Biomedicine & Pharmacotherapy 148 (2022) 112744

Contents lists available at ScienceDirect

Biomedicine & Pharmacotherapy
journal homepage: www.elsevier.com/locate/biopha

Identification of prophylactic drugs for oxaliplatin-induced peripheral
neuropathy using big data
Yoshito Zamami a, b, 1, Takahiro Niimura c, 1, Takehiro Kawashiri d, 1, Mitsuhiro Goda e,
Yutaro Naito e, Keijo Fukushima f, Soichiro Ushio b, Fuka Aizawa c, Hirofumi Hamano g,
Naoto Okada c, Kenta Yagi g, Koji Miyata e, Kenshi Takechi h, Masayuki Chuma i,
Toshihiro Koyama j, Daisuke Kobayashi d, Takao Shimazoe d, Hiromichi Fujino f,
Yuki Izawa-Ishizawa k, Keisuke Ishizawa c, e, *aDepartment of Clinical Pharmacy, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
Department of Pharmacy, Okayama University Hospital, Okayama, Japanc
Department of Pharmacy, Tokushima University Hospital, Tokushima, Japand
Department of Clinical Pharmacy and Pharmaceutical Care, Graduate School of Pharmaceutical Sciences, Kyushu University, Fukuoka, Japane
Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japanf
Department of Pharmacology for Life Sciences, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japang
Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Tokushima, Japanh
Department of Drug Information Analysis, College of Pharmaceutical Sciences, Matsuyama University, Matsuyama, Japani
Department of Hospital Pharmacy and Pharmacology, Asahikawa Medical University, Asahikawa, Japanj
Department of Pharmaceutical Biomedicine, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama, Japank
Department of Pharmacology, University of Tokushima Graduate School of Biomedical Sciences, Tokushima, Japanb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Drug repositioning
Simvastatin
Oxaliplatin-induced peripheral neuropathy
Database

Background: Drug repositioning is a cost-effective method to identify novel disease indications for approved
drugs; it requires a shorter developmental period than conventional drug discovery methods. We aimed to
identify prophylactic drugs for oxaliplatin-induced peripheral neuropathy by drug repositioning using data fromlarge-scale medical information and life science information databases.
Methods: Herein, we analyzed the reported data between 2007 and 2017 retrieved from the FDA’s database of
spontaneous adverse event reports (FAERS) and the LINCS database provided by the National Institute of Health.
The efficacy of the drug candidates for oxaliplatin-induced peripheral neuropathy obtained from the database
analysis was examined using a rat model of peripheral neuropathy. Additionally, we compared the incidence of
peripheral neuropathy in patients who received oxaliplatin at the Tokushima University Hospital, Japan. The
effects of statins on the animal model were examined in six-week-old male Sprague–Dawley rats and seven oreight-week-old male BALB/C mice. Retrospective medical chart review included clinical data from Tokushima
University Hospital from April 2009 to March 2018.
Results: Simvastatin, indicated for dyslipidemia, significantly reduced the severity of peripheral neuropathy andoxaliplatin-induced hyperalgesia. In the nerve tissue of model rats, the mRNA expression of Gstm1 increased withstatin administration. A retrospective medical chart review using clinical data revealed that the incidence of
peripheral neuropathy decreased with statin use.
Conclusion and relevance: Thus, drug repositioning using data from large-scale basic and clinical databases en­ables the discovery of new indications for approved drugs with a high probability of success.

Abbreviations: AE, Adverse event; DRG, dorsal root ganglion; FAERS, FDA’s database of spontaneous adverse event reports; GST, glutathione S-transferase; LINCS,
Library of Integrated Network-based Cellular Signatures; MedDRA, Medical Dictionary for Regulatory Activities; OIPN, oxaliplatin-induced peripheral neuropathy;
PWT, paw withdrawal threshold; ROR, reporting odds ratio; ROS, reactive oxygen species.
* Correspondence to: Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto,Tokushima 770-8503, Japan.
E-mail address: ishizawa@tokushima-u.ac.jp (K. Ishizawa).
1
These authors contributed equally to this study.
https://doi.org/10.1016/j.biopha.2022.112744
Received 24 October 2021; Received in revised form 6 February 2022; Accepted 18 February 2022
Available online 28 February 2022
0753-3322/© 2022 Published by Elsevier Masson SAS. This is an open access(http://creativecommons.org/licenses/by-nc-nd/4.0/).

