Systematic identification of proteins that elicit drug side effects

Kuhn, Michael; Banchaabouchi, Mumna Al; Campillos, Mónica; Jensen, Lars Juhl; Gross, Cornelius; Gavin, Anne‐Claude; Bork, Peer

doi:10.1038/msb.2013.10

Cited by 114 publications

(157 citation statements)

References 41 publications

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“…As SIDER2 consists of recognized side effects only, we cannot reliably distinguish between false positive signals and previously unknown ADRs. Furthermore, SIDER was compiled from package insert information by NLP tools [79], and as such may include side effects that seldom occur in practice or false associations that were caused by text-mining errors [150]. While SIDER2 is sufficient to evaluate the hypotheses of the current work, in future work we plan to incorporate other data sources, such as EHR data and FDA reports.…”

Section: Discussionmentioning

confidence: 99%

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Shang

Rindflesch

et al. 2014

Journal of Biomedical Informatics

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Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to “rediscover” these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

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Section: Discussionmentioning

confidence: 99%

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Shang

Rindflesch

et al. 2014

Journal of Biomedical Informatics

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“…To evaluate the side effects of the drugs, we considered SIDER2 (http://sideeffects.embl.de/, last accessed October 10, 2014) database (Kuhn et al 2013). Thereby, we compiled a list of 996 drugs associated with 4,192 side effects.…”

Section: Methodsmentioning

confidence: 99%

Elucidating the Genotype–Phenotype Relationships and Network Perturbations of Human Shared and Specific Disease Genes from an Evolutionary Perspective

Begum

Ghosh

2014

Genome Biology and Evolution

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To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

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“…Second, many associations go undiscovered in cases of a small number of drugs interacting with a particular protein (e.g. fewer than five drugs per target as mentioned in some publications) [53]. Therefore, estimated profiles are relatively small (few hundred targets) and include mostly therapeutic drug targets as well as metabolizing enzymes and transporters, but only a few off-target proteins.…”

Section: Drug Targets and Pathwaysmentioning

confidence: 98%

“…As an example, Kuhn et al performed a large-scale search for potential ADR-protein associations and found corresponding direct and ://www.informatics.jax.org/) for most of the identified associations[53]. If there is no directinformation in the literature, the functions of revealed proteins can be compared to the functions of proteins with known associations to the corresponding disease.…”

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confidence: 98%

In silico assessment of adverse drug reactions and associated mechanisms

Ivanov

Lagunin

Poroikov

2016

Drug Discovery Today

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Systematic identification of proteins that elicit drug side effects

Abstract: Protein–side effects associations are identified by integrating drug–target data with side effects information from drug labels. Benchmarking against the literature and validation with an in vivo mouse model shows that these pairs correspond to causal relations.

Cited by 114 publications

References 41 publications

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Elucidating the Genotype–Phenotype Relationships and Network Perturbations of Human Shared and Specific Disease Genes from an Evolutionary Perspective

In silico assessment of adverse drug reactions and associated mechanisms

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