J.; Phan, L.; et al. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 2007, 39, 1181–1186. [Google Scholar] [CrossRef] [PubMed]
  71. Sherry, S.T. dbSNP: The NCBI database of genetic variation. Nucleic Acids Res. 2001, 29, 308–311. [Google Scholar] [CrossRef] [PubMed]
  72. Lappalainen, I.; Lopez, J.; Skipper, L.; Hefferon, T.; Spalding, J.D.; Garner, J.; Chen, C.; Maguire, M.; Corbett, M.; Zhou, G.; et al. DbVar and DGVa: Public archives for genomic structural variation. Nucleic Acids Res. 2013, 41. [Google Scholar] [CrossRef] [PubMed]
  73. Piñero, J.; Queralt-Rosinach, N.; Bravo, À.; Deu-Pons, J.; Bauer-Mehren, A.; Baron, M.; Sanz, F.; Furlong, L.I. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, 2015. [Google Scholar] [CrossRef] [PubMed]
  74. Grossman, R.L.; Heath, A.P.; Ferretti, V.; Varmus, H.E.; Lowy, D.R.; Kibbe, W.A.; Staudt, L.M. Toward a Shared Vision for Cancer Genomic Data. N. Engl. J. Med. 2016, 375, 1109–1112. [Google Scholar] [CrossRef] [PubMed]
  75. Barrett, T. NCBI GEO: Mining millions of expression profiles--database and tools. Nucleic Acids Res. 2004, 33, D562–D566. [Google Scholar] [CrossRef] [PubMed]
  76. Lonsdale, J.; Thomas, J.; Salvatore, M.; Phillips, R.; Lo, E.; Shad, S.; Hasz, R.; Walters, G.; Garcia, F.; Young, N.; et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013, 45, 580–585. [Google Scholar] [CrossRef] [PubMed]
  77. Kim, M.-S.; Pinto, S.M.; Getnet, D.; Nirujogi, R.S.; Manda, S.S.; Chaerkady, R.; Madugundu, A.K.; Kelkar, D.S.; Isserlin, R.; Jain, S.; et al. A draft map of the human proteome. Nature 2014, 509, 575–581. [Google Scholar] [CrossRef] [PubMed]
  78. Cancer, T. International Cancer Genome Consortium. Cancer 2011, 2011, 1–20. [Google Scholar] [CrossRef]
  79. Clarke, L.; Fairley, S.; Zheng-Bradley, X.; Streeter, I.; Perry, E.; Lowy, E.; Tassé, A.M.; Flicek, P. The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data. Nucleic Acids Res. 2017, 45, D854–D859. [Google Scholar] [CrossRef] [PubMed]
  80. Aymé, S.; Schmidtke, J. Networking for rare diseases: A necessity for Europe. Bundesgesundheitsblatt Gesundheitsforschunq Gesundheitsschutz 2007, 50, 1477–1483. [Google Scholar] [CrossRef] [PubMed]
  81. Bernstein, B.E.; Stamatoyannopoulos, J.A.; Costello, J.F.; Ren, B.; Milosavljevic, A.; Meissner, A.; Kellis, M.; Marra, M.A.; Beaudet, A.L.; Ecker, J.R.; et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 2010, 28, 1045–1048. [Google Scholar] [CrossRef] [PubMed]
  82. Szklarczyk, D.; Franceschini, A.; Kuhn, M.; Simonovic, M.; Roth, A.; Minguez, P.; Doerks, T.; Stark, M.; Muller, J.; Bork, P.; et al. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39. [Google Scholar] [CrossRef] [PubMed]
  83. Lamb, J.; Crawford, E.D.; Peck, D.; Modell, J.W.; Blat, I.C.; Wrobel, M.J.; Lerner, J.; Brunet, J.P.; Subramanian, A.; Ross, K.N.; et al. The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313, 1929–1935. [Google Scholar] [CrossRef] [PubMed]
  84. Musa, A.; Ghoraie, L.S.; Zhang, S.-D.; Galzko, G.; Yli-Harja, O.; Dehmer, M.; Haibe-Kains, B.; Emmert-Streib, F. A review of connectivity map and computational approaches in pharmacogenomics. Brief. Bioinform. 2017, bbw112. [Google Scholar] [CrossRef] [PubMed]
  85. Angermueller, C.; Pärnamaa, T.; Parts, L.; Oliver, S. Deep Learning for Computational Biology. Mol. Syst. Biol. 2016, 878. [Google Scholar] [CrossRef] [PubMed]
  86. Gottlieb, A.; Stein, G.Y.; Ruppin, E.; Sharan, R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 2011, 7. [Google Scholar] [CrossRef] [PubMed]
  87. Liu, Z.; Guo, F.; Gu, J.; Wang, Y.; Li, Y.; Wang, D.; Lu, L.; Li, D.; He, F. Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources. Bioinformatics 2015, 31, 1788–1795. [Google Scholar] [CrossRef] [PubMed]
  88. Li, J.; Lu, Z. A new method for computational drug repositioning using drug pairwise similarity. In Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012, Philadelphia, PA, USA, 4–7 October 2012; pp. 453–456. [Google Scholar]
