BACKGROUND
the actions of drugs have been systematically observed and recorded by governments, non-trading organizations, and academic institutions.
from phenotypic screening to post-marketing surveillance, abundant reports have been archived and follow-up studies on the mechanisms of action of drugs have been conducted.
although this research delivers us advances in knowledge, our understanding of drug actions is generally biased toward intended therapeutic effects and frequent adverse reactions.
this partiality has caused delays in deciphering the mechanisms of unintended drug actions.
historically, it was inevitable that the discovery of unexpected drug actions, regardless of whether they are desirable or not, usually depends on empirical detection .
however, an unbiased analysis of drug actions should be a basis for understanding unintended drug responses and predicting drug-repositioning opportunities or undesirable reactions.
the rapidly expanding databases and newly available data in the literature, including pharmacogenomic biomarkers, drug-induced gene expression profiles, and drug side-effect information, continually provide clues which indicate unknown drug actions  <cit> .
recently, computational approaches for systematic analyses of these data have been highlighted, enhancing both the availability and usability of the data  <cit> .
in comparison to in vitro and in vivo experiments, computational approaches are remarkable in terms of time and cost efficiency.
moreover, systematic implementations are reproducible.
these implementations can be utilized for upcoming drugs as well as failed drugs, but a lack of appropriate methods creates an arduous task for those who attempt to integrate and utilize these scattered pieces of evidence.
for a comprehensive understanding of drug action, it is necessary to organize and analyze drug-signaling pathways in a systematic manner.
there have been many attempts to predict drug actions based on similar properties of drugs, including their targets, chemical structures and side effects  <cit> .
although these properties are fairly useful for distinguishing repurposed drugs, these attempts tend to depend on the extrinsic properties of drugs and not on the intrinsic mechanisms of drug actions.
therefore, the findings are limited.
one of the most tangible mechanisms of action is a network in which the nodes refer to biomolecules and the edges refer to the physical interaction between two nodes  <cit> .
it should be noted that drugs exert their effects through multiple signaling cascades in a molecular network rather than through a single gene or a single route.
therefore, we need to devise a network platform which realistically infers the drug-signaling pathways.
previously, few methods attempted to design drug-signaling pathways at the molecular level in order to identify a novel pathway for a particular drug  <cit> .
however, these methods tended to utilize limited resources to generate the pathway or do not consider the directionality of biological networks.
moreover, systematic approaches to represent the perturbation of molecular and cellular responses are lacking, as the field is in its infancy.
here, we devise a novel platform, called drug voyager , on which to construct drug-signaling pathways for different drugs .
with this platform, the molecular-level action of a drug is represented by connecting the three conceptual levels of “initiation,” “perturbation,” and “destination.” each level includes a combination of the five types of seed genes related to drug responses and phenotypes: drug target genes , pharmacogenomic variant genes , differentially expressed genes , disease genes , and side-effect genes .
as a consequence of construction of level-to-level pathways,  <dig> drug-signaling pathways were generated in total for  <dig> drugs.
in the validation step, these pathways were significantly enriched in known drug pathway databases and show higher significance levels compared to when other models are used.fig.
<dig> method overview: a drug voyager representing the molecular-level actions of drugs by connecting three conceptual levels; b constructing a pathway from the starting point to the end point; and c transformer, utilizing drug-specific pathways to predict drug indications and adverse reactions
furthermore, we present transformer, a new method for translating the functional features of drug-signaling pathways into new medicine and adverse reactions.
based on the drug-signaling pathways generated by drug voyager and a snapshot of the cross-talk within each pathway, transformer predicts drug indications and adverse reactions.
our predictions for therapeutic use significantly overlaps with drug indications currently tested in clinical trials and an up-to-date drug-disease association database.
when used to predict pubchem bioassay results, transformer surpasses existing methods in terms of its ability to classify active compounds for cancers.
for adverse reactions, our predictions show high significance levels in enrichment testing for an independent database derived from adverse event reporting system  <cit>  of the food and drug administration  and meaningfully cover the adverse reaction database provided from health canada  <cit> .
lastly, we highlight several predictions for both therapeutic indications and side effects through pathway analyses and from published reports.
RESULTS
validation of drug-signaling pathways
as noted above, we devised a novel platform, termed drug voyager, which constructs drug-signaling pathways for individual drugs.
in this platform, the three conceptual levels of initiation, perturbation, and destination were adopted to imitate drug perturbations.
we assume that a signal transduction cascade in drug action begins at the initiation level and reaches the destination level through the perturbation level.
given the genes that contribute to drug responses and phenotypes, they were assigned to corresponding levels and the mechanism of action of a drug was delineated by connecting these three conceptual levels .
among all the fda approved drugs, some drugs whose corresponding genes are unknown have been filtered out, and finally  <dig> drugs remain.
by constructing level-to-level pathways, drug voyager built  <dig> drug-signaling pathways, one for each of  <dig> drugs.
the validity of each of these pathways was evaluated by the following three observations: 1) enrichment in the small molecule pathway database   <cit> , 2) enrichment in pharmgkb  <cit> , and 3) the co-occurrence of the drug and genes in the literature.
we then compared drug voyager with other pathway construction models.
we undertook gene enrichment computations in the curated drug-action pathways extracted from smpdb  <cit> .
the drug pathways from smpdb were generated based on various medical and pharmacology textbooks, as well as relevant published reviews and online databases such as kegg  <cit>  and the medical biochemistry page  <cit> .
among the  <dig> drugs, information about the drug-action pathways was accessible for  <dig>  and we concentrated on  <dig> of these which had pathways composed of ten or more elements.
twenty-one  of the  <dig> drug-signaling pathways were significantly enriched in the curated drug-action pathways .
in addition, the validity of the drug-signaling pathways was tested by comparing these pathways to the pharmacodynamic pathways derived from pharmgkb  <cit> .
pharmacodynamic pathways depict the pharmacodynamics of a drug based on evidence obtained through an extensive review of a variety of sources, including the u.s. fda biomarker list  <cit>  and clinical pharmacogenetics implementation consortium  nominations  <cit> .
out of the  <dig> drugs, it was possible to obtain pharmacodynamic pathways from pharmgkb for three: valproic acid, methotrexate, and etoposide.
