REVIEW
published: 01 November 2021
doi: 10.3389/fphar.2021.752174

Drug-Repositioning Approaches
Based on Medical and Life Science
Databases
Yoshito Zamami 1,2,3†, Hirofumi Hamano 1†, Takahiro Niimura 2, Fuka Aizawa 2, Kenta Yagi 4,
Mitsuhiro Goda 4, Yuki Izawa-Ishizawa 5 and Keisuke Ishizawa 1,2*
1
Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences,
Tokushima, Japan, 2Department of Pharmacy, Tokushima University Hospital, Tokushima, Japan, 3Department of Pharmacy,
Okayama University Hospital, Okayama, Japan, 4Clinical Trial Center for Developmental Therapeutics, Tokushima University
Hospital, Tokushima, Japan, 5Department of Pharmacology, Tokushima University Graduate School of Biomedical Sciences,
Tokushima, Japan

Edited by:
Sandor Kerpel-Fronius,Semmelweis University, Hungary
Reviewed by:
Hon-Cheong So,
The Chinese University of Hong Kong,
Hong Kong SAR, China
Varvara Barbara Baroutsou,
Consultant, Athens, Greece*Correspondence:
Keisuke Ishizawa
ishizawa@tokushima-u.ac.jp†
These authors have contributed
equally to this work and share ﬁrst
authorship

Specialty section:
This article was submitted toDrugs Outcomes Researchand Policies,
a section of the journal
Frontiers in Pharmacology
Received: 11 August 2021
Accepted: 18 October 2021
Published: 01 November 2021
Citation:
Zamami Y, Hamano H, Niimura T,
Aizawa F, Yagi K, Goda M,
Izawa-Ishizawa Y and Ishizawa K(2021) Drug-Repositioning
Approaches Based on Medical andLife Science Databases.
Front. Pharmacol. 12:752174.
doi: 10.3389/fphar.2021.752174

Drug repositioning is a drug discovery strategy in which an existing drug is utilized as atherapeutic agent for a different disease. As information regarding the safety,
pharmacokinetics, and formulation of existing drugs is already available, the cost andtime required for drug development is reduced. Conventional drug repositioning has been
dominated by a method involving the search for candidate drugs that act on the target
molecules of an organism in a diseased state through basic research. However, recently,
information hosted on medical information and life science databases have been used intranslational research to bridge the gap between basic research in drug repositioning andclinical application. Here, we review an example of drug repositioning wherein candidate
drugs were found and their mechanisms of action against a novel therapeutic target wereidentiﬁed via a basic research method that combines the ﬁndings retrieved from various
medical and life science databases.
Keywords: drug repositioning, database, medical information database, life science databases, existing drugs

INTRODUCTION
Drug repositioning represents a strategy to discover novel pharmacological effects by using theexisting approved drugs that have well-proven pharmacokinetics and safety in humans. The
advantage of this strategy is that a few necessary steps in drug development are eliminated,
thereby reducing the cost and time required for clinical trials as well as the risk of clinical trial
failure due to adverse events (Ashburn and Thor, 2004; Chong and Sullivan, 2007; Zamami et al.,2017). Examples of drug repositioning include thalidomide, once used as a sedative, is now used totreat multiple myeloma, and Viagra, which is currently used as an erectile dysfunctions drug (Shimand Liu, 2014). Lee et al. conducted a large retrospective cohort study of 800,000 subjects andreported that metformin, a drug used to treat diabetes, reduced the incidence of colorectal, liver, andpancreatic cancer (Lee et al., 2011). Furthermore, Chamoto et al. reported that the combination of theimmune checkpoint inhibitor nivolumab with bezaﬁbrate, a drug for dyslipidemia treatment,
increased the antitumor effect of nivolumab by activating the mitochondria of T cells andenhancing their ability to damage cancer cells (Chamoto et al., 2017). In recent years, drug
repositioning has attracted attention as a strategy for developing treatments for rare diseases,
addressing unmet medical needs, and managing other diseases for which early treatment is desired(Cao et al., 2020; Grein et al., 2020; Wang et al., 2020; Kalil et al., 2021; Salama et al., 2021).