  89. Luo, H.; Wang, J.; Li, M.; Luo, J.; Peng, X.; Wu, F.X.; Pan, Y. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 2016, 32, 2664–2671. [Google Scholar] [CrossRef] [PubMed]
  90. Oh, M.; Ahn, J.; Yoon, Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS ONE 2014, 9. [Google Scholar] [CrossRef] [PubMed]
  91. Mamoshina, P.; Vieira, A.; Putin, E.; Zhavoronkov, A. Applications of Deep Learning in Biomedicine. Mol. Pharm. 2016, 13, 1445–1454. [Google Scholar] [CrossRef] [PubMed]
  92. Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
  93. Aliper, A.; Plis, S.; Artemov, A.; Ulloa, A.; Mamoshina, P.; Zhavoronkov, A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 2016, 13, 2524–2530. [Google Scholar] [CrossRef] [PubMed]
  94. Unterthiner, T.; Mayr, A.; Klambauer, G.; Hochreiter, S. Toxicity Prediction using Deep Learning. Front. Environ. Sci. 2015, 3, 10. [Google Scholar] [CrossRef]
  95. Ramsundar, B.; Kearnes, S.; Riley, P.; Webster, D.; Konerding, D.; Pande, V. Massively Multitask Networks for Drug Discovery. arXiv, 2015. [Google Scholar]
  96. Schadt, E.E. Molecular networks as sensors and drivers of common human diseases. Nature 2009, 461, 218–223. [Google Scholar] [CrossRef] [PubMed]
  97. Azuaje, F. Drug interaction networks: An introduction to translational and clinical applications. Cardiovasc. Res. 2013, 97, 631–641. [Google Scholar] [CrossRef] [PubMed]
  98. Ye, H.; Liu, Q.; Wei, J. Construction of drug network based on side effects and its application for drug repositioning. PLoS ONE 2014, 9. [Google Scholar] [CrossRef] [PubMed]
  99. Mizutani, S.; Pauwels, E.; Stoven, V.; Goto, S.; Yamanishi, Y. Relating drug-protein interaction network with drug side effects. Bioinformatics 2012, 28. [Google Scholar] [CrossRef] [PubMed]
  100. Campillos, M.; Kuhn, M.; Gavin, A.-C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science 2008, 321, 263–266. [Google Scholar] [CrossRef] [PubMed]
  101. Brown, A.S.; Kong, S.W.; Kohane, I.S.; Patel, C.J. ksRepo: A generalized platform for computational drug repositioning. BMC Bioinform. 2016, 17. [Google Scholar] [CrossRef] [PubMed]
  102. Setoain, J.; Franch, M.; Martínez, M.; Tabas-Madrid, D.; Sorzano, C.O.S.; Bakker, A.; Gonzalez-Couto, E.; Elvira, J.; Pascual-Montano, A. NFFinder: An online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Res. 2015, 43, W193–W199. [Google Scholar] [CrossRef] [PubMed]
  103. Chang, R.; Shoemaker, R.; Wang, W. A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention. IEEE/ACM Trans. Comput. Biol. Bioinform. 2011, 8, 1170–1182. [Google Scholar] [CrossRef] [PubMed]
  104. Chen, H.; Zhang, H.; Zhang, Z.; Cao, Y.; Tang, W. Network-based inference methods for drug repositioning. Comput. Math. Methods Med. 2015, 2015. [Google Scholar] [CrossRef] [PubMed]
  105. Wu, C.; Gudivada, R.C.; Aronow, B.J.; Jegga, A.G. Computational drug repositioning through heterogeneous network clustering. BMC Syst. Biol. 2013, 7, 1–9. [Google Scholar] [CrossRef] [PubMed]
  106. Chen, J.; Bardes, E.E.; Aronow, B.J.; Jegga, A.G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009, 37. [Google Scholar] [CrossRef] [PubMed]
  107. Luo, Y.; Zhao, X.; Zhou, J.; Yang, J.; Zhang, Y.; Kuang, W.; Peng, J.; Chen, L.; Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 2017, 8. [Google Scholar] [CrossRef] [PubMed]
  108. Himmelstein, D.S.; Lizee, A.; Hessler, C.; Brueggeman, L.; Chen, S.L.; Hadley, D.; Green, A.; Khankhanian, P.; Baranzini, S.E. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 2017, 6, 1–35. [Google Scholar] [CrossRef] [PubMed]
  109. Luo, Y.; Thompson, W.K.; Herr, T.M.; Zeng, Z.; Berendsen, M.A.; Jonnalagadda, S.R.; Carson, M.B.; Starren, J. Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review. Drug Saf. 2017, 40, 1075–1089. [Google Scholar] [CrossRef] [PubMed]
  110. Xu, H.; Aldrich, M.C.; Chen, Q.; Liu, H.; Peterson, N.B.; Dai, Q.; Levy, M.; Shah, A.; Han, X.; Ruan, X.; et al. Validating drug repurposing signals using electronic health records: A case study of metformin associated with reduced cancer mortality. J. Am. Med. Inform. Assoc. 2014, 1–10. [Google Scholar] [CrossRef] [PubMed]