all three drug-signaling pathways derived from drug voyager for these drugs were significantly enriched in pharmacodynamic pathways in terms of their member genes .
furthermore, by querying pubmed, we counted the co-occurrences of a drug and member genes for each drug-signaling pathway in the literature.
the public application program interface  of the national center for biotechnology information , e-utilities, was used to send queries, with each query made up of a drug generic name and an official gene symbol.
for comparison to a random control , the seed genes, which were used to construct the pathway, were removed from the pathway members, and a random control was generated with an identical number of the remaining member genes.
we found that pathway members for each drug significantly co-occurred when they were compared to random controls .
we compared the significance of drug voyager with the significance levels of three other models: 1) the basic model, 2) the silberberg model  <cit> , and 3) the gottlieb model  <cit> .
our platform, drug voyager, employs the three conceptual levels of initiation, perturbation, and destination, and it assigns drug-specific genes to these levels.
in contrast, the basic model is a simple model that utilizes the same genes used by drug voyager, but at two levels.
this model consists of a start level, which contains tgs, and an end level, which involves the other four types of genes .
with this model, the pathway for each drug was constructed using the same construction method used with drug voyager.
silberberg et al.
<cit>  reconstructed drug-specific subnetworks for each drug by connecting tgs to degs.
more recently, the gottlieb model  <cit>  applied three types of genes  and linked them to each other to build drug-specific pathways for each drug.
for comparison, we used their models with up-to-date interaction networks equivalent to drug voyager.
figures 2a, b display box plots of the enrichment tests for each model applied to the same set of drugs and show that the pathway members of our model were more significantly enriched than those of the other methods in the curated drug pathways.fig.
<dig> enrichment comparison with other models: a pharmgkb enrichment tests and b smpdb enrichment tests for the drug pathways of each model
drug-signaling pathway as a better indicator of drug repositioning
drug repositioning has been of great interest to the pharmaceutical industry, with increased numbers of systematic analyses to identify additional drug indications  <cit> .
similar properties of drugs have been used to generate new hypotheses on drug indications and have been considered the crucial basis for computational drug-repositioning approaches  <cit> .
we harness drug-signaling pathways to calculate drug similarity levels and compare the results with prime similarity measurements to discriminate known chemical-disease associations  <cit>  from unknown chemical-disease pairs.
the similarity levels of drug-signaling pathways were computed in three different ways: gene-sim, go-sim and kegg-sim .
other similarity measures traditionally successful in predicting drug indications were compiled, i.e., chemical similarity, drug target similarity and side-effect similarity measures .
based on each similarity measure, feature values were assigned to each drug indication and classifiers learned using several classification algorithms, including naive bayes, logistic regression and decision tree .
we found that classifiers using the drug-signaling pathway similarity computed by gene-sim and go-sim show better performance than those using other similarity measures when predicting known drug associations .fig.
<dig> drug similarity-based classification results: auc values for drug-similarity-based classifications based on the drug-signaling pathway similarity and traditional drug-similarity measures.
drug-signaling pathway similarity includes the gene component similarity , gene ontology enrichment similarity  and kegg enrichment similarity .
traditional drug similarity involves drug target similarity , chemical similarity  and side-effect similarity
in addition, drug clusters generated based on the similarity between drug-signaling pathways suitably reflect the anatomical, therapeutic and chemical  classification system  <cit> , in which drugs are categorized according to their therapeutic properties.
we were able to find that some unexpected drugs are included in the clusters and that these drugs can be regarded as candidates for drug repositioning, common side-effects and even drug-drug interactions .fig.
<dig> heat map of similarity between drugs.
the x-axis and y-axis indicate the same  <dig> drugs.
each cell depicts the similarity between two drugs based on a similarity score of their drug-specific pathways  and corresponding color key.
green dotted squares display the anatomical, therapeutic and chemical  top-level classes
predicting new drug indications
transformer utilizes drug-signaling pathways derived from drug voyager to predict novel drug indications under the assumption that drugs which have functionally similar pathways could have similar therapeutic effects .
the known indications of a certain drug were assigned to other drugs which show significant similarity to that drug.
three approaches were applied to measure the functional similarity between drug-signaling pathways: gene-sim, go-sim and kegg-sim .
to select the optimal cutoff for each similarity, we compare drug-disease pairs derived from each cutoff with those of the latest and most reliable repositories: 1) clinical trials  <cit> , and 2) the comparative toxicogenomics database  chemical-disease associations  <cit> .
in fig.
<dig>  distinct cutoff values for which the output drug-disease pairs were strongly supported in both registries were identified for each drug-similarity measurement.
by considering the most significant cutoff values, we obtained three optimal thresholds for three similarity measures, and they yielded three sets of drug-disease pairs.
in a conservative manner, we only chose the intersections between the three sets.
consequently, we acquired  <dig>  drug indications .
they were also significantly enriched in clinical trials .fig.
<dig> selecting optimal thresholds for predictions.
selection of optimal thresholds for predicting drug indications  or adverse reactions  by considering the lowest enrichment p-value
furthermore, we evaluated our predictions using pubchem bioassays for cancers and compared these results to the prediction results obtained from oh et al.
<cit>  and gottlieb et al.
<cit> .
we queried the predicted drugs for each type of cancer and then acquired active or non-active records for each drug-cancer pair.
in total,  <dig> drug-cancer pairs were aggregated.
using these pairs, we computed the f <dig> scores of the predictions identified by each method .
our prediction shows better performance  than the other methods in terms of its ability to predict active compounds for cancers.table  <dig> evaluation of methods on pubchem bioassays for cancers.
performance of prediction results on cancer bioassays were displayed
predicting side effects of drugs
in addition to predicting drug indications, the same strategy was applied to infer drug-related side effects.
using the pathway-based similarities of drugs, the known side effects of a particular drug were assigned to other similar drugs.
if the assigned side effects were in the known set, they were discarded.
we applied three drug-drug-similarity measures  and obtained a set of drug and side-effect pairs for each threshold and for each similarity measure.
the three sets were selected using the optimal threshold for each similarity measure .