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Beklen et al., 2021; Chong et al., 2021; Kort and Jovinge, 2021;
Mohammad Sadeghi et al., 2021; Reay and Cairns, 2021).
On the contrary, medical information databases can be used to
examine the efﬁcacy of drugs in actual clinical practice, theoccurrence of adverse events, and patient information, such as
age and sex. However, a few previous reviews focused on medical
databases, and the use of pharmaco-epidemiology approaches torepositioning have been reported. This article describes medical
information databases and life science databases that are used bymany researchers, including an overview of the latest ﬁndings,
ﬁlling the gap in literature.
FIGURE 1 | The number of publications from 2000 to 2020. The whiteand black bars show the results of searches performed in Scopus on the“Drug Repositioning” or “Drug Repositioning” and “databases”, respectively.
The number of drug-repositioning studies has increased with theincrease in the number of databases. Date of analysis: Jul 9, 2021.

MEDICAL INFORMATION DATABASESThe United States Food and Drug Administration (FDA) (U.S.
FOOD and DRUG administration, 2021) Adverse Event
Reporting System (FAERS) (FAERS, 2021), the JapaneseAdverseDrug
Event
Reportdatabase(JADER)(Pharmaceuticals and Medical Devices Agency, 2021), and theWorld Health Organization (WHO) database (vigibase) (Uppsala
Monitoring Center, 2021) are the main databases—hosting
reports associated with spontaneous adverse events—that areassessed in drug-repositioning research. In Japan, the National
database of Health Insurance Claims and Speciﬁc Health
Checkups of Japan (NDB) (Ministry of Health Labour andWelfare, 2021) and the Japan Medical Data Center (JMDC)
Claims database (JMDC, 2021) collect the data of insured
patients. Of these, vigibase and the JMDC require a certain fee
to provide access to the information, whereas databases like
FAERS, JADER, and NDB, offer free services.
The US FDA collects spontaneous adverse event reports frommedical professionals and companies worldwide. In January
2021, the number of such reports was approximately 12
million. In Japan, the Pharmaceuticals and Medical Devices
Agency (PMDA) collects reports regarding spontaneousadverse events and has accumulated approximately 700,000
reports as of January 2021. Furthermore, vigibase, which iscurated by the WHO, hosts a large number of reports (∼20
million until 2021); companies from more than 130 countries are
involved in vigibase. Vigibase catalogs the data on the time-toonset (TTO), which is used to investigate the features and theonset of adverse events and is often performed due to the lack ofmissing data. In recent years, epidemiological studies using
databases, such as FAERS, JADER, and vigibase that curate
information regarding spontaneous adverse events have been
actively conducted to search for drugs with minimal adverse
events. Data curation is a key feature that affects the analysis, but
until now, individual researchers have used their own curation
methods to create datasets. Therefore, unifying such datasets has
become a necessary procedure for researchers, especially those
who are unfamiliar with the ﬁeld, to create appropriate datasets,
and in recent years, attempts have been made in this direction(Banda et al., 2016).
The NDB collects more than 1.6 billion electronic receipts
every year; however, because the NDB is a database that is
managed by the government, certain procedures need to be

The use of big data has been attracting attention in various
ﬁelds. In the medical ﬁeld, databases of medical information, suchas prescriptions and databases of spontaneous reports of adverse
events caused by drugs and medical devices, are used for research.
These databases include data (e.g., prescription data, imaging,clinical data, etc) for patients with various clinical backgrounds[patients (and patient data) who are not always included inclinical trials] and thus enable evaluations that reﬂect the realworld setting. Therefore, studies to evaluate the efﬁcacy and safetyof drugs using medical big data have been actively conducted inmany countries worldwide. Drug repositioning is no exception tothis trend. The number of papers on drug repositioning isincreasing every year, and the number of studies on drug
repositioning based on the use of databases is also increasing(Figure 1). The advent of several promising and drug-relevant
databases has led to the exploration of a wide range of target
coverages and data types (Tanoli et al., 2021). As a gold standard,
the repoDB aggregates information on approved and failed drugs(repoDB, 2021). This database can not only provide information
on successful drug repositioning to date but also help to identify
both true positives (approved drugs) and true negatives (failed
drugs), facilitating the computational identiﬁcation of drugclasses considered to have high success rates in drug
repositioning (Brown and Patel, 2017).
Databases include life science databases, which host molecular
information such as genes and proteins, and medical information
databases, which curate clinical information on real-world
patients (Table 1). Given the increased interest in drug
repositioning, an accumulating body of reviews on the use of
databases for drug repositioning has been published (Behl et al.,2021; Kort and Jovinge, 2021; Reay and Cairns, 2021). Several
studies reviewing drug discovery approaches using
bioinformatics techniques and tools have been reported.
Moreover, in silico approaches for drug repositioning inindividual diseases such as depression (Mohammad Sadeghi
et al., 2021), spinal muscular atrophy (Chong et al., 2021), andcolorectal cancer (Beklen et al., 2021) have also been reported.
However, many of the approaches used for drug repositioning are
based on genome-wide association databases (Behl et al., 2021;