  111. Brown, A.S.; Patel, C.J. A review of validation strategies for computational drug repositioning. Brief. Bioinform. 2018, 19, 174–177. [Google Scholar] [CrossRef] [PubMed]
  112. Lee, W.H. Open access target validation is a more efficient way to accelerate drug discovery. PLoS Biol. 2015, 13, 1–9. [Google Scholar] [CrossRef] [PubMed]
  113. Carter, A.J.; Donner, A.; Lee, W.H.; Bountra, C. Establishing a reliable framework for harnessing the creative power of the scientific crowd. PLoS Biol. 2017, 15. [Google Scholar] [CrossRef] [PubMed]
  114. Rijnders, T.W.; Tzalis, D.; Jaroch, S. The European lead factory—An experiment in colla- borative drug discovery. J. Med. Dev. Sci. 2015, 1, 20–33. [Google Scholar]
  115. Munos, B. Can open-source drug RD repower pharmaceutical innovation? Clin. Pharmacol. Ther. 2010, 87, 534–536. [Google Scholar] [CrossRef] [PubMed]
  116. Lakhani, K.R.; Boudreau, K.J.; Loh, P.R.; Backstrom, L.; Baldwin, C.; Lonstein, E.; Lydon, M.; MacCormack, A.; Arnaout, R.A.; Guinan, E.C. Prize-based contests can provide solutions to computational biology problems. Nat. Biotechnol. 2013, 31, 108–111. [Google Scholar] [CrossRef] [PubMed]
  117. Bentzien, J.; Muegge, I.; Hamner, B.; Thompson, D.C. Crowd computing: Using competitive dynamics to develop and refine highly predictive models. Drug Discov. Today 2013, 18, 472–478. [Google Scholar] [CrossRef] [PubMed]
  118. Patel, A.C.; Coyle, A.J. Building a new biomedical ecosystem: Pfizer’s centers for therapeutic innovation. Clin. Pharmacol. Ther. 2013, 94, 314–316. [Google Scholar] [CrossRef] [PubMed]
  119. Wang, Z.; Monteiro, C.D.; Jagodnik, K.M.; Fernandez, N.F.; Gundersen, G.W.; Rouillard, A.D.; Jenkins, S.L.; Feldmann, A.S.; Hu, K.S.; McDermott, M.G.; et al. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat. Commun. 2016, 7. [Google Scholar] [CrossRef] [PubMed]
  120. Dorsch, H.; Jurock, A.E.; Schoepe, S.; Lessl, M.; Asadullah, K. Grants4Targets: An open innovation initiative to foster drug discovery collaborations. Nat. Rev. Drug Discov. 2014, 14, 74. [Google Scholar] [CrossRef] [PubMed]
  121. Markoff, J. Scientists see advances in deep learning. New York Times 2012. [Google Scholar]
  122. Alvim-Gaston, M.; Grese, T.; Mahoui, A.; Palkowitz, A.; Pineiro-Nunez, M.; Watson, I. Open Innovation Drug Discovery (OIDD): A Potential Path to Novel Therapeutic Chemical Space. Curr. Top. Med. Chem. 2014, 14, 294–303. [Google Scholar] [CrossRef] [PubMed]
  123. Margolin, A.A.; Bilal, E.; Huang, E.; Norman, T.C.; Ottestad, L.; Mecham, B.H.; Sauerwine, B.; Kellen, M.R.; Mangravite, L.M.; Furia, M.D.; et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci. Transl. Med. 2013, 5. [Google Scholar] [CrossRef] [PubMed]
  124. Aksulu, A.; Wade, M. A Comprehensive Review and Synthesis of Open Source Research. J. Assoc. Inf. Syst. 2010, 11, 576–656. [Google Scholar] [CrossRef]
  125. Stajich, J.E.; Lapp, H. Open source tools and toolkits for bioinformatics: Significance, and where are we? Brief. Bioinform. 2006, 7, 287–296. [Google Scholar] [CrossRef] [PubMed]
  126. Kluyver, T.; Ragan-kelley, B.; Pérez, F.; Granger, B.; Bussonnier, M.; Frederic, J.; Kelley, K.; Hamrick, J.; Grout, J.; Corlay, S.; et al. Jupyter Notebooks—A publishing format for reproducible computational workflows. Position. Power Acad. Publ. Play. Agents Agendas 2016, 87–90. [Google Scholar] [CrossRef]
  127. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar] [CrossRef]
  128. Demšar, J.; Curk, T.; Erjavec, A.; Hočevar, T.; Milutinovič, M.; Možina, M.; Polajnar, M.; Toplak, M.; Starič, A.; Stajdohar, M.; et al. Orange: Data Mining Toolbox in Python. J. Mach. Learn. Res. 2013, 14, 23492353. [Google Scholar]
  129. Hall, M.A.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. SIGKDD Explor. 2009, 11, 10–18. [Google Scholar] [CrossRef]
  130. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlow: A System for Large-Scale Machine Learning TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16), Savannah, Georgia, 22 August 2016; pp. 265–284. [Google Scholar]
