Characterization of Schizophrenia Adverse Drug Interactions through a Network Approach and Drug Classification

Sun, Jingjing; Zhao, Min; Fanous, Ayman H.; Zhao, Zhongming

doi:10.1155/2013/458989

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…The rationale of this inference was based on the assumption that drugs having same targets with SCZ drugs, or drugs whose targets have direct interactions with SCZ drug target, might be potential SCZ drugs. In our previous study, we collected 32 SCZ drugs [ 36 ]. In this study, we updated the drug list by manually checking the drugs' indication field in the DrugBank.…”

Section: Methodsmentioning

confidence: 99%

A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

et al. 2015

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BackgroundComputational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations.ResultsIn this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP Ki Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance.ConclusionsThe WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing.

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Section: Methodsmentioning

confidence: 99%

A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

et al. 2015

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“…These existing works suggest that various types of data are potentially useful in predicting drug properties. Network-based approaches have been proposed to discover novel drug-target interactions [5]- [7], drug-drug interactions [8], [9], [11], and drug adverse reactions [10]. In these approaches, networks containing drugs and drug property entities are created, and the network features, such as common neighbors or the number of paths, are used to predict drug properties.…”

Section: Related Workmentioning

confidence: 99%

“…We utilize the structure of the drug graph to quantify the edge likelihood. As it has been shown that the structure of networks that represent relationships among drugs and drug properties can effectively help to discover novel drug properties [5]- [11], we quantify edge likelihood based on the number of paths between two nodes. However, in contrast to previous works in which the networks contain limited types of nodes, our drug graph contains various node types, and our approach takes into account the types of nodes along the paths in order to leverage the semantics in the heterogeneous drug graph.…”

Section: Introductionmentioning

confidence: 99%

Supporting Drug Prescription via Predictive and Personalized Query System

Khemmarat

Gao

2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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Drug prescription requires consideration of several factors, such as drug interactions and side effects. The process is further complicated by the fact that the presence of some drug properties, such as side effects, depends on patient characteristics, such as age and gender. Our goal is to provide a tool to assist medical practitioners in prescribing drugs. We develop an approach to query for drugs that satisfy a set of conditions based on drug properties. Furthermore, the approach tailors the answers to a given patient profile. We utilize drug information from multiple sources. However, data from these sources are usually noisy and incomplete as they are either manually curated or automatically extracted from text resources. To cope with incomplete and noisy data, our approach considers both the answers that exactly match and those that closely match the query. We represent drug information as a heterogeneous graph and model answering a query as a subgraph matching problem. To rank answers, our approach leverages the structure and the heterogeneity of the drug graph to quantify the likelihood of missing edges and integrates the likelihood into the scores of the answers. Our evaluation shows that for quantifying the edge likelihood, our graph-based approach can improve the AUROC (Area under Receiver Operating Characteristic) [1] by up to 40%, comparing to a baseline approach. We demonstrate the benefits of our system through several query examples.

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“…Other drug-target databases have become available to assist further computational analyses [ 9 , 10 ]. Furthermore, the concept of network medicine has been proposed and various approaches have been developed to assist with drug-drug and drug-target discovery [ 11 – 14 ]. We recently applied network pharmacology approach to exploring the features of antipsychotic and illicit drugs as well as their targets and found some interesting drug-target interaction features [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Network-Assisted Prediction of Potential Drugs for Addiction

Sun

Huang

et al. 2014

BioMed Research International

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Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.

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Characterization of Schizophrenia Adverse Drug Interactions through a Network Approach and Drug Classification

Cited by 14 publications

References 25 publications

A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

Supporting Drug Prescription via Predictive and Personalized Query System

Network-Assisted Prediction of Potential Drugs for Addiction

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