finally, the drug and side-effect associations that were included in all three sets were proposed as new side effects for existing drugs.
in total,  <dig>  predictions spanning  <dig> drugs and  <dig>  side effects were determined in this manner .
for validation purposes, drug-event associations were gathered from the offsides database  <cit> , in which associations were statistically induced from the fda adverse event reporting system.
the offsides database contains  <dig>  off-label side effects for  <dig>  drugs which are different from those in the sider database  <cit>  .
our predictions of drug side effects were significantly enriched in offsides .
additionally, an adverse reaction database, canada’s medeffect, was used for further evaluation.
in order to reduce confounding effects, we use only high-confidence reports that include exactly one drug which is suspected as the primary reason for an adverse reaction.
out of  <dig>  drug-reaction pairs,  <dig>  pairs involve drugs and side effects that belong to our data set .
our predictions, which consist of  <dig> drugs and  <dig> side effects, significantly overlap with medeffect .
discussion
in this study, we propose a computational platform to extract drug-signaling pathways for different drugs and its application to predictions of drug indications and side effects.
by integrating genetic and phenotypic resources reflecting drug responses, we can formalize drug actions into drug-signaling pathways.
similarity between the drug-signaling pathways successfully led to meaningful candidates for drug indications and side effects.
to validate the computed drug-signaling pathways, we utilize the most comprehensive and reliable drug action pathway databases reflecting current knowledge of drug action.
despite considerable effort to accumulate drug action pathways, the curated pathways are limited to cover all of the computed pathways.
out of the  <dig> computed pathways, 30% of them are validated by smpdb and only three drugs are validated by pharmgkb.
the low coverages of known pathway databases might limit the applicability of this study.
however, the further validation using text mining on literature was conducted for all  <dig> pathways and the statistical significance of  <dig> pathways was observed.
given the incompleteness of the curated pathways, a literature mining may reinforce the validation by covering the rest.
also, we note that there can be a limitation when our method is applied to new compounds or drugs whose seed genes are not known because genes related to drug responses and phenotypes are required to construct the drug-signaling pathways.
but the rapidly expanding databases and systematic prediction pipelines to secure drug-relevant genes may complement the restriction .
although our approach successfully obtains meaningful candidates for new drug uses and possible side effects, it is still limited with regard to its ability to offer insight into new mechanisms of drug actions.
here, we undertake a pathway analysis of the inferring mechanisms of unexpected drug actions based on drug-signaling pathways.
to explore specific drug actions which are assumed to be responsible for new indications, we focus on the commonalities between drug-signaling pathways for both drugs, one a known treatment for a certain disease and the other a predicted one.
among the predicted drug indications, we highlight the following three indications as case studies: 1) haloperidol for alzheimer’s disease, 2) propranolol for asthma and 3) thalidomide for prostate cancer.
also, we suggest a repositioning candidate for breast and prostate cancer, 4) fluphenazie, which is worth conducting further research on its feasibility of therapeutic application.
haloperidol is an antipsychotic agent which is used as a treatment for schizophrenia.
the prediction of its use for alzheimer’s disease originated from the similarity between haloperidol and valproic acid, which is a promising agent to combat alzheimer’s disease  <cit>  .
the drug-signaling pathway of haloperidol has a large degree of overlap with that of valproic acid.
additional file 4: figure s <dig> shows the shared portion.
genes in the shared part are annotated with the “jak-stat signaling pathway,” the “erbb signaling pathway,” and the “vascular smooth muscle contraction pathway” in the kegg pathway database using the david tool .
these pathways have been studied and reported to be significantly linked to alzheimer’s disease, as explained below.
dysregulation of the jak-stat signaling pathway is mainly associated with brain inflammation processes and neuronal/glial survival in the mature central nervous system .
it is also involved in most brain disorders, including alzheimer’s disease  <cit> .
seven genes, jak <dig>  jak <dig>  grb <dig>  il <dig>  pik3r <dig>  sos <dig>  and myc, as shown in additional file 4: figure s <dig> are involved in the jak-stat signaling pathway.
aberration of erbb signaling pathway have been deciphered as key regulatory entity in human diseases.
especially, deficiency of erbb signaling is related to the development of neurodegenerative disorders, including alzheimer’s disease and multiple sclerosis.
<cit> .
six genes, gab <dig>  grb <dig>  pik3r <dig>  sos <dig>  myc, and src, as shown in additional file 4: figure s <dig> are involved in the erbb signaling pathway.
it has been shown that the endophenotype-associated pathways of alzheimer’s disease include vascular smooth muscle contraction, which was previously implicated in the biology of alzheimer’s disease  <cit> .
four genes, cyp4a <dig>  gna <dig>  myh <dig>  and npr <dig> in additional file 4: figure s <dig>  are involved in vascular smooth muscle contraction.
propranolol, a beta-adrenergic antagonist used predominantly for hypertension, was predicted to be efficient in the treatment of asthma.
in our method, a basis for this prediction was two asthma medications, flunisolide and theophylline, which have similar drug-signaling pathways to that of propranolol.
we found that drug-signaling pathways for these three drugs have large portions in common.
those shared pathway elements are shown in additional file 5: figure s <dig>  and the genes are annotated with “mapk signaling pathway”, “neurotrophin signaling pathway”, and “vegf signaling pathway” in the kegg pathway database using david tools .
those pathways have been demonstrated to have a significant relationship with asthma in the following studies.
asthmatic patients demonstrated increased immunostaining for phospho -erk1/ <dig>  p-p38α/β/γ , and pjnk1/2/ <dig>   <cit> , which are important members of the mapk signaling pathway, and are also shown in additional file 5: figure s <dig>  neurotrophins and their receptors are expressed in lung components, and the neurotrophin signaling pathway may be important in normal lung development, developmental lung disease, and allergy and inflammation   <cit> .
eight genes including mapk <dig>  mapk <dig>  mapk <dig>  mapk <dig>  map2k <dig>  map3k <dig>  rps6ka <dig>  and raf <dig> in additional file 5: figure s <dig> are involved in the neurotrophin signaling pathway.
elevated vegf levels have been observed in patients with asthma  <cit> .
furthermore, it has also been suggested  <cit>  that vegf excess can contribute to the pathogenesis of th <dig> inflammatory disorders such as asthma.