  131. Chollet, F. Keras. Available online: https://github.com/fchollet/keras (accessed on 23 April 2018).
  132. Paszke, A.; Chanan, G.; Lin, Z.; Gross, S.; Yang, E.; Antiga, L.; Devito, Z. Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst. 2017, 30, 1–4. [Google Scholar]
  133. Yu, D.; Huang, X. Microsoft Computational Network Toolkit (CNTK). In Neural Information Processing Systems (NIPS); Palais des Congrès de Montréal: Montréal, QC, Canada, 2015. [Google Scholar]
  134. Vedaldi, A.; Lenc, K. MatConvNet—Convolutional Neural Networks for MATLAB. arXiv, 2014. [Google Scholar]
  135. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, San Jose, CA, USA, 17–20 May 2009; pp. 361–362. [Google Scholar]
  136. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  137. Corsello, S.M.; Bittker, J.A.; Liu, Z.; Gould, J.; McCarren, P.; Hirschman, J.E.; Johnston, S.E.; Vrcic, A.; Wong, B.; Khan, M.; et al. The Drug Repurposing Hub: A next-generation drug library and information resource. Nat. Med. 2017, 23, 405–408. [Google Scholar] [CrossRef] [PubMed]
  138. Jia, Z.; Liu, Y.; Guan, N.; Bo, X.; Luo, Z.; Barnes, M.R. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery. BMC Genomics 2016, 17. [Google Scholar] [CrossRef] [PubMed]
  139. Altae-Tran, H.; Ramsundar, B.; Pappu, A.S.; Pande, V. Low Data Drug Discovery with One-Shot Learning. ACS Cent. Sci. 2017, 3, 283–293. [Google Scholar] [CrossRef] [PubMed]
  140. Zhou, H.; Gao, M.; Skolnick, J. Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci. Rep. 2015, 5. [Google Scholar] [CrossRef] [PubMed]
  141. Douguet, D. e-LEA3D: A computational-aided drug design web server. Nucleic Acids Res. 2010, 38. [Google Scholar] [CrossRef] [PubMed]
  142. Miteva, M.A.; Guyon, F.; Tufféry, P. Frog2: Efficient 3D conformation ensemble generator for small compounds. Nucleic Acids Res. 2010, 38. [Google Scholar] [CrossRef] [PubMed]
  143. Zu, S.; Chen, T.; Li, S. Global optimization-based inference of chemogenomic features from drug-target interactions. Bioinformatics 2015, 31, 2523–2529. [Google Scholar] [CrossRef] [PubMed]
  144. Louhimo, R.; Laakso, M.; Belitskin, D.; Klefström, J.; Lehtonen, R.; Hautaniemi, S. Data integration to prioritize drugs using genomics and curated data. BioData Min. 2016, 9. [Google Scholar] [CrossRef] [PubMed]
  145. Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E. The Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 2003, 43, 493–500. [Google Scholar] [CrossRef] [PubMed]
  146. Duan, Q.; Reid, S.P.; Clark, N.R.; Wang, Z.; Fernandez, N.F.; Rouillard, A.D.; Readhead, B.; Tritsch, S.R.; Hodos, R.; Hafner, M.; et al. L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst. Biol. Appl. 2016, 2, 16015. [Google Scholar] [CrossRef] [PubMed]
  147. Carrella, D.; Napolitano, F.; Rispoli, R.; Miglietta, M.; Carissimo, A.; Cutillo, L.; Sirci, F.; Gregoretti, F.; Di Bernardo, D. Mantra 2.0: An online collaborative resource for drug mode of action and repurposing by network analysis. Bioinformatics 2014, 30, 1787–1788. [Google Scholar] [CrossRef] [PubMed]
  148. O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An Open chemical toolbox. J. Cheminform. 2011, 3. [Google Scholar] [CrossRef] [PubMed]
  149. Williams, A.J.; Harland, L.; Groth, P.; Pettifer, S.; Chichester, C.; Willighagen, E.L.; Evelo, C.T.; Blomberg, N.; Ecker, G.; Goble, C.; et al. Open PHACTS: Semantic interoperability for drug discovery. Drug Discov. Today 2012, 17, 1188–1198. [Google Scholar] [CrossRef] [PubMed]
  150. Garfield, E. The Who and Why of ISI. Essays of Information Scientist. 1966. Available online: http://garfield.library.upenn.edu/essays/V1p033y1962-73.pdf (accessed on 1 June 2018).