article

under

the

CC

BY-NC-ND

licenseY. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

recommendations; the remaining 6,994,117 reports were analyzed.
Access 2016 (Microsoft, Redmond, WA, USA) was used to process thedata and R ver. 3.2.1 (R Foundation for Statistical Computing, Vienna,
Austria) for statistical analysis. Peripheral neuropathy was defined as“peripheral sensory neuropathy” and “neuropathy peripheral” accord­
ing to the Medical Dictionary for Regulatory Activities (MedDRA) ver.
21.0.
The risk of AEs was assessed using the reporting odds ratio (ROR)
and 95% confidence interval (CI) [16]. Patients using oxaliplatin wereclassified into four groups: (A) patients who used drug A and reported
peripheral neuropathy; (B) patients who used drug A and did not report
peripheral neuropathy; (C) patients who did not use drug A and reported
peripheral neuropathy; and (D) patients who did not use drug A and did
not report peripheral neuropathy. ROR and 95% CI were calculatedusing the following equations:

1. Introduction
Drug development is an expensive and time-intensive process.
Frequently, compounds that show efficacy in the early developmental
stages, fail in clinical trials because of low efficacy, unexpected adverse
effects, and inappropriate pharmacokinetic results. This gap between
basic research and clinical development is known as “the valley ofdeath”—a major bottleneck in drug discovery and approval. The in­vestment in drug development and cost of drug discovery continue torise annually [1].
Drug repositioning could overcome these obstacles by discovering
new uses of approved drugs [2,3]. As information on adverse effects andpharmacokinetics of approved drugs already exists, drug repositioning isless likely to stall drug development compared with conventional drug
discovery processes; moreover, it can reduce the associated time andcost [4].
Large-scale drug discovery research has been conducted using largescale medical information including medical electronic records [5]. A
meta-analysis of clinical trial data revealed that the antiepileptic drug
eslicarbazepine could be effective against partial epileptic seizures,
thereby expanding its applications [6]. Large-scale medical information
contains abundant clinical data on approved drugs and is suitable fordrug repositioning. Several drug repositioning studies have utilized
large-scale medical information [7–9]. Previously, we demonstrated invivo that teprenone, an approved drug for peptic ulcers, improvesdepressive symptoms by upregulating heat shock protein expression.
Data analyzed from an adverse event (AE) reporting database showedthat the reporting rate of depression may decrease in patients treatedwith teprenone [10].
Big data on life sciences, such as gene expression data, have been
accumulating and are used to systematically evaluate complex patho­
logical conditions in humans. The Library of Integrated Network-based
Cellular Signatures (LINCS) is a gene expression database developedby the National Institutes of Health. It contains data obtained using
human-derived cell lines and facilitates the search for compounds that
may cause similar or opposite variations in the expression of genes ofinterest [11]. Using the connectivity map, the predecessor of LINCS, it
was demonstrated that the antiepileptic drug topiramate counteracts
gene expression changes in inflammatory bowel disease [12]. In a rat
inflammatory bowel disease model, tissue damage and severity werereduced by topiramate.
We hypothesized that combining big data on clinical informationand gene expression would help predict drug efficacy in humans andinfer action mechanism. Herein, we aimed to establish a new drug
development platform to overcome the “valley of death”, the gap be­
tween early-stage discoveries and translation to novel therapeutics. We
focused on the effect of non-treatment to verify the usefulness of thisapproach. As an example, we investigated strategies to prevent
oxaliplatin-induced peripheral neuropathy (OIPN). Oxaliplatin, a key
anticancer drug, causes peripheral neuropathy with numbness inapproximately 90% of the patients [13]. Although various drugs arepromising prophylactic agents for OIPN, no preventive method has been
established owing to barriers in clinical practice [14,15]. We evaluatedthe usefulness of an efficient drug repositioning method that integrates
data from a large-scale medical information database and life science
information database by identifying approved drugs that serve as OIPN
prophylactic agents.