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TABLE 1 | Examples of medical information databases and life science databases.
Type
Spontaneous
Adverse Event
Reports database

National sick funds
and insurances
database

Gene database

Biobanks

Characteristics

Name

Website link

References

A database that compiles reports ofadverse events that occur when a drug
is administered to a patient. These
databases cataloging the adverse event
reports are the most widely used
databases for drug repositioning
because they can be used to investigate
drug-speciﬁc adverse events andestimate new drug effects

JADER

https://www.pmda.go.jp/index.html

FAERS

https://www.fda.gov/drugs/questionsand-answers-fdas-adverse-eventreporting-system-faers/fda-adverseevent-reporting-system-faers-publicdashboard
https://www.who-umc.org/

Claims database by National sick funds
and insurances is suitable for cohort
studies and epidemiological surveys
that are conducted over time

NDB

Gene databases can be used tocomprehensively examine the genetic
information generated by diseases anddrugs in the tissues and cells of humans
and mice and can be used to elucidate
mechanisms of action

Detailed information on blood,
pathology, patient information, etc., canbe obtained. Suitable for longitudinal or
epidemiological DNA analysis

Vigibase

Pharmaceuticals and Medical
Devices Agency, (2021)
FAERS, (2021)

Uppsala Monitoring Center,(2021)

https://www.mhlw.go.jp/stf/
seisakunitsuite/bunya/0000177182.
html
https://www.jmdc.co.jp/news/
news20200910/

Ministry of Health Labour andWelfare, (2021)

GEO

https://www.ncbi.nlm.nih.gov/geo/

LINCS
TargetMine

https://lincsproject.org/
https://targetmine.mizuguchilab.
org/ja/https://platform-docs.
opentargets.org/

NCBI Gene Expression Omnibus,(2021)
NIH LINCS PROGRAM, (2021)
TargetMine, (2021)

JMDC Claims database

Open Targets Platform

United Kingdom Biobank
All of Us
China Kadoorie Biobank
Taiwan Biobank
BioBank Japan, National
Center Biobank
Network, etc.

https://www.ukbiobank.ac.uk/
https://allofus.nih.gov/funding-andprogram-partners/biobank
https://www.ckbiobank.org/site/
https://www.twbiobank.org.tw/new_
web_en/https://biobankjp.org/https://www.
megabank.tohoku.ac.jp/english/
sample/https://ncbiobank.org/

JMDC, (2021)

Open Targets Platform, (2021)

UK Biobank, 2021
All of US Research program,(2016)
China Kadoorie Biobank, (2021)
Taiwan Biobank, (2019)
Biobank Japan, (2021); Eastern
Medical Megabank, (2020);
National Center Biobank, (2021)

JADER, Japanese Adverse Drug Event Report database; FAERS, FDA Adverse Events Reporting System; JMDC Claims database, Japan Medical Data Center Claims database; NDB, theNational database of Health Insurance Claims and Speciﬁc Health Checkups of Japan; GEO, Gene Expression Omnibus; LINCS, Library of Integrated Network-Based Cellular Signatures
Data Portal.

expression database managed by the National Center for
Biotechnology Information (NCBI), and the Library ofIntegrated Network-based Cellular Signatures (LINCS) (NIH
LINCS PROGRAM, 2021), a drug discovery tool funded by
the National Institutes of Health (NIH), are the most famous.
GEO hosts microarray data from more than 4.5 million samples;
it is possible to search for experiments of interest (e.g., disease
samples or samples of drug-exposed cells) and download therelevant data on GEO. The LINCS program aims to create a
database of changes in gene expression and cellular responses totreatment with various compounds. Research is being conducted
to develop new therapeutic agents for diseases by comparing thegene expression data associated with the diseases with the LINCS
cataloged information on changes in the gene expression using
these compounds.
TargetMine (TargetMine, 2021), an integrated data
warehousing system developed at the National Institutes ofBiomedical Innovation, Health and Nutrition, integrates
multiple components, such as genes, proteins, and pathways.
Therefore, it is possible to identify related diseases by combining
and analyzing data from multiple databases, i.e., speciﬁc genes
and drugs that target those genes (Chen et al., 2019; TargetMine