six genes including mapk <dig>  mapk <dig>  mapk <dig>  pla2g1b, raf <dig>  and src in additional file 4: figure s <dig> are involved in the vegf signaling pathway.
we predict that thalidomide can be used for prostate cancer.
thalidomide, an immunosuppressive agent, was once withdrawn from the market because of its teratogenic effects and has been reintroduced and used for immunological diseases.
thalidomide’s drug-signaling pathway substantially overlaps with a drug-signaling pathway for estradiol which has a therapeutic effect on prostate cancer  <cit> .
additional file 6: figure s <dig> shows the overlapping pathway.
the genes in additional file 6: figure s <dig> are annotated with “tca cycle”, “pyruvate metabolism”, and “wnt signaling pathway” in the kegg pathway database, using david tools .
those pathways have been reported and observed to have a significant relationship with prostate cancer, as detailed below.
it has been reported  <cit>  that there are significant changes in citrate-related metabolism and transport in prostate cancer.
the tricarboxylic acid  cycle is also linked to the excess production of reactive oxygen species .
as excess ros causes damage to dna, rna and proteins, it leads to oxidative stress, including metabolic alteration and mitochondrial dysfunction which accelerate tumorigenesis in prostate cancer.
five genes including dlat, dld, pc, pdha <dig>  and pdhb were involved in the tca cycle.
those five genes were also found in pyruvate metabolism, and pyruvate is used for metabolic imaging of prostate cancer  <cit> .
disruption or dysregulation of the wnt signaling pathway can lead to the development of many tumors including prostate cancer  <cit> .
the wnt/β-catenin pathway may regulate prostate tumor cells’ invasive behavior, mediating cell proliferation and epithelial-mesenchymal trans-differentiation  <cit> .
ctbp <dig>  jun, and myc in additional file 6: figure s <dig> are downstream genes in the wnt signaling pathway.
fluphenazine is one of phenothiazine antipyscotics, which is categorized as dopamine receptor antagonists and calmodulin inhibitors.
transformer predicted its new indication for breast and prostate cancer.
it was found that a drug-signaling pathway of this agent shares substantial members with that of estradiol, which has been reported to be used for treatment of breast and prostate cancer.
to identify functional commonality, go annotation analysis was performed on the shared part of their pathways.
table  <dig> shows the top  <dig> go enrichment results.
among them, we focus on cyclin-dependent kinase  activity and g-protein coupled receptor  signaling pathway.
it is clear that cdk family is principal to several signaling pathways regulating transcription and cell-cycle progression.
in year  <dig>  fda approved cdk <dig> and cdk <dig> inhibitors for breast cancer as breakthrough therapies  <cit> .
we found that the shared part of fluphenazine and estradiol drug-signaling pathways involves several cdk4/ <dig> inhibitor genes, including cdkn2a, cdkn1b and cdkn1a.
the other interesting cdk protein in the shared part is cdk <dig>  which has multiple roles in some tissues with relevance to cancers  <cit> .
gpcr controls key physiological functions  <cit> .
it is reviewed that gpcrs can be crucial players in tumor growth and metastasis  <cit> .
especially, chrm <dig> gene which is annotated to go term of gpcr signaling pathway was identified in the shared part.
in the recent study, it has been found that autocrine activation of chrm <dig> promotes prostate cancer growth  <cit> .
these findings could support the potential indication of fluphenazine for breast cancer and prostate cancer.table  <dig> the top ten gene ontology terms enriched.
go annotation analysis was performed on the shared members derived from the drug-signaling pathways of fluphenazine and estradiol
additionally, we discuss a few predicted adverse drug reactions based on published studies and the us fda online label repository.
although the fda drug label is biased toward the number of occurrences of adverse reactions that are observed and reported in clinical trials or post-marketing surveillance, it is one of the clearest options for comparison to the predicted results.
we highlight the predicted adverse reactions of the three drugs paclitaxel for bradycardia and tachycardia, valproic acid for delirium and neutropenia, and tamoxifen for hypothyroidism.
paclitaxel, a tubulin modulator used in chemotherapy, was predicted to induce bradycardia and tachycardia.
in one phase  <dig> study, 29% of the  <dig> patients who were treated with paclitaxel developed bradycardia  <cit> .
in a subsequent large-cohort study, a rate of  <dig> % of cardiac toxicity was reported, and most of these cases were asymptomatic bradycardia.
in rare cases, atrial and ventricular tachycardia were described  <cit> .
another study assessing cardiac disturbances in one hundred african-american patients treated with paclitaxel found that 26% of patients experience sinus tachycardia  <cit> .
valproic acid, which is administered predominantly in epilepsy and psychiatric disorders, was suspected to cause delirium in our prediction results.
delirium had been reported in three studies in which valproic acid levels in plasma were within therapeutic ranges .
although delirium is excluded from the adverse reactions described in the fda drug labeling for valproic acid, the most common form of delirium, hyperactivity, is included in the labeling.
in addition to delirium, neutropenia appeared in our predictions for valproic acid.
likewise, neutropenia following valproic acid exposure was also observed in other studies .
the fda drug label does not specify neutropenia as an adverse reaction for valproic acid, but leukopenia is labeled.
as the most common subtype of leukopenia is neutropenia, this label corroborates that our prediction is supported in the current pharmacovigilance system.
tamoxifen, a selective estrogen receptor modulator  used predominantly for the treatment of breast cancer, was inferred to be a possible agent responsible for hypothyroidism by our method.
the fda drug label does not include warnings for drug-induced hypothyroidism.
however, particularly in postmenopausal breast cancer patients, significant alterations in thyroid function tests were observed during treatment with tamoxifen  <cit> .
specifically, the previous study reports that tamoxifen treatment significantly elevates plasma levels of thyroid-stimulating hormone  and significantly suppresses free triiodothyronine  and free thyroxine .
as the diagnosis of hypothyroidism is confirmed by an elevated tsh level and a low ft <dig> level, we can suspect that tamoxifen may have an effect in inducing hypothyroidism.
in a more recent study, the authors raise caution related to treating thyroid dysfunction in women who are taking serms such as tamoxifen  <cit> .
we further note that it was difficult to find literature evidence of predictions of severe adverse reactions such as cardiac arrest and coma, as we found that the more the relative severity of the adverse reactions increases, the fewer reports there are available .