ROR = (A/B)/(C/D)(
95%CI = exp lnROR ± 1.96)
√̅̅̅
1 1 1 1+ + +A B C D

Drugs with an ROR < 0.5 and significantly lower frequency of OIPN
were selected as candidates.
As the AE database is an anonymized open-access database, institu­
tional review board approval and informed consent were not required
according to the Ministry of Health, Labor, and Welfare’s Ethical
Guidelines for Epidemiological Research.
We screened review articles on OIPN and selected nine genes asso­
ciated with the development or exacerbation of peripheral neuropathy[17–20]. Five genes associated with promoting peripheral neuropathy
were classified as “up genes” and four genes associated with suppression
were “down genes” (eTable 1 in the Supplement). These genes wereentered in a web-based search application LINCS L1000CDS2, a searchengine that identifies compounds that mimic or reverse the gene
expression entered by the user [21]. The search is based on the gene
expression data of 3924 compounds collected from the National Instituteof Health’s LINCS project [22]. When entering a list of up and down
genes into the LINCS L1000CDS2, cosine distance score is calculated forthe gene expression data of each compound against the entered gene list.
The top 50 compounds with the highest cosine distance score are pre­sented along with their associated genes as candidate compounds that
inhibit the expression of the input genes. Among the compounds in the
search results, compounds already approved as drugs were selected asprophylactic drug candidates.
Therefore, the drugs extracted using both FAERS and LINCS analyses
were selected as candidate drugs and validated using in vitro and in vivo
experiments.
2.2. Animals and ethical considerations
Six-week-old male Sprague–Dawley rats were purchased from
Kyudo, Saga, Japan, while seven- or eight-week-old male BALB/C mice
were purchased from Clea Japan, Tokyo, Japan. All animals had freeaccess to food and water and were maintained under a 12-h light/darkcycle.
All animal experiments were approved by the Ethical Committee forAnimal Research of Tokushima University (No. T28–69). We followed
the “Animal Research: Reporting of In Vivo Experiments” reporting
guidelines.

2. Methods
2.1. Prophylactic drug candidate selection

2.3. Validating peripheral neuropathy mitigation

From the FDA website, we downloaded 7,738,415 spontaneous AE
reports that were submitted to FDA’s database of spontaneous adverse
event reports (FAERS) between the first quarter of 2007 through to thefirst quarter of 2017. Duplicate data were excluded following FDA

Sprague–Dawley rats were divided into four groups—vehicle, 4 mg/
kg oxaliplatin (Tokyo Chemical Industry, Tokyo, Japan), oxaliplatin+ 1 mg/kg statins (simvastatin, atorvastatin, and rosuvastatin; Tokyo
Chemical Industry), and oxaliplatin + 10 mg/kg statins (N = 6 per2

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

group). The effects of statins on the animal model were examined.
Oxaliplatin 4 mg/kg (Tokyo Chemical Industry, Tokyo, Japan) preparedin 5% glucose solution was intraperitoneally administered twice a week
for 4 weeks (days 1, 2, 8, 9, 15, 16, 22, and 23). Statins (simvastatin,
atorvastatin, and rosuvastatin; Tokyo Chemical Industry) dispersed in
0.5% methylcellulose were administered orally five times a week forfour weeks (days 1–5, 8–12, 15–19, and 22–26) at a dose of 1 or 10 mg/
kg/day. The vehicle group was administered 5% glucose and 0.5%
methylcellulose solution.
Mechanical pain threshold was assessed via the von Frey test before
the first dose (day 0) and on days 5, 12, 19, and 26. The von Frey fila­
ment (Touch Test Sensory Evaluator Set, Linton Instrumentation, Nor­folk, UK) was in contact with the center of the hind paw for 6 s each. The
paw withdrawal threshold (PWT) was calculated using the up–down
method [23].