completed before the database can be used for research purposes,
which poses a hurdle for new entrants to the receipts researchﬁeld. The JMDC Claims database hosts data collected from healthinsurance associations in Japan. It has been collecting data since2005, with a total of 10 million cases (JMDC 2020 Year News,2021). The JMDC Claims database contains information on
diagnoses of injuries and illnesses, drugs prescribed, and the
medical procedures that were performed, and it is possible toevaluate the efﬁcacy and safety of drugs by analyzing data fromthe JMDC Claims database. Although there is a fee for thisservice, the database is advantageous in that the procedure issimple, and the data have already been curated to some extent.
Moreover, in this database, a unique ID is assigned to eachpatient, and it is possible to track patients over a long period
even if they visit multiple medical institutions or dispensing
pharmacies.

LIFE SCIENCE DATABASES
Among the life science databases, the Gene Expression Omnibus(GEO) (NCBI Gene Expression Omnibus, 2021), a gene

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Data Warehouse for Drug Discovery, 2021). Similarly, the Open
Targets Platform (Open Targets Platform, 2021) documents theevidence for the target-disease associations. The platform
incorporates molecular data from UniProt, phenotypes fromExpression Atlas, and drug information from ChEMBL and iseasy to handle (Koscielny et al., 2017).
In recent years, there have been attempts to use biobanks forthe transcriptome analysis of biological samples. For example, the
UK Biobank (UK Biobank, 2021) is a well-known biobank thatcurates information regarding blood and urine analysis and theliving environment of 500,000 people in the general population. It
also stores data genotyping and exome analysis data using
genotype arrays from blood DNA (Sudlow et al., 2015). In
other countries, research efforts such as the All of US(United States) (All of US Research Program, 2016), China
Kadoorie Biobank (China) (China Kadoorie Biobank, 2021),
and Taiwan Biobank (Taiwan) (Taiwan Biobank, 2019), arebeing developed by collecting data from a huge number ofcases as part of a national project. In addition, Japan is home
to several university hospital biobanks, including the three major
biobanks with approximately 700,000 collaborators, i.e., Biobank
Japan (Biobank Japan, 2021), Eastern Medical Megabank(Eastern Medical Megabank, 2020), and National Center
Biobank (National Center Biobank, 2021). Although it isnecessary to go through several contractual processes, such as
conﬁdentiality agreements, to handle patient information, it ispossible to obtain medical information linked to biologicalsamples, facilitating genomic research that is more in line withclinical practice.

collected over time and accumulated as big data. Researchusing such medical big data enables the analysis of patientswith various backgrounds and in various regions, thus
enabling the evaluation of efﬁcacy and safety in actual clinicalpractice and facilitating the detection of adverse events that are
not yet clear among a limited number of patients. Therefore, thedrug-repositioning method is considered the best drug-discovery
model, which uses big data to bridge basic and clinical medicine.

Examples of Life Science Database
Applications
The technological advancement of omics has generated a large
amount of biomedical data, rendering useful resources for drug
repositioning. For example, Dudley et al. conducted a study using
L1000CDS2—the predecessor of LINCS—and GEO to develop
new therapeutic agents for inﬂammatory bowel disease (Dudley
et al., 2011). First, they used microarray data from patients withinﬂammatory bowel disease downloaded from the GEO database
to identify the genes showing alteration in their expression in
response to inﬂammatory bowel disease. Next, these genes were
input into LINCS to search for drugs that could counteract thegenetic changes caused by inﬂammatory bowel disease. This
resulted in the identiﬁcation of topiramate, an antiepileptic
drug. Subsequently, its efﬁcacy was demonstrated in animalmodels of inﬂammatory bowel disease.