  151. Repeat after me. Nature 2012, 488, 253. [CrossRef]
  152. Begley, C.G.; Ellis, L.M. Drug development: Raise standards for preclinical cancer research. Nature 2012, 483, 531–533. [Google Scholar] [CrossRef] [PubMed]
  153. Baker, M. Irreproducible biology research costs put at $28 billion per year. Nature 2015. [Google Scholar] [CrossRef]
  154. Peng, R.D. Reproducible research in computational science. Science 2011, 334, 1226–1227. [Google Scholar] [CrossRef] [PubMed]
  155. Gentleman, R. Reproducible Research: A Bioinformatics Case Study. Stat. Appl. Genet. Mol. Biol. 2005, 4. [Google Scholar] [CrossRef] [PubMed]
  156. Guney, E. Reproducible Drug Repurposing: When Similarity Does Not Suffice. Pac. Symp. Biocomput. 2016, 22, 132–143. [Google Scholar]
  157. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; da Silva Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016, 3, 160018. [Google Scholar] [CrossRef] [PubMed]
  158. Torre, D.; Krawczuk, P.; Jagodnik, K.M.; Lachmann, A.; Wang, Z.; Wang, L.; Kuleshov, M.V.; Ma’Ayan, A. Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses. Sci. Data 2018, 5. [Google Scholar] [CrossRef] [PubMed]
  159. Ram, K. Git can facilitate greater reproducibility and increased transparency in science. Source Code Biol. Med. 2013, 8. [Google Scholar] [CrossRef] [PubMed]
  160. Merkel, D. Docker: Lightweight Linux containers for consistent development and deployment. Linux J. 2014, 2014, 2. [Google Scholar]
Table 1. Examples of repositioned drugs (adapted in part from [11], this list is neither extensive nor exhaustive).
Table 1. Examples of repositioned drugs (adapted in part from [11], this list is neither extensive nor exhaustive).
DrugOriginal IndicationNew Indication
AllopurinolCancerGout
AmantadineInfluenzaParkinson’s disease
AmphotericinAntifungalLeishmaniasis
ArsenicSyphilisLeukemia
AspirinInflammation, painAntiplatelet
AtomexetineDepressive disorderADHD
BimatoprostGlaucomaPromoting eyelash growth
Bromocriptine Parkinson’s diseaseDiabetes mellitus
BupropionDepressionSmoking cessation
ColchicineGoutRecurrent pericarditis
ColesevelamHyperlipidemiaType 2 diabetes mellitus
DapsoneLeprosyMalaria
DisulfiramAlcoholismMelanoma
DoxepinDepressive disorderAntipruritic
EflornithineDepressionADHD
FinasterideBenign prostatic hyperplasiaMale pattern baldness
GabapentinEpilepsyNeuropathic pain
GemcitabineAntiviralCancer
LomitapideLipidemiaFamilial hypercholesterolemia
MethotrexateCancerPsoriasis, rheumatoid arthritis
MiltefosineCancerVisceral leishmaniasis
MinoxidilHypertensionHair loss
NaltrexoneOpioid addictionAlcohol withdrawal
NaproxenInflammation, painAlzheimer’s disease
NortriptylineDepressionNeuropathic pain
PremetrexedMesotheliomaLung cancer
PropranololHypertensionMigraine prophylaxis
RaloxifeneContraceptiveOsteoporosis
SildenafilAnginaErectile dysfunction; pulmonary hypertension
ThalidomideMorning sicknessLeprosy; multiple myeloma
TretinoinAcneLeukemia
ZidovudineCancerHIV/AIDS
ZileutonAsthmaAcne
Table 2. Drug and Disease Centric Database Resources.
Table 2. Drug and Disease Centric Database Resources.
DatabaseTypeDescriptionURLRef.