2.7. Transfection and gene silencing with siRNAs
To perform siRNA transfection, the cultured PC12 cells were inocu­
lated with DMEM containing 10% FBS and incubated for 24 h. siRNAtargeting rat Gstm1 (Predesigned siRNA, rat Gstm1, 24423; Bioneer,
Oakland, CA, USA) or scrambled control (sc-37007; Santa Cruz) wastransfected using Lipofectamine RNAiMAX (Life Technologies, Carlsbad,
CA, USA) for 24 h according to the manufacturer’s instructions (100 nM
final density). The silencing efficiency was measured byimmunoblotting.
2.8. Western blotting
Western blot analysis was performed as described previously [25].
Briefly, PC12 cells cultured in 24-well plates were lysed with cell lysis
buffer (CelLytic MT Cell Lysis Reagent; Sigma-Aldrich, St. Louis, MO,
USA) and then homogenized using HANDY SONIC UR-20 P (Tommy,
Tokyo, Japan) on ice. Following centrifugation at 15,294 × g (4 ◦ C), thesupernatant was used as the protein sample. The samples were added tothe sodium dodecyl sulfate sample buffer, boiled, and loaded onto
4–20% polyacrylamide gels (Mini-PROTEAN TGX Precast Gels; Bio-Rad
Laboratories) to be separated by electrophoresis at a constant current of
0.02 A/gel. The samples were electro-transferred onto polyvinylidene
fluoride (PVDF) membranes at a constant voltage of 24 V for 10 min,and the membranes were blocked with Bullet Blocking One for Western
Blotting (Nacalai Tesque, Kyoto, Japan). Anti-Gstm1 (Anti Gstm1
Rabbit-Poly, GTX113448; GeneTex, Irvine, CA, USA) antibody diluted at
1:1000 with Can Get Signal Immunoreaction Enhancer Solution 1(Toyobo, Tokyo, Japan) was used as the primary antibody. Anti-rabbit
IgG antibody (ECL Anti-rabbit IgG horseradish peroxidase-linked
whole antibody (from donkey), NA934V; GE Healthcare, Chicago, IL,
USA) diluted at 1:1000 with Can Get Signal Immunoreaction Enhancer
Solution 1 (Toyobo) was used as the secondary antibody. The PVDF
membranes were treated using the Amersham ECL western blotting
analysis system (GE Healthcare) for 5 min prior to visualization of the
bands. Band luminescence was photographed using ChemiDoc (Bio-Rad
Laboratories) and quantified using ImageJ. Protein expression levels
were standardized with anti-beta-actin antibody (1:1000, β-actin(E4D9Z) Mouse mAb; Cell Signaling Technology, Danvers, MA, USA),and anti-mouse IgG antibody was used as the secondary antibody(1:1000, ECL Anti-mouse IgG horseradish peroxidase-linked whole
antibody (from sheep), GE Healthcare).

2.4. Sciatic nerve axonal degeneration
Sciatic nerves were collected from rats on day 30. They were pro­
cessed with 2% (w/v) glutaraldehyde and then with 8% (w/v) sucrose.
The samples were then embedded in Epon, sliced, and stained with to­luidine blue. The sliced samples were evaluated by light microscopy(BX51; Olympus, Tokyo, Japan) and analyzed using ImageJ 1.51.
Circularity was calculated using the following formula:
Circularity =4π × (axonalarea)(axonalperimeter)2

2.5. Real-time polymerase chain reaction (PCR) of OIPN rat model
tissues
The bilateral dorsal root ganglion (DRG) of the lumbar vertebrae(L4–L6) was collected from the OIPN rats. RNA was extracted according
to the manufacturer’s instructions using an RNA extraction solution(NIPPON GENE, Tokyo, Japan). The RNA was reverse transcribed using
the PrimeScript RT Reagent Kit (Takara Bio, Shiga, Japan) and PCR
Thermal Cycler Dice (Takara). In each sample, the cDNA and forward
and reverse primers (eTable 2 in the Supplement) were mixed withThunderbird SYBR qPCR Mix (Toyobo, Osaka, Japan), and PCR was
performed using Applied Biosystems StepOnePlus (Applied Biosystems,
Waltham, MA, USA). Using rat GAPDH as the internal standard, gene
expression level was determined by the ΔΔCt method.