Examples of Medical Information
Databases Applications
Several studies have been reported on the use of medical
information databases for drug repositioning. For example,
Zhao et al. used the medical information databases and thelife science database (Zhao et al., 2013). Initially, Zhao et al.
used FAERS to identify candidate prophylactic drugs that reducethe incidence of myocardial infarction when used in combination
with rosiglitazone and other drugs. In addition, they validated the
efﬁcacy of the identiﬁed prophylactic candidates using theelectronic medical record data of Mount Sinai Medical Center.
For the candidate prophylactic agents whose efﬁcacy was
conﬁrmed upon the validation of the electronic medical record
data, we conducted pathway analysis using the DrugBank andestimated the mechanism of action of the candidate molecules.
The presumed mechanisms and the efﬁcacy of the candidate
prophylactic agents were veriﬁed in animal experiments using
mice. The results showed that exenatide, a glucagon-like peptide1 (GLP-1) receptor agonist, signiﬁcantly reduced the risk ofrosiglitazone-induced myocardial infarction.
In another study, Hashikawa et al. explored the role of the heatshock protein (HSP105) in preventing depression-like behavior
in mice (Hashikawa et al., 2017). HSP has neuroprotective effects,
and its expression in the brain is known to change under stressful
conditions. In this study, assuming an increase in HSP levels inthe brain may ameliorate depression, they evaluated the efﬁcacy
of teprenone, an HSP inducer, in treating depression. They
created a mouse model of depression and measured HSP
expression in the brain. The HSP105 expression in the brain

USE OF PHARMACOEPIDEMIOLOGY IN
DRUG REPOSITIONING
Bridging Basic Research to ClinicalResearch
The prognosis of various diseases, including cancer, has greatly
improved with the development of innovative drugs. However,the development cost per drug has been increasing each year
worldwide, which could be because clinical trials are becoming
increasingly larger. Additionally, the demand for therapeutic
agents against intractable and rare diseases is increasing(Dickson and Gagnon, 2004), which has resulted in theemergence of some ultra-expensive drugs (cost of a single
treatment ranging from several hundred to several tens of
millions of yen). In the conventional drug-discovery model,
bridging basic research to clinical research is often a problem,
and it is difﬁcult to predict the efﬁcacy and safety of a compoundin actual patients based on the results of cell and animal
experiments, which decreases the success rate of drug
development. Therefore, there is a need to develop therapeutic
drugs using unconventional drug discovery models. Drugrepositioning research using medical big data is a goodstrategy to compensate for the limitations associated withbasic pharmacological methods. As existing approved drugs
have already undergone clinical trials, various data such aslaboratory values and imaging data from each patient are

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was decreased, and the depressive behavior was improved in theteprenone-treated mice. Furthermore, in the JADER database,1,626 (3.784%) reports of depression in a group of 42,974
individuals without teprenone treatment were identiﬁed,
wherein only 10 (2.421%) in a group of 413 individuals
administered with teprenone showed depression. These data
showed a trend toward fewer reports of depression in theteprenone-treated group (odds ratio 0.63; 95% conﬁdence
interval [CI] 0.30–1.18; p  0.1920). These results suggest that
teprenone may be a novel antidepressant with a different
mechanism of action from conventional antidepressants byincreasing HSP expression in the brain.
Using the FAERS database, Hamano et al. evaluated the
efﬁcacy of diphenhydramine, an antiallergic drug, inpreventing cisplatin-induced kidney toxicity (Hamano et al.,2021). They reported that the administration ofdiphenhydramine in a mouse model of cisplatin-associatedrenal injury improved the characteristics of renal damage(which included worsening renal function, histopathologicalchanges, elevated inﬂammatory cytokines, and increasedoxidative stress and apoptosis), whereas the histamine
knockout mice did not show the same effects. The results
suggest that diphenhydramine exerts its effects throughmechanisms other than histamine receptors. In addition, they
conﬁrmed that diphenhydramine suppressed renal damage
without affecting the antitumor effect of cisplatin in mice withembedded cancer. To conﬁrm the efﬁcacy of cisplatin in clinicalpractice, they conducted a propensity score matching-based
retrospective chart review analysis of 1,467 patients who
received cisplatin. In 49 matched pairs, diphenhydraminetreated patients had lower incidence of cisplatin-induced renal
injury than non-treated patients. Furthermore, the inhibitory
effect of diphenhydramine on cisplatin renal injury was seenin all three approaches used in the study, i.e., database study, basic
study, and clinical study. Taken together, they proposed
diphenhydramine as a candidate drug for prophylaxis against
cisplatin-induced renal injury.