ADReCSDrugSystem Toxicology and in silico drug safety evaluation. Contains 137,619 Drug-ADR pairshttp://bioinf.xmu.edu.cn/ADReCS/[41]
ChEMBLDrugDatabase of bioactive drug-like small molecules and abstracted bioactivitieshttps://www.ebi.ac.uk/chembl[42]
ChemSpiderDrugDatabase of 64 million chemical structureshttp://www.chemspider.com/[43]
Clue (L1000 Platform) DrugDataset of transcriptional responses of human cells to chemical and genetic perturbation. 1.2 Million L1000 profiles and tools for their analysis.https://clue.io/[44]
Comparative Toxicogenomics Database DrugAssociations of Drug-Gene, Gene-Disease, Drug-Disease and gene-genehttp://ctdbase.org/[45]
DailyMEDDrugCatalogue of drug listings/drug label information https://dailymed.nlm.nih.gov/dailymed/[46]
DGIdbDrugDrug-gene annotations, interactions and potential drug ability databasehttp://dgidb.org/[47]
DrugBankDrugContains 11,000 drug entries and each entry contains more than 200 data fields of chemical information and drug targets.https://www.drugbank.ca/[48]
DrugCentralDrugInformation on active ingredients chemical entities, pharmaceutical products, drug mode of action, indications, pharmacologic actionhttp://drugcentral.org/[49]
e-Drug3D Druge-Drug3D offers a facility to explore FDA approved drugs and active metaboliteshttp://chemoinfo.ipmc.cnrs.fr/MOLDB/index.html[50]
Genomics of Drug Sensitivity in Cancer (GDSC) DrugScreenings of >1000 genetically characterized human cancer cell lines with a wide range of anti-cancer therapeuticshttp://www.cancerrxgene.org/[51]
Inxight DrugsDrugA comprehensive portal for drug development information from NCATShttps://drugs.ncats.io/ginas/app
Open Targets PlatformDrugcomprehensive and robust data integration for access to and visualization of potential drug targets associated with diseasehttps://www.targetvalidation.org[52]
PharmGKB DrugCurated dataset of genetic variation on drug responsehttps://www.pharmgkb.org/[53]
pkCSMDrugSmall-molecule pharmacokinetic (ADMET) properties prediction using SMILE datahttp://biosig.unimelb.edu.au/pkcsm/prediction[54]
Project Achilles DrugA genome-wide catalog of tumor dependencies, to identify vulnerabilities associated with genetic and epigenetic alterationshttps://portals.broadinstitute.org/achilles[55]
Promiscuous DrugDatabase contains three different types of entities: drugs, proteins and side-effects as well as relations between themhttp://bioinformatics.charite.de/promiscuous/[56]
PubChemDrugPubChem contains more than 90 million compounds chemical information along with their bio activities, gene and protein targetshttp://pubchem.ncbi.nlm.nih.gov/[57]
SIDERDrugInformation on marketed medicines and their recorded adverse drug reactionshttp://sideeffects.embl.de/[58]
STITCHDrug68,000 chemicals, interactions and over 1.5 million proteins in 373 specieshttp://stitch.embl.de/[59]
SuperPred DrugA prediction webserver for ATC code and target prediction of compoundshttp://prediction.charite.de/[60]
Therapeutic Target Database (TTD) DrugDataset of known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targethttp://bidd.nus.edu.sg/group/cjttd/[61]
Toxin and Toxin-Target Database (T3DB)DrugA database of 3673 toxins described by 41,733 synonyms, including pollutants, pesticides, drugs, and food toxins, which are linked to 2087 corresponding toxin target recordshttp://www.t3db.ca/[62]
Human Protein AtlasDisease and DrugConsists of three separate parts; the Tissue Atlas showing the distribution of the proteins across all major tissues and organs in the human body, the Cell Atlas showing the subcellular localization of proteins in single cells, and finally the Pathology Atlas showing the impact of protein levels for survival of patients with cancer.https://www.proteinatlas.org/[63]
KEGG MedicusDisease and DrugCollection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substanceshttp://www.genome.jp/kegg/disease/
http://www.kegg.jp/
http://www.genome.jp/kegg/drug/
[64]
PsychEncodeDisease https://www.synapse.org//#!Synapse:syn4921369/wiki/235539[65]
Allen Brain AtlasDiseaseGene expression maps for mouse and human brainhttp://www.brain-map.org/[66]
ArrayExpressDiseaseMicro array gene expression data at EBIhttps://www.ebi.ac.uk/arrayexpress[67]
CCLEDiseaseDatabase of mRNA expression and mutation data over 1100 cancer cell lineshttps://portals.broadinstitute.org/ccle[68]
COSMIC DiseaseCatalogue of somatic mutations in human cancerhttp://cancer.sanger.ac.uk/cosmic[69]
dbGAPDiseaseCatalogue of somatic mutations causing cancerhttp://www.ncbi.nlm.nih.gov/gap[70]
dbSNPDiseaseDatabase of single nucleotide polymorphismshttps://www.ncbi.nlm.nih.gov/snp[71]
dbVarDiseasePublic archives for genomic structural variationhttps://www.ncbi.nlm.nih.gov/dbvar[72]
DisGeNET DiseaseDatabase on human disease-associated genes and variantshttp://www.disgenet.org/[73]
ENCODE DiseaseDatabase of comprehensive parts list of functional elements in human genomehttps://genome.ucsc.edu/ENCODE/[20]
Genomics Data CommonsDiseaseHarmonized Cancer Datasets with 40 cancer mutated gene projects, 22,147 Genes and 3 million mutationshttps://gdc.cancer.gov/[74]
GEODiseaseHigh throughput gene expression datasetshttp://www.ncbi.nlm.nih.gov/geo[75]
GTex DiseaseCatalog of genetic variations and their influence on gene expressionshttps://www.gtexportal.org/home/[76]
Human Proteome Map DiseaseInteractive resource with massive peptide sequencing resultshttp://www.humanproteomemap.org/[77]
ICGCDiseaseDataset with more than 17,000 cancer donors spanning 76 projects and 21 tumor siteshttp://icgc.org/[78]
IGSRDisease1000 genome project data usability and extensionhttp://www.internationalgenome.org/[79]
OrphadataDiseaseRare diseases, drugs and associated geneshttp://www.orphadata.org/cgi-bin/index.php/[80]
Roadmap EpigenomicsDiseaseEpigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human diseasehttp://www.roadmapepigenomics.org/[81]
STRINGDiseaseProtein-Protein interaction, analysis, and networkshttps://string-db.org/cgi/input.pl[82]
Table 3. Open innovation research resources.