CONCLUSIONS
verifiable hypotheses about unintended drug actions are implicitly inherent in emerging data relevant to drug responses.
our study shows how we can utilize these data to computationally represent drug-signaling pathways.
furthermore, computational analysis of drug-signaling pathways can provide more precise predictions of drug-repositioning candidates and adverse reactions as well as the mechanisms of unintended actions.

 the actions of drugs have been systematically observed and recorded by governments, non-trading organizations, and academic institutions. from phenotypic screening to post-marketing surveillance, abundant reports have been archived and follow-up studies on the mechanisms of action of drugs have been conducted. although this research delivers us advances in knowledge, our understanding of drug actions is generally biased toward intended therapeutic effects and frequent adverse reactions. this partiality has caused delays in deciphering the mechanisms of unintended drug actions. historically, it was inevitable that the discovery of unexpected drug actions, regardless of whether they are desirable or not, usually depends on empirical detection. however, an unbiased analysis of drug actions should be a basis for understanding unintended drug responses and predicting drug-repositioning opportunities or undesirable reactions. the rapidly expanding databases and newly available data in the literature, including pharmacogenomic biomarkers, drug-induced gene expression profiles, and drug side-effect information, continually provide clues which indicate unknown drug actions. recently, computational approaches for systematic analyses of these data have been highlighted, enhancing both the availability and usability of the data. in comparison to in vitro and in vivo experiments, computational approaches are remarkable in terms of time and cost efficiency. moreover, systematic implementations are reproducible. these implementations can be utilized for upcoming drugs as well as failed drugs, but a lack of appropriate methods creates an arduous task for those who attempt to integrate and utilize these scattered pieces of evidence. for a comprehensive understanding of drug action, it is necessary to organize and analyze drug-signaling pathways in a systematic manner. there have been many attempts to predict drug actions based on similar properties of drugs, including their targets, chemical structures and side effects. although these properties are fairly useful for distinguishing repurposed drugs, these attempts tend to depend on the extrinsic properties of drugs and not on the intrinsic mechanisms of drug actions. therefore, the findings are limited. one of the most tangible mechanisms of action is a network in which the nodes refer to biomolecules and the edges refer to the physical interaction between two nodes. it should be noted that drugs exert their effects through multiple signaling cascades in a molecular network rather than through a single gene or a single route. therefore, we need to devise a network platform which realistically infers the drug-signaling pathways. previously, few methods attempted to design drug-signaling pathways at the molecular level in order to identify a novel pathway for a particular drug. however, these methods tended to utilize limited resources to generate the pathway or do not consider the directionality of biological networks. moreover, systematic approaches to represent the perturbation of molecular and cellular responses are lacking, as the field is in its infancy. here, we devise a novel platform, called drug voyager, on which to construct drug-signaling pathways for different drugs. with this platform, the molecular-level action of a drug is represented by connecting the three conceptual levels of “initiation,” “perturbation,” and “destination.” each level includes a combination of the five types of seed genes related to drug responses and phenotypes: drug target genes, pharmacogenomic variant genes, differentially expressed genes, disease genes, and side-effect genes. as a consequence of construction of level-to-level pathways, drug-signaling pathways were generated in total for drugs. in the validation step, these pathways were significantly enriched in known drug pathway databases and show higher significance levels compared to when other models are used.fig. method overview: a drug voyager representing the molecular-level actions of drugs by connecting three conceptual levels; b constructing a pathway from the starting point to the end point; and c transformer, utilizing drug-specific pathways to predict drug indications and adverse reactions furthermore, we present transformer, a new method for translating the functional features of drug-signaling pathways into new medicine and adverse reactions. based on the drug-signaling pathways generated by drug voyager and a snapshot of the cross-talk within each pathway, transformer predicts drug indications and adverse reactions. our predictions for therapeutic use significantly overlaps with drug indications currently tested in clinical trials and an up-to-date drug-disease association database. when used to predict pubchem bioassay results, transformer surpasses existing methods in terms of its ability to classify active compounds for cancers. for adverse reactions, our predictions show high significance levels in enrichment testing for an independent database derived from adverse event reporting system of the food and drug administration and meaningfully cover the adverse reaction database provided from health canada. lastly, we highlight several predictions for both therapeutic indications and side effects through pathway analyses and from published reports.  validation of drug-signaling pathways as noted above, we devised a novel platform, termed drug voyager, which constructs drug-signaling pathways for individual drugs. in this platform, the three conceptual levels of initiation, perturbation, and destination were adopted to imitate drug perturbations. we assume that a signal transduction cascade in drug action begins at the initiation level and reaches the destination level through the perturbation level. given the genes that contribute to drug responses and phenotypes, they were assigned to corresponding levels and the mechanism of action of a drug was delineated by connecting these three conceptual levels. among all the fda approved drugs, some drugs whose corresponding genes are unknown have been filtered out, and finally drugs remain. by constructing level-to-level pathways, drug voyager built drug-signaling pathways, one for each of drugs. the validity of each of these pathways was evaluated by the following three observations: 1) enrichment in the small molecule pathway database, 2) enrichment in pharmgkb, and 3) the co-occurrence of the drug and genes in the literature. we then compared drug voyager with other pathway construction models. we undertook gene enrichment computations in the curated drug-action pathways extracted from smpdb. the drug pathways from smpdb were generated based on various medical and pharmacology textbooks, as well as relevant published reviews and online databases such as kegg and the medical biochemistry page. among the drugs, information about the drug-action pathways was accessible for and we concentrated on of these which had pathways composed of ten or more elements. twenty-one of the drug-signaling pathways were significantly enriched in the curated drug-action pathways. in addition, the validity of the drug-signaling pathways was tested by comparing these pathways to the pharmacodynamic pathways derived from pharmgkb. pharmacodynamic pathways depict the pharmacodynamics of a drug based on evidence obtained through an extensive review of a variety of sources, including the u.s. fda biomarker list and clinical pharmacogenetics implementation consortium nominations. out of the drugs, it was possible to obtain pharmacodynamic