the presence of quetiapine, which was reversed by concomitantcalcitriol treatment in a PI3K-dependent manner.
Wakai et al. integrated the in silico and in vivo approaches to
identify potential therapeutic options for cisplatin-induced renal
injury and suggested that this approach could be successful indrug repositioning (Wakai et al., 2020). The study identiﬁed a
208-gene expression signature for cisplatin-induced renal injury
using the mouse kidney and human kidney organoidtranscriptome datasets available in the GEO database.
Subsequently, using the bioinformatics database CMap and
LINCS Uniﬁed Environment (CLUE) and FAERS database,the study identiﬁed palonosetron, a serotonin type 3 receptor
antagonist, as the candidate drug to reduce cisplatin-induced
acute kidney injury. Additionally, clinical data of 103 patientswith cisplatin treatment for head and neck cancer demonstrated ahigher efﬁcacy of palonosetron in suppressing kidney injury thanthat of ramosetron. Moreover, the study also showed that thesurvival rate of zebraﬁsh exposed to cisplatin was signiﬁcantly
increased by palonosetron than by other 5-HT3R antagonists.
Taken together, it can be inferred that combining the above
mentioned medical information data and drug discovery tools
could identify more suitable candidates for preventive
pharmacotherapy of adverse events and elucidate novel
mechanisms of action of the existing approved drugs(Nagashima et al., 2016; Wakai et al., 2020). Moreover, many
drug-repositioning studies have been conducted based on
information hosted on drug epidemiological databases(Figure 2), indicating the scope of developing novel
therapeutic strategies in areas lacking treatment options. In
Figure 3, we have summarized the drug repositioning studies
conducted by our research group. Additionally, we have planned
to identify drugs with preventive effects against largely intractablediseases and assess their side effects of drugs employing data
hosted on pharmaco-epidemiological databases (Figure 3).

EXAMPLES OF ACTUAL RESEARCH:
APPROACHES TO INTRACTABLEDISEASES

Collaboration Between Life Science andMedical Information Databases

We present three examples of drug-repositioning studies basedon medical big data for intractable diseases in our ﬁeld.
The ﬁrst example is similar to the second one, wherein we
combined data searches in the TargetMine with those in the
JMDC to extract candidate drugs to improve the prognosis ofcardiopulmonary arrest (Zamami et al., 2019). We initially used
TargetMine to identify 165 candidate drugs with vasodilatory
properties, such as calcium channel blockade; drug data obtained
from the JMDC for 2,227 cardiac arrest patients were used to exclude
the patient backgrounds based on the propensity score matching,following which, isosorbide nitrate, nitroglycerin, and nicardipine
were used as candidate drugs. The adjusted odds ratios for thesurvival of patients treated with isosorbide nitrate, nitroglycerin, andnicardipine were 3.35, 5.44, and 4.58, respectively. Therefore, theresults suggest that isosorbide nitrate, nitroglycerin, and nicardipine
may function as new therapeutic agents to improve the prognosis ofcardiac arrest patients.

Nagashima et al. focused on ﬁnding the molecular mechanism
underlying antipsychotic-induced hyperglycemia (Nagashima
et al., 2016). First, they searched for drug combinations in the
FAERS database and found that a combination with vitamin D
analogs signiﬁcantly decreased quetiapine–induced diabetes
mellitus. In clarifying the molecular mechanisms underlying
quetiapine-induced hyperglycemia, in GEO, 10 insulin
resistance-related genes were found to be downregulated in rat
liver after oral quetiapine administration. The most prominent
reduction in expression was observed for phosphatidylinositol 3kinase (PI3K), a known risk factor for diabetes mellitus.
Experimental validation using mice revealed that quetiapine
induced-insulin resistance was mitigated by cholecalciferol.
Further, the PI3K signaling pathway activity was reversed bycholecalciferol supplementation in skeletal muscle, and insulinstimulated glucose uptake into C2C12 myotube was inhibited in

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FIGURE 2 | Schematic of the database-based drug repositioning method. By analyzing the database of existing drugs, we can develop novel therapies in areas
where there were no treatment options.

FIGURE 3 | The drug repositioning studies conducted by our research group using the databases. Our future work focuses on developing drugs that have
preventive effects against largely intractable diseases and assessing their side effects.

