Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

Li, Jiao; Zhu, Xiaoyan; Chen, Jake Y.

doi:10.1371/journal.pcbi.1000450

Cited by 169 publications

(130 citation statements)

References 52 publications

(64 reference statements)

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“…Systems biology approaches are naturally suited to capture the complexity of drug activity in cells (4)(5)(6). Prediction of drug MoA has been attempted by using gene expression profiles following drug treatment (7)(8)(9)(10)(11)(12)(13), by comparing side-effect similarities (14), by text-mining literature (15), or by applying chemoinformatic tools to search for small molecules similarities (16,17). Most of these approaches are applicable only to well-characterized molecules (e.g., when the structure is available, or side effects are documented).…”

mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

755

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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

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mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

755

728

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“…The known drug-disease indications span 3,250 associations between 799 drugs and 719 diseases and were extracted from the National Drug File -Reference Ter-minology (NDF-RT) as suggested in a previous study by Li and Lu. 21 The data set is publicly available online at http://astro.temple.edu/~tua87106/drugreposition.html. We used the 536 drugs that were common among chemical, target, side effect, and indication data, corresponding to 2,229 drug-disease associations covering 578 diseases and 40,455 drug-side effect associations covering 1,252 side effects.…”

Section: Data Setsmentioning

confidence: 99%

Reproducible Drug Repurposing: When Similarity Does Not Suffice

Güney

2016

Biocomputing 2017

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Repurposing existing drugs for new uses has attracted considerable attention over the past years. To identify potential candidates that could be repositioned for a new indication, many studies make use of chemical, target, and side effect similarity between drugs to train classifiers. Despite promising prediction accuracies of these supervised computational models, their use in practice, such as for rare diseases, is hindered by the assumption that there are already known and similar drugs for a given condition of interest. In this study, using publicly available data sets, we question the prediction accuracies of supervised approaches based on drug similarity when the drugs in the training and the test set are completely disjoint. We first build a Python platform to generate reproducible similaritybased drug repurposing models. Next, we show that, while a simple chemical, target, and side effect similarity based machine learning method can achieve good performance on the benchmark data set, the prediction performance drops sharply when the drugs in the folds of the cross validation are not overlapping and the similarity information within the training and test sets are used independently. These intriguing results suggest revisiting the assumptions underlying the validation scenarios of similarity-based methods and underline the need for unsupervised approaches to identify novel drug uses inside the unexplored pharmacological space. We make the digital notebook containing the Python code to replicate our analysis that involves the drug repurposing platform based on machine learning models and the proposed disjoint cross fold generation method freely available at github. com/emreg00/repurpose.

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“…The development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps was described. Initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment [29]. Negative regulators of basic helix-loop-helix transcription factors, has been implicated in diverse cellular processes such as proliferation, apoptosis, differentiation, and migration.…”

Section: Toxicogenomics Historymentioning

confidence: 99%

Toxicogenomics and cancer pathogenesis

Gupta¹,

AK²,

Boraste³

et al. 2009

Int J Genet

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Abstract-Toxicogenomics is emerging field give idea of the application of large-scale differential gene expression data to toxicology, starting to influence drug discovery and development in the pharmaceutical industry. Its future potential in cancer pathogenesis, study of poisons and poisoning and has an ancient and venerable history. Toxicogenomics is the merging of toxicology with technologies that have been developed, together with bioinformatics, to identify and quantify global gene expression changes for gene therapy. Immunotoxic processes and the development of in vitro screening assays is therefore expected to be of value for mechanistic insight into immunotoxicity and hazard identification of existing and novel compounds. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. Toxicogenomics is considered a valuable tool for reducing pharmaceutical candidate attrition by facilitating earlier identification, prediction and understanding of toxicities. Pharmaco-epidemiological (toxicogenomics) data is now available for both antiepileptic drugs, evidence for human carcinogenicity. Therapeutic researches converge triad of rejuvenation, regeneration or replacement strategies that rely on self-healing processes, stem cell regeneration organ transplantation. Metastases studies usually display more genetic changes than the primary tumour. Helix-loop-helix application in translational and functional toxicogenomics transcription factors has been implicated in diverse cellular processes such as proliferation, apoptosis, differentiation, and migration.

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Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

Cited by 169 publications

References 52 publications

Discovery of drug mode of action and drug repositioning from transcriptional responses

Discovery of drug mode of action and drug repositioning from transcriptional responses

Reproducible Drug Repurposing: When Similarity Does Not Suffice

Toxicogenomics and cancer pathogenesis

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