Table 3. Open innovation research resources.
NameDescriptionURL
Centers for Therapeutic Innovation (CTI)Collaborative research platform for clinical applications and drug discovery [118]https://www.pfizercti.com
CREEDSCrowd-extracted expression of differential signatures [119]http://amp.pharm.mssm.edu/CREEDS
Grants4LeadsFinancial support for exploration of new approaches in infectious diseases [120]https://www.grants4leads.com/
KaggleData scientists and statisticians competition platform with few bioinformatics challenges [117,121]http://www.kaggle.com/
Open Innovation Drug DiscoveryAcademic and Industry researchers open collaboration platform for drug discovery [122]https://openinnovation.lilly.com/dd/
Sage BionetworksBioinformatics and data science challenge platform building prognostic models for breast cancer [123]http://sagebionetworks.org/
TopCoderMachine learning engineers, programmers and data scientists challenge platform [116]http://www.topcoder.com
Table 4. NIH-NCATS funded new therapeutic uses projects (2013–2018).
Table 4. NIH-NCATS funded new therapeutic uses projects (2013–2018).
Project/Study TitleYearNCATS ProgramCondition
The Efficacy and Safety of a Selective Estrogen Receptor Beta Agonist (LY500307)2013NIH-Industry PartnershipSchizophrenia
Fyn Inhibition by AZD0530 for Alzheimer’s Disease2013NIH-Industry PartnershipAlzheimer’s disease
Medication Development of a Novel Therapeutic for Smoking Cessation2013NIH-Industry PartnershipCigarette smoking
A Novel Compound for Alcoholism Treatment: A Translational Strategy2013NIH-Industry PartnershipAlcoholism
Partnering to Treat an Orphan Disease: Duchenne Muscular Dystrophy2013NIH-Industry PartnershipDuchenne muscular dystrophy
Reuse of ZD4054 for Patients with Symptomatic Peripheral Artery Disease2013NIH-Industry PartnershipPeripheral artery disease
Therapeutic Strategy for Lymphangioleiomyomatosis2013NIH-Industry PartnershipLymphangioleiomyomatosis
Therapeutic Strategy to Slow Progression of Calcific Aortic Valve Stenosis2013NIH-Industry PartnershipCalcific aortic valve stenosis
Translational Neuroscience Optimization of GlyT1 Inhibitor2013NIH-Industry PartnershipSchizophrenia
Anti-inflammatory Small Drug as Adjunctive Therapy to Improve Glucometabolic Variables in Obese, Insulin-Resistant Type 2 Diabetic Patients2015NIH-Industry PartnershipInsulin-resistant type 2 diabetes
Evaluation of AZD9291 in Glioblastoma Patients with Activated EGFR2015NIH-Industry PartnershipGlioblastoma
Evaluation of a Cathepsin S Inhibitor as a Potential Drug for Chagas Disease2015NIH-Industry PartnershipChagas disease
Wee1 and HDAC Inhibition in Relapsed/Refractory AML2015NIH-Industry PartnershipRelapsed/refractory AML
Anti-Virulence Drug Repurposing Using Structural Systems Pharmacology2016Bench-to-ClinicBacterial virulence
CXCR2 Antagonism in the Immunometabolic Regulation of Type 2 Diabetes2016Bench-to-ClinicType 2 diabetes
Drug Repositioning in Diabetic Nephropathy2016Bench-to-ClinicDiabetic nephropathy
Ketorolac and Related NSAIDs for Targeting Rho-Family GTPases in Ovarian Cancer2016Bench-to-ClinicOvarian cancer
Network-Driven Drug Repurposing Approaches to Treat Coronary Artery Disease2016Bench-to-ClinicCoronary artery disease
Pre-Clinical Evaluation of a Neutrophil Elastase Inhibitor for the Treatment of Inflammatory Bowel Disease2016Bench-to-ClinicInflammatory bowel disease
Quantum Model Repurposing of Cethromycin for Liver Stage Malaria2016Bench-to-ClinicLiver-stage malaria
Repurposing Lesogaberan for the Treatment of Type 1 Diabetes2016Bench-to-ClinicType 1 diabetes
Repurposing Misoprostol for Clostridium Difficile Colitis as Identified by PheWAS2016Bench-to-ClinicClostridium difficile colitis
Repurposing Pyronaridine as a Treatment for the Ebola Virus2016Bench-to-ClinicEbola virus
Therapeutic Repurposing of Benserazide for Colon Cancer2016Bench-to-ClinicColon cancer