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The second example pertains to a study on aortic dissection(Izawa-Ishizawa et al., 2019). Although aortic dissection is a
fatal condition, the only effective treatments are surgery andantihypertensive therapy. Using an established mouse model ofdrug-induced aortic dissection, we investigated the efﬁcacy of
pitavastatin, which is known to have endothelial protective
effects. Pitavastatin signiﬁcantly reduced the rate of aortic
dissection and rupture in this mouse model. Next, theresearchers used JADER to examine the users of 44 drugs at
risk for aortic dissection and found that 113 out of 95,090(0.119%) patients in the non-statin group had an aortic
dissection, compared with six out of 16,668 (0.036%) patientsin the statin group. Aortic dissection was reported signiﬁcantly
less frequently in the statin group (odds ratio 0.30; 95% CI
0.13–0.69; p  0.0043). The results of basic experiments and the
medical information database analysis suggest that statins may
inhibit vascular endothelial damage and serve as a newtreatment option for aortic dissection.
The third example is of a study on interstitial nephritis(Horinouchi et al., 2018), one of the major causes of chronic
kidney disease (CKD) and has emerged as a major clinical issue
in recent years. The Factor Xa (FXa) level is increased in various
inﬂammatory diseases; therefore, we investigated the potential
application of FXa inhibitors for the treatment of interstitialnephritis. Initially, we examined the effects of FXa inhibitors in amouse model of renal tubular interstitial ﬁbrosis and found that
the FXa inhibitors suppressed ﬁbrosis and inﬂammatory
responses. The FAERS database had 4,895 (0.060%) reports
of interstitial nephritis among 8,281,917 cases in the non-FXa
inhibitor group) and only 52 (0.039%) reports among 132,591
cases in the FXa inhibitor group (odds ratio 0.65; 95% CI
0.49–0.85). However, for warfarin, both an anticoagulant andan FXa inhibitor, the percentage of interstitial-nephritis-related
reports in the total number of reports was 0.060% (4,862/
8,131,377) and 0.056% (85/150,540) in the warfarin-free andwarfarin-use groups, respectively. There was no signiﬁcantdifference in the frequency of reports of interstitial nephritis
between the two groups (odds ratio 0.94; 95% CI 0.76–1.16;
p  0.0398). These results suggest that FXa inhibitors may serveas potential preventive agents for CKD that act by inhibiting
tubulointerstitial ﬁbrosis.

using FAERS analysis and a survey of hospital medical records.
Concomitant use of ﬁrst-generation 5-HT3 receptor antagonists(ondansetron, granisetron, and ramosetron) signiﬁcantly
increased the renal accumulation of cisplatin and worsened
renal damage, whereas concomitant use of palonosetron had noeffect on renal function in the analysis of the data from FAERS
and medical records. This revealed that the combination of
cisplatin and a ﬁrst-generation 5-HT3 receptor antagonist was
associated with a signiﬁcant increase in the number of reported
adverse renal events compared with the combination of cisplatin
and a second-generation 5-HT3 receptor antagonist. These
results indicate that second-generation 5-HT3 receptor
antagonists can be used safely during cisplatin-induced renal
injury therapy.
The second approach was used for bevacizumab-induced
hypertension (Yagi et al., 2021). Hypertension is a frequent
adverse event in bevacizumab-treated patients, and thedevelopment of hypertension has been suggested to be abiomarker of treatment response; FAERS found that
hypertension was less frequently reported in bevacizumabtreated patients who were using omeprazole. However, a
study of 58 patients diagnosed with colorectal cancer who
received initial treatment with XELOX or mFOLFOX6 plus
bevacizumab found that patients who used proton pumpinhibitors (PPIs) responded more poorly to treatment thanthose who did not. Using GEO analysis, we estimated themechanism of action and inferred that PPIs induce vascularendothelial growth factor (VEGF) expression. Using celllines, we found that PPIs induce VEGF expression in coloncancer cells and promote vascular endothelial cellproliferation.
The third approach pertains to a case of prevention of
anticancer drug-induced neutropenia (Hamano et al., 2019).
Using the data hosted on the JADER database, we reported
that alkalinizing agents, such as sodium bicarbonate, reduced
the frequency of irinotecan-induced neutropenia. Patientsusing irinotecan frequently experience adverse events, suchas diarrhea and neutropenia. Diarrhea can be alleviated byalkalinizing agents and herbal medicines (Takasuna et al.,
1996; Takeda et al., 2001). However, no effective prophylactic
agents have been identiﬁed for neutropenia to date. One of
the causes of neutropenia is SN-38, a metabolite of irinotecan.
Alkalinizing agents are known to suppress the reabsorptionof SN-38, and we assumed that alkalinizing agents could also
suppress irinotecan-induced neutropenia. Therefore, we
compared the incidence of neutropenia in patients who
received irinotecan with or without alkalinizing agentsbased on data extracted from electronic medical records.
The ﬁndings showed that the frequency of neutropenia
was low in the patients who used the alkalinizing agent,and the intensity of treatment could be maintained,suggesting that the alkalinizing agent could prolong thesurvival period. Furthermore, we conducted a morecomprehensive analysis using data from the JADER
database maintained by the PMDA. We found that thefrequency of neutropenia was lower in patients treatedwith alkalinizing agents in the studies using JADER data.