2.9. Tumor-bearing model mouse experiment2.6. Cell culture and cell viability assay

Mouse colorectal cancer cells, Colon26 (1 × 106 cells), were sus­pended in DMEM (Nacalai Tesque) and subcutaneously injected into theright abdomen of BALB/C mice, which showed no tumor expansion 5days post implantation. The mice whose tumor volume could not bemeasured were excluded from the experiment. The included mice weredivided into three groups—vehicle, 6 mg/kg oxaliplatin, and oxaliplatin+ 15 mg/kg statins.
Oxaliplatin and statins were administered 5 days after tumor cell
implantation. Oxaliplatin (6 mg/kg/day) was administered intraperi­
toneally (10 mL/kg twice a week for 2 weeks; days 1, 2, 8, and 9).
Simvastatin, atorvastatin, and rosuvastatin (15 mg/kg/day) wereadministered orally five times a week for 2 weeks (days 1–5 and 8–12).
Oxaliplatin was diluted in 5% glucose solution and statins in 0.5%
methylcellulose. The vehicle group was treated with 10 mL/kg 0.5%
methylcellulose solution. Tumor volume was measured every 2 days
(days 1, 3, 5, 7, 9, 11, 13, and 15) for tumor length (a: mm), short
diameter (b: mm), and height (h: mm). Tumor volume (V: mm3) was
calculated using the following formula [26]:We used PC12 cells (rat adrenal gland cell; American Type Culture
Collection, Manassas, VA, USA) to investigate the effect of statins on
oxaliplatin-induced cell death. The cells were maintained and subcultured in Dulbecco’s modified Eagle’s medium (DMEM) containing
10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 μg/mL
streptomycin in 95% air/5% CO2 at 37 ◦ C, according to the culture
protocol [24].
Cell viability was assessed using the Cell Counting Kit-8 (Dojindo
Laboratories, Kumamoto, Japan) according to the manufacturer’s in­structions (N = 8–16). Briefly, the cells were seeded in 96-well plates at
a concentration of 5 × 104 cells/well and incubated for 24 h at 37 ◦ C.
Subsequently, the cells were cultured in DMEM containing 3 μM oxali­
platin and statin for 24 h. Thereafter, the cells (with and without oxa­
liplatin) were subjected to statin treatment. Cell viability was assessedby measuring the absorbance of WST-8 formazan at 450 nm using aplate reader (Model680 Microplate Reader; Bio-Rad Laboratories, Her­
cules, CA, USA). For experiments using small-interfering RNA (siRNA),the cells were seeded at 3 × 104 cells/well, treated with or without 3 μM
oxaliplatin and statins (0.1, 1, and 10 μM for 24 h after siRNA treatment,
and assessed by measuring WST-8 absorbance.

V = (πabh)/63

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

2.10. Retrospective medical chart review

Table 2
Drug candidate selection using the LINCS analysis.

We retrospectively collected patient data from Tokushima University
Hospital from April 2009 to March 2018. Patients aged ≥ 18 years who
received oxaliplatin were included in the medical chart review. The
following patients were excluded: patients who had previously received
oxaliplatin; patients with a history of peripheral neuropathy due to otheranticancer agents or diseases; clinical trial participants; patients who
had undergone nerve block therapy; regimens containing oxaliplatin of
one cycle or less. OIPN was defined as grade 1 or higher, based on the
Common Terminology Criteria for Adverse Events ver. 5 [27]. We also
analyzed patient background (age and sex), laboratory data, cancer
types, and concomitant drugs.
Medical charts were reviewed in compliance with the Ministry of
Health, Labor, and Welfare’s Ethical Guidelines for EpidemiologicalResearch. The study was approved by the Ethics Committee ofTokushima University Hospital (No. 3275) and conformed to theguidelines of the Declaration of Helsinki.

Cosine distance
score
0.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28570.28572.11. Statistics

0.28570.28570.28570.28570.2857

Differences between groups in medical chart review were comparedusing Mann-Whitney U test for continuous variables and Fisher’s exact
test for categorical variables. The effect of statin use on peripheral
neuropathy was calculated with a Cox proportional hazards model
adjusted for age. A one-way analysis of variance was performed forcomparisons involving three or more groups, and Tukey’s test was
performed as a post hoc analysis. R ver. 3.2.1 was used for statisticalanalysis, and results with two-tailed P < 0.05 were consideredsignificant.