Computational Repurposing of Chemotherapies for Pulmonary Hypertension2017Bench-to-ClinicPulmonary hypertension
Pre-Clinical Evaluation of Vorinostat in Alopecia Areata2017Bench-to-ClinicAlopecia areata
Pre-Clinical Testing of a Novel Therapeutic for Nonalcoholic Steatohepatitis2017Bench-to-ClinicNonalcoholic steatohepatitis
Repurposing Pyronaridine as a Treatment for Chagas Disease2017Bench-to-ClinicChagas disease
Single-Cell-Driven Drug Repositioning Approaches to Target Inflammation in Atherosclerosis2017Bench-to-ClinicAtherosclerosis
Impact of SAR152954 on Prenatal Alcohol Exposure-Induced Neurobehavioral Deficits2017Bench-to-ClinicNeurobehavioral deficits
An Endoplasmic Reticulum Calcium Stabilizer for the Treatment of Wolfram Syndrome2017Bench-to-ClinicWolfram syndrome
Utilization of Phenotypic Precision Medicine to Identify Optimal Drug Combinations for the Treatment of Hepatocellular Carcinoma2017Bench-to-ClinicHepatocellular carcinoma
Targeting Glucose Metabolism for the Treatment of Hepatocellular Carcinoma2017Bench-to-ClinicHepatocellular carcinoma
Application of a Repurposed FDA Approved Drug as a Local Osteogenic Agent2017Bench-to-ClinicTo induce local osteogenesis
Repurposing Misoprostol to Prevent Recurrence of Clostridium Difficile Infection2018Bench-to-ClinicRecurrent Clostridium difficile
AZD9668: A First in Class Disease Modifying Therapy to Treat Alpha-1 Antitrypsin Deficiency, a Genetically Linked Orphan Disease2018NIH-Industry PartnershipAlpha-1 antitrypsin deficiency
AZD9668 and Neutrophil Elastase Inhibition to Prevent Graft-versus-Host Disease2018NIH-Industry PartnershipGraft-versus-host disease
Use of the Src Family Kinase Inhibitor Saracatinib in the Treatment of Pulmonary Fibrosis2018NIH-Industry PartnershipPulmonary fibrosis
Table 5. Web-tools and open source kits.
Table 5. Web-tools and open source kits.
ToolDescriptionURLRef
ClueTools for perturbagens (small molecules or genes) query, L1000 cohorts, and gene expression heatmap visualizationhttps://clue.io[44]
Clue Repurposing ToolInteractive application to access approved and pre-clinical drug annotationshttps://clue.io/repurposing[137]
COGENAAnalysis, visualizing and clustering tool for gene expression profileshttps://github.com/zhilongjia/cogena[138]
DeepChemDeep learning toolkit for drug discovery and cheminformaticshttps://deepchem.io/[139]
DR.PRODISPrediction of drug-protein interactions, side effects http://cssb.biology.gatech.edu/repurpose[140]
e-LEA3DCollection of tools related to computer-aided drug designhttp://chemoinfo.ipmc.cnrs.fr/[141]
Frog2Chemo-informatics toolkit for small compound 3D generation from 1D/2D inputhttp://bioserv.rpbs.univ-paris-diderot.fr/services/Frog2/[142]
GIFTInfer chemogenomic features from drug-target interactions.http://bioinfo.au.tsinghua.edu.cn/software/GIFT/[143]
GoPredictDrug target prioritization tool for breast and ovarian cancerhttp://csblcanges.fimm.fi/GOPredict/[144]
JOELib/JOELib2Toolkit to interconvert chemical file formats, descriptor calculation classes, and SMARTS substructure searchhttp://www.ra.cs.uni-tuebingen.de/software/joelib/introduction.html[145]
ksRepoDrug repositioning tool that utilizes gene expression drug datasets from different platformshttps://github.com/adam-sam-brown/ksRepo[101]
L1000CDSL1000 dataset based gene expression signature search enginehttp://amp.pharm.mssm.edu/L1000CDS2/#/index[146]
MANTRAPrediction and analysis of mechanism of action of drugs for drug repositioninghttp://mantra.tigem.it/[147]
NFFinderTool to discover multiple drugs with similar drugs based on up/down regulated geneshttp://nffinder.cnb.csic.es/[102]
Open babelOpen source chemistry toolboxhttp://openbabel.org/wiki/Main_Page[148]
Open PHACTSEuropean funded initiative to bring together industry and academic partners for semantic integration of pharmacological data using an RDF data modelhttp://www.openphacts.org[149]

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