EXAMPLE OF ACTUAL RESEARCH:
APPROACH TO DRUG-INDUCED ADVERSE
EVENTThree examples of drug-repositioning studies using medical big
data for drug-induced adverse events are presented in thissection.
The ﬁrst approach, similar to the ﬁrst approach, is forcisplatin renal injury (Goda et al., 2021). Cisplatin is excreted
via the multidrug and toxin release (MATE) transporter, and a
5-HT3 receptor antagonist is used as an antiemetic. We
investigated the effect of the 5-HT3 receptor antagonist in amouse model of cisplatin-induced kidney injury and validated itusing medical big data. The results of the study were validated

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Drug-Repositioning Approaches Based on Database

PROBLEMS FACED BY DATABASERESEARCH

drug-repositioning research to identify new applications for the
existing approved drugs. Our group is focused on drugrepositioning research to develop preventive drugs for diseases
and anticancer drug-induced adverse events using multiple
medical big data and life science databases. In these studies,
the target population is clearly deﬁned as patients who use drugs,
making it easier to conduct both retrospective and prospective
studies. Therefore, the level of evidence can be increased by
combining the data from these databases with clinicalinformation from facilities with medical information or canundertake collaborative research. Thus, databases are suitable
to be used as a bridge between clinical and basic research. By
combining medical big data, including actual patient data, withbasic pharmacological experimental methods, the limitations of
the two methods can be complemented, and the safe and highly
effective prophylactic drug candidates can be identiﬁed. In
addition, life science databases that provide genetic
information are compatible with basic research. In the future,
it is expected that drug-repositioning research will be actively
conducted by utilizing data hosted on these databases and basic
research.

In using databases for drug repositioning, one has to beparticularly careful about the issue of bias. For example, someof the medical information in the databases we introduced in thisarticle are collected by spontaneous reporting by medical
professionals. This leads to many biases, such asinconsistencies in medical terminology due to reporter bias,
missing data, multiple reporting, and differences in reporting
rates among medical institutions. Therefore, the possibility thatthe results may include false positives and false negatives as wellas true positives must be considered. These demonstrations need
to be augmented by approaches other than those involving
medical information databases, such as basic pharmacologicalmethods.
On the other hand, the fact that we are not able to makeassessments for drug dosage decisions and routes of administrationin our study is a current research limitation. Precise dosage
determination requires a lot of validation and funding, and insome cases, there is a risk of failure of the clinical trial due to theappearance of adverse events associated with overdose (Peck,2021). The combination of database analysis methods that canassess this problem is a future challenge.
In addition, retrospective analysis of clinical data fromelectronic medical records includes noise, and overall data
heterogeneity when combining different types of data, such astranscriptome data, chemical structure data, and clinical literature
data, poses a challenge for effective drug repositioning. In our
research, to take advantage of the characteristics of each, we did not
unify them by language or numerical values but suggested
multifaceted and robust effectiveness with the ﬁnal match ofeach result. However, with future developments, it may bepossible to comprehensively analyze clinical data and multiple
databases through machine learning methods using natural
language. This realization is expected to lead to more consistentand advanced drug discovery strategies.

AUTHOR CONTRIBUTIONSYZ designed and developed the research concept; YZ and HH
wrote the ﬁrst draft of the manuscript with intellectual feedback
from all co-authors. YZ and HH made critical contributions tothe study so that they merit sharing an important author byline
position. All co-authors agreed to take responsibility for all
aspects of the research and gave ﬁnal approval to amanuscript prior to publication. Author ordering wasdetermined after discussion among the authors.

FUNDING
This work is the result of research that was supported by the Japan
Society for the Promotion of Science KAKENHI grants(20H05798 and 18K06785 to YZ).

DISCUSSION
Numerous medical big data and life science databases have beencreated. Researchers can choose the database according to theusage situation and the hypothesis they want to prove. A varietyof human data on existing approved drugs has been accumulated
as big data; therefore, big data are suitable for utilization in

ACKNOWLEDGMENTS
We would like to thank Editage (www.editage.jp/) for their help
with English-language editing.
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Conﬂict of Interest: The authors declare that the research was conducted in theabsence of any commercial or ﬁnancial relationships that could be construed as apotential conﬂict of interest.
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