pathways from pharmgkb for three: valproic acid, methotrexate, and etoposide. all three drug-signaling pathways derived from drug voyager for these drugs were significantly enriched in pharmacodynamic pathways in terms of their member genes. furthermore, by querying pubmed, we counted the co-occurrences of a drug and member genes for each drug-signaling pathway in the literature. the public application program interface of the national center for biotechnology information, e-utilities, was used to send queries, with each query made up of a drug generic name and an official gene symbol. for comparison to a random control, the seed genes, which were used to construct the pathway, were removed from the pathway members, and a random control was generated with an identical number of the remaining member genes. we found that pathway members for each drug significantly co-occurred when they were compared to random controls. we compared the significance of drug voyager with the significance levels of three other models: 1) the basic model, 2) the silberberg model, and 3) the gottlieb model. our platform, drug voyager, employs the three conceptual levels of initiation, perturbation, and destination, and it assigns drug-specific genes to these levels. in contrast, the basic model is a simple model that utilizes the same genes used by drug voyager, but at two levels. this model consists of a start level, which contains tgs, and an end level, which involves the other four types of genes. with this model, the pathway for each drug was constructed using the same construction method used with drug voyager. silberberg et al. reconstructed drug-specific subnetworks for each drug by connecting tgs to degs. more recently, the gottlieb model applied three types of genes and linked them to each other to build drug-specific pathways for each drug. for comparison, we used their models with up-to-date interaction networks equivalent to drug voyager. figures 2a, b display box plots of the enrichment tests for each model applied to the same set of drugs and show that the pathway members of our model were more significantly enriched than those of the other methods in the curated drug pathways.fig. enrichment comparison with other models: a pharmgkb enrichment tests and b smpdb enrichment tests for the drug pathways of each model drug-signaling pathway as a better indicator of drug repositioning drug repositioning has been of great interest to the pharmaceutical industry, with increased numbers of systematic analyses to identify additional drug indications. similar properties of drugs have been used to generate new hypotheses on drug indications and have been considered the crucial basis for computational drug-repositioning approaches. we harness drug-signaling pathways to calculate drug similarity levels and compare the results with prime similarity measurements to discriminate known chemical-disease associations from unknown chemical-disease pairs. the similarity levels of drug-signaling pathways were computed in three different ways: gene-sim, go-sim and kegg-sim. other similarity measures traditionally successful in predicting drug indications were compiled, i.e., chemical similarity, drug target similarity and side-effect similarity measures. based on each similarity measure, feature values were assigned to each drug indication and classifiers learned using several classification algorithms, including naive bayes, logistic regression and decision tree. we found that classifiers using the drug-signaling pathway similarity computed by gene-sim and go-sim show better performance than those using other similarity measures when predicting known drug associations.fig. drug similarity-based classification results: auc values for drug-similarity-based classifications based on the drug-signaling pathway similarity and traditional drug-similarity measures. drug-signaling pathway similarity includes the gene component similarity, gene ontology enrichment similarity and kegg enrichment similarity. traditional drug similarity involves drug target similarity, chemical similarity and side-effect similarity in addition, drug clusters generated based on the similarity between drug-signaling pathways suitably reflect the anatomical, therapeutic and chemical classification system, in which drugs are categorized according to their therapeutic properties. we were able to find that some unexpected drugs are included in the clusters and that these drugs can be regarded as candidates for drug repositioning, common side-effects and even drug-drug interactions.fig. heat map of similarity between drugs. the x-axis and y-axis indicate the same drugs. each cell depicts the similarity between two drugs based on a similarity score of their drug-specific pathways and corresponding color key. green dotted squares display the anatomical, therapeutic and chemical top-level classes predicting new drug indications transformer utilizes drug-signaling pathways derived from drug voyager to predict novel drug indications under the assumption that drugs which have functionally similar pathways could have similar therapeutic effects. the known indications of a certain drug were assigned to other drugs which show significant similarity to that drug. three approaches were applied to measure the functional similarity between drug-signaling pathways: gene-sim, go-sim and kegg-sim. to select the optimal cutoff for each similarity, we compare drug-disease pairs derived from each cutoff with those of the latest and most reliable repositories: 1) clinical trials, and 2) the comparative toxicogenomics database chemical-disease associations. in fig.  distinct cutoff values for which the output drug-disease pairs were strongly supported in both registries were identified for each drug-similarity measurement. by considering the most significant cutoff values, we obtained three optimal thresholds for three similarity measures, and they yielded three sets of drug-disease pairs. in a conservative manner, we only chose the intersections between the three sets. consequently, we acquired drug indications. they were also significantly enriched in clinical trials.fig. selecting optimal thresholds for predictions. selection of optimal thresholds for predicting drug indications or adverse reactions by considering the lowest enrichment p-value furthermore, we evaluated our predictions using pubchem bioassays for cancers and compared these results to the prediction results obtained from oh et al. and gottlieb et al.. we queried the predicted drugs for each type of cancer and then acquired active or non-active records for each drug-cancer pair. in total, drug-cancer pairs were aggregated. using these pairs, we computed the f scores of the predictions identified by each method. our prediction shows better performance than the other methods in terms of its ability to predict active compounds for cancers.table evaluation of methods on pubchem bioassays for cancers. performance of prediction results on cancer bioassays were displayed predicting side effects of drugs in addition to predicting drug indications, the same strategy was applied to infer drug-related side effects. using the pathway-based similarities of drugs, the known side effects of a particular drug were assigned to other similar drugs. if the assigned side effects were in the known set, they were discarded. we applied three drug-drug-similarity measures and obtained a set of drug and side-effect pairs for each threshold and for each similarity measure. the three sets were selected using the optimal threshold for each similarity measure. finally, the drug and side-effect associations that were included in all three sets were proposed as new side effects for existing drugs. in total, predictions spanning drugs and side effects were determined in this manner. for validation purposes, drug-event associations were gathered from the offsides database, in which associations were statistically induced from