0.2857

Perturbation

Cell line

Dose
(μM)

Time
(h)

Anagrelide hydrochloride
Bromhexine
hydrochloride
Bromocryptine mesilate
Cimetidine
Dihydroergocristine
Docosahexaenoic acid
Etodolac
Guanabenz acetate
Ipratropium bromide
Isoxsuprinehydrochloride
Labetalol hydrochloride
Lidocaine hydrochloride
Losartan
Nabumetone
Olaparib
Ondansetronhydrochloride
Perindopril erbumine
Rimexolone
Simvastatin
Talipexole
Ticlopidinehydrochloride
Trimipramine maleate

A375
MCF7

10.0
10.0

6.0
6.0

A375
HA1E
HT29
HA1E
PC3
PC3
HT29
HCC515

10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0

24.0
6.0
24.0
24.0
6.0
6.0
6.0
6.0

PC3
HA1E
HCC515
HCC515
PC3
A549

10.0
10.0
10.0
10.0
10.0
10.0

24.0
24.0
24.0
6.0
24
24.0

HA1E
VCAP
A375
PC3
HCC515

10.0
10.0
10.0
10.0
10.0

24.0
24.0
6.0
24.0
6.0

PC3

10.0

24.0

3. Results

Compounds that promote the expression of suppressor genes and suppress theexpression of OIPN-related promoter genes were identified using the LINCS
analysis. Three kinds of approved drugs were extracted. Cell line, Dose, andTime indicate the experimental conditions under which the gene expression datastored in LINCS L1000CDS2 were acquired. OIPN, oxaliplatin-induced periph­eral neuropathy; LINCS, Library of Integrated Network-based Cellular
Signatures.

3.1. Identification of candidate drugs

3.2. Effect of statins on rat OIPN model

Thirteen drugs with an ROR < 0.5 were identified using data fromthe FAERS (Table 1), including allopurinol, an antipodagric drug, andmetformin, an antidiabetic drug. For the LINCS analysis, 22 drugs
including the local anesthetic lidocaine and the antihypertensive drug
losartan were extracted (Table 2). Simvastatin was included as a com­mon drug in both FAERS and LINCS analyses. Simvastatin has relatively
fewer reports of peripheral neuropathy according to the FAERS analysis
and of increased expression of glutathione S-transferase (GST)-related
genes according to the LINCS analysis.
The reporting frequency of OIPN was significantly lower for not only
simvastatin but also all HMG-CoA reductase inhibitors (eTable 3 in the
Supplement). Therefore, to clarify whether this effect is common among
HMG-CoA reductase inhibitors, we conducted in vitro/in vivo experi­ments of atorvastatin and rosuvastatin in addition to simvastatin.

The von Frey test in OIPN model rats revealed no difference in the
PWT between the groups on day 0. The PWT of the oxaliplatin groupdecreased with an increase in the cumulative dose; on day 26, the PWT
was significantly lower than that of the vehicle group. However, thePWT of the vehicle and oxaliplatin + statin groups was not significantly
different over 26 days. On day 26, the PWT of the oxaliplatin + statingroup was significantly higher than that of the oxaliplatin group andsimilar to that of the vehicle group (Fig. 1A–C). We focused on statins ascandidate drugs for OIPN prevention and used toluidine blue staining todetermine their protective effect, if any, against axonal degeneration of
the sciatic nerve. In oxaliplatin-treated rats, axon circularity wassignificantly reduced. Conversely, statin treatment suppressed theoxaliplatin-induced degeneration of nerve axons (eFig. 1).

Table 1Drug candidate selection using the FAERS analysis.
Drug nameAllopurinol
Bisoprolol
Cytarabine
Enoxaparin sodium
Levothyroxine sodium
Metformin hydrochloride
Metoprolol tartrate
Morphine
Omeprazole
Pantoprazole sodium
Prochlorperazine
Ramipril
Simvastatin

Peripheral neuropathy reporting rate (number of reports)

ROR

Without the drug of interest

With the drug of interest(95% CI)

5.48% (1271/23192)
5.48% (1273/23234)
5.49% (1274/23226)
5.49% (1270/23139)
5.48% (1270/23165)
5.53% (1269/22953)
5.48% (1274/23256)
5.49% (1273/23198)
5.53% (1258/22762)
5.53% (1265/22868)
5.48% (1273/23233)
5.49% (1272/23170)
5.52% (1267/22967)