the fda adverse event reporting system. the offsides database contains off-label side effects for drugs which are different from those in the sider database. our predictions of drug side effects were significantly enriched in offsides. additionally, an adverse reaction database, canada’s medeffect, was used for further evaluation. in order to reduce confounding effects, we use only high-confidence reports that include exactly one drug which is suspected as the primary reason for an adverse reaction. out of drug-reaction pairs, pairs involve drugs and side effects that belong to our data set. our predictions, which consist of drugs and side effects, significantly overlap with medeffect. discussion in this study, we propose a computational platform to extract drug-signaling pathways for different drugs and its application to predictions of drug indications and side effects. by integrating genetic and phenotypic resources reflecting drug responses, we can formalize drug actions into drug-signaling pathways. similarity between the drug-signaling pathways successfully led to meaningful candidates for drug indications and side effects. to validate the computed drug-signaling pathways, we utilize the most comprehensive and reliable drug action pathway databases reflecting current knowledge of drug action. despite considerable effort to accumulate drug action pathways, the curated pathways are limited to cover all of the computed pathways. out of the computed pathways, 30% of them are validated by smpdb and only three drugs are validated by pharmgkb. the low coverages of known pathway databases might limit the applicability of this study. however, the further validation using text mining on literature was conducted for all pathways and the statistical significance of pathways was observed. given the incompleteness of the curated pathways, a literature mining may reinforce the validation by covering the rest. also, we note that there can be a limitation when our method is applied to new compounds or drugs whose seed genes are not known because genes related to drug responses and phenotypes are required to construct the drug-signaling pathways. but the rapidly expanding databases and systematic prediction pipelines to secure drug-relevant genes may complement the restriction. although our approach successfully obtains meaningful candidates for new drug uses and possible side effects, it is still limited with regard to its ability to offer insight into new mechanisms of drug actions. here, we undertake a pathway analysis of the inferring mechanisms of unexpected drug actions based on drug-signaling pathways. to explore specific drug actions which are assumed to be responsible for new indications, we focus on the commonalities between drug-signaling pathways for both drugs, one a known treatment for a certain disease and the other a predicted one. among the predicted drug indications, we highlight the following three indications as case studies: 1) haloperidol for alzheimer’s disease, 2) propranolol for asthma and 3) thalidomide for prostate cancer. also, we suggest a repositioning candidate for breast and prostate cancer, 4) fluphenazie, which is worth conducting further research on its feasibility of therapeutic application. haloperidol is an antipsychotic agent which is used as a treatment for schizophrenia. the prediction of its use for alzheimer’s disease originated from the similarity between haloperidol and valproic acid, which is a promising agent to combat alzheimer’s disease. the drug-signaling pathway of haloperidol has a large degree of overlap with that of valproic acid. additional file 4: figure s shows the shared portion. genes in the shared part are annotated with the “jak-stat signaling pathway,” the “erbb signaling pathway,” and the “vascular smooth muscle contraction pathway” in the kegg pathway database using the david tool. these pathways have been studied and reported to be significantly linked to alzheimer’s disease, as explained below. dysregulation of the jak-stat signaling pathway is mainly associated with brain inflammation processes and neuronal/glial survival in the mature central nervous system. it is also involved in most brain disorders, including alzheimer’s disease. seven genes, jak jak grb il pik3r sos and myc, as shown in additional file 4: figure s are involved in the jak-stat signaling pathway. aberration of erbb signaling pathway have been deciphered as key regulatory entity in human diseases. especially, deficiency of erbb signaling is related to the development of neurodegenerative disorders, including alzheimer’s disease and multiple sclerosis.. six genes, gab grb pik3r sos myc, and src, as shown in additional file 4: figure s are involved in the erbb signaling pathway. it has been shown that the endophenotype-associated pathways of alzheimer’s disease include vascular smooth muscle contraction, which was previously implicated in the biology of alzheimer’s disease. four genes, cyp4a gna myh and npr in additional file 4: figure s are involved in vascular smooth muscle contraction. propranolol, a beta-adrenergic antagonist used predominantly for hypertension, was predicted to be efficient in the treatment of asthma. in our method, a basis for this prediction was two asthma medications, flunisolide and theophylline, which have similar drug-signaling pathways to that of propranolol. we found that drug-signaling pathways for these three drugs have large portions in common. those shared pathway elements are shown in additional file 5: figure s and the genes are annotated with “mapk signaling pathway”, “neurotrophin signaling pathway”, and “vegf signaling pathway” in the kegg pathway database using david tools. those pathways have been demonstrated to have a significant relationship with asthma in the following studies. asthmatic patients demonstrated increased immunostaining for phospho -erk1/ p-p38α/β/γ, and pjnk1/2/, which are important members of the mapk signaling pathway, and are also shown in additional file 5: figure s neurotrophins and their receptors are expressed in lung components, and the neurotrophin signaling pathway may be important in normal lung development, developmental lung disease, and allergy and inflammation. eight genes including mapk mapk mapk mapk map2k map3k rps6ka and raf in additional file 5: figure s are involved in the neurotrophin signaling pathway. elevated vegf levels have been observed in patients with asthma. furthermore, it has also been suggested that vegf excess can contribute to the pathogenesis of th inflammatory disorders such as asthma. six genes including mapk mapk mapk pla2g1b, raf and src in additional file 4: figure s are involved in the vegf signaling pathway. we predict that thalidomide can be used for prostate cancer. thalidomide, an immunosuppressive agent, was once withdrawn from the market because of its teratogenic effects and has been reintroduced and used for immunological diseases. thalidomide’s drug-signaling pathway substantially overlaps with a drug-signaling pathway for estradiol which has a therapeutic effect on prostate cancer. additional file 6: figure s shows the overlapping pathway. the genes in additional file 6: figure s are annotated with “tca cycle”, “pyruvate metabolism”, and “wnt signaling pathway” in the kegg pathway database, using david tools. those pathways have been reported and observed to have a significant relationship with prostate cancer, as detailed below. it has been reported that there are significant changes in citrate-related metabolism and transport in prostate cancer. the tricarboxylic acid cycle is also linked to the excess production of reactive oxygen species. as excess ros causes damage to dna, rna and proteins, it leads to oxidative stress, including metabolic alteration and mitochondrial dysfunction which accelerate tumorigenesis in prostate cancer. five genes including dlat, dld, pc, pdha and pdhb were involved in the tca cycle. those five genes were also found in pyruvate metabolism, and pyruvate is used for metabolic imaging of prostate cancer. disruption or dysregulation of the wnt signaling pathway can lead to the development of many tumors including prostate cancer. the wnt/β-catenin pathway may regulate prostate tumor cells’ invasive behavior, mediating cell proliferation and epithelial-mesenchymal trans-differentiation. ctbp jun, and myc in additional file 6: figure s are downstream genes in the wnt signaling pathway. fluphenazine is one of phenothiazine antipyscotics, which is categorized as dopamine receptor antagonists and calmodulin inhibitors. transformer predicted its new indication for breast and prostate cancer. it was found that a drug-signaling pathway of this agent shares substantial members with that of estradiol, which has been reported to be used for treatment of breast and prostate cancer. to identify functional commonality, go annotation analysis was performed on the shared part of their pathways. table  shows the top go enrichment results. among them, we focus on cyclin-dependent kinase activity and g-protein coupled receptor signaling pathway. it is clear that cdk family is principal to several signaling pathways regulating transcription and cell-cycle progression. in year fda approved cdk and cdk inhibitors for breast cancer as breakthrough therapies. we found that the shared part of fluphenazine and estradiol drug-signaling pathways involves several cdk4/ inhibitor genes, including cdkn2a, cdkn1b and cdkn1a. the other interesting cdk protein in the shared part is cdk which has multiple roles in some tissues with relevance to cancers. gpcr controls key physiological functions. it is reviewed that gpcrs can be crucial players in tumor growth and metastasis. especially, chrm gene which is annotated to go term of gpcr signaling pathway was identified in the shared part. in the recent study, it has been found that autocrine activation of chrm promotes prostate cancer growth. these findings could support the potential indication of fluphenazine for breast cancer and prostate cancer.table the top ten gene ontology terms enriched. go annotation analysis was performed on the shared members derived from the drug-signaling pathways of fluphenazine and estradiol additionally, we discuss a few predicted adverse drug reactions based on published studies and the us fda online label repository. although the fda drug label is biased toward the number of occurrences of adverse reactions that are observed and reported in clinical trials or post-marketing surveillance, it is one of the clearest options for comparison to the predicted results. we highlight the predicted adverse reactions of the three drugs paclitaxel for bradycardia and tachycardia, valproic acid for delirium and neutropenia, and tamoxifen for hypothyroidism. paclitaxel, a tubulin modulator used in chemotherapy, was predicted to induce bradycardia and tachycardia. in one phase study, 29% of the patients who were treated with paclitaxel developed bradycardia. in a subsequent large-cohort study, a rate of % of cardiac toxicity was reported, and most of these cases were asymptomatic bradycardia. in rare cases, atrial and ventricular tachycardia were described. another study assessing cardiac disturbances in one hundred african-american patients treated with paclitaxel found that 26% of patients experience sinus tachycardia. valproic acid, which is administered predominantly in epilepsy and psychiatric disorders, was suspected to cause delirium in our prediction results. delirium had been reported in three studies in which valproic acid levels in plasma were within therapeutic ranges. although delirium is excluded from the adverse reactions described in the fda drug labeling for valproic acid, the most common form of delirium, hyperactivity, is included in the labeling. in addition to delirium, neutropenia appeared in our predictions for valproic acid. likewise, neutropenia following valproic acid exposure was also observed in other studies. the fda drug label does not specify neutropenia as an adverse reaction for valproic acid, but leukopenia is labeled. as the most common subtype of leukopenia is neutropenia, this label corroborates that our prediction is supported in the current pharmacovigilance system. tamoxifen, a selective estrogen receptor modulator used predominantly for the treatment of breast cancer, was inferred to be a possible agent responsible for hypothyroidism by our method. the fda drug label does not include warnings for drug-induced hypothyroidism. however, particularly in postmenopausal breast cancer patients, significant alterations in thyroid function tests were observed during treatment with tamoxifen. specifically, the previous study reports that tamoxifen treatment significantly elevates plasma levels of thyroid-stimulating hormone and significantly suppresses free triiodothyronine and free thyroxine. as the diagnosis of hypothyroidism is confirmed by an elevated tsh level and a low ft level, we can suspect that tamoxifen may have an effect in inducing hypothyroidism. in a more recent study, the authors raise caution related to treating thyroid dysfunction in women who are taking serms such as tamoxifen. we further note that it was difficult to find literature evidence of predictions of severe adverse reactions such as cardiac arrest and coma, as we found that the more the relative severity of the adverse reactions increases, the fewer reports there are available.  verifiable hypotheses about unintended drug actions are implicitly inherent in emerging data relevant to drug responses. our study shows how we can utilize these data to computationally represent drug-signaling pathways. furthermore, computational analysis of drug-signaling pathways can provide more precise predictions of drug-repositioning candidates and adverse reactions as well as the mechanisms of unintended actions. 
also, our method outperforms existing methods with regard to classification of active compounds for cancers. furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. for adverse reactions, our predictions were significantly enriched in an independent database derived from the food and drug administration adverse event reporting system and meaningfully cover an adverse reaction database provided by health canada. the dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. drug pathwaydrug-signaling pathwaydrug actionpharmacodynamicsdrug repurposingdrug repositioningadverse reactionsside effectshttp://dx.doi.org/ /501100003725national research foundation of koreanrf-2015r1a2a2a03004088yoon youngmi issue-copyright-statement© the author 2017 for therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. in this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. however, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening. we suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response.
 the dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. however, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking.  we suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response. in this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. for therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. also, our method outperforms existing methods with regard to classification of active compounds for cancers. for adverse reactions, our predictions were significantly enriched in an independent database derived from the food and drug administration adverse event reporting system and meaningfully cover an adverse reaction database provided by health canada. lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature.  our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords drug pathwaydrug-signaling pathwaydrug actionpharmacodynamicsdrug repurposingdrug repositioningadverse reactionsside effectshttp://dx.doi.org/ /501100003725national research foundation of koreanrf-2015r1a2a2a03004088yoon youngmi issue-copyright-statement© the author 2017 