2.03% (4/197)
1.29% (2/155)
0.61% (1/163)
2% (5/250)
2.23% (5/224)
1.38% (6/436)
0.75% (1/133)
1.05% (2/191)
2.71% (17/627)
1.92% (10/521)
1.28% (2/156)
1.37% (3/219)
1.9% (8/422)

0.36 (0.13–0.96)
0.23 (0.06–0.91)
0.11 (0.01–0.76)
0.35 (0.14–0.85)
0.39 (0.16–0.96)
0.24 (0.11–0.53)
0.13 (0.02–0.94)
0.18 (0.05–0.73)
0.48 (0.29–0.77)
0.33 (0.18–0.63)
0.22 (0.06–0.9)
0.24 (0.08–0.75)
0.33 (0.16–0.67)

P value
.03
.02
< .01
.01
.03
< .01
.01
< .01
< .01
< .01
.02
< .01
< .01

Statistical analysis was conducted using Fisher’s exact test. FAERS, FDA Adverse Event Reporting System; ROR, reporting odds ratio; CI, confidence interval.
4

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

Fig. 1. Effect of statins on mechanical allodynia in the OIPN rat model.
Time course of paw withdrawal threshold (PWT) on oxaliplatin-induced
mechanical allodynia in the von Frey test of (A) simvastatin, (B) atorvasta­tin, or (C) rosuvastatin administration. On day 26, the PWT of the oxaliplatingroup was significantly lower than that of the vehicle group, and the PWT of
the oxaliplatin + statin group was significantly higher than that of the oxa­
liplatin group. Data are expressed as mean ± standard error. (N = 6,
*P < .05, **P < .01 vs. vehicle, ††P < .01 vs. oxaliplatin alone; Tukey’s test).
OIPN, oxaliplatin-induced peripheral neuropathy; Sim, simvastatin; Ato,
atorvastatin; Ros, rosuvastatin.

5

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

3.3. Effect of statins on Gstm1 expression

3.4. Effects of statins on oxaliplatin properties

We evaluated Gstm1 to determine its neuroprotective effect against
OIPN using LINCS L1000CDS2 and the DRG rat model. Real-time PCR
was conducted with RNA samples extracted from the DRG of the rats.
The oxaliplatin group showed a significant decrease in Gstm1 mRNA
expression compared with the vehicle group. All oxaliplatin + statingroups showed a significant increase in Gstm1 expression compared withthe oxaliplatin group (Fig. 2A). In vitro PC12 cell viability significantly
reduced within 24 h of oxaliplatin exposure, with cell viability being
subsequently elevated by simvastatin (≥ 1 μM), atorvastatin (10 μM), or
rosuvastatin (10 μM) (Fig. 2B). In Gstm1 knockdown (eFig. 2A), PC12cell viability was reduced in an oxaliplatin concentration-dependent
manner (eFig. 2B); statins did not improve PC12 cell viability (Fig. 2C).

In the tumor-bearing mouse model experiment, the tumor volume ofthe oxaliplatin group was significantly lower than that of the vehicle
group on day 15. Furthermore, the tumor volume of the simvastatin andatorvastatin groups was similar to that of the oxaliplatin group. The
tumor volume of the rosuvastatin group was significantly lower thanthat of the vehicle and oxaliplatin groups (eFig. 3).
3.5. Retrospective medical chart review of oxaliplatin-treated patients
receiving statins
The medical charts of 392 patients treated with oxaliplatin from
April 2009 to March 2018 were reviewed. Among these, 115 patientswere excluded, including 10 patients who had received oxaliplatin, 15

Fig. 2. Effects of oxaliplatin and statins on Gstm1 mRNA expression. The expression of Gstm1 mRNA in the rats of the (A) simvastatin, atorvastatin, androsuvastatin groups. The oxaliplatin group showed a significant decrease in Gstm1 mRNA expression in DRG neuron compared with the vehicle group. The oxali­
platin + statin group showed a significant increase in Gstm1 mRNA expression compared with the oxaliplatin group. Data are expressed as mean ± standard error.
(N = 9–10, **P < .01 vs. vehicle. †P < .05, ††P < .01 vs. oxaliplatin alone; Tukey’s test). PC12 cell viability following GST knockdown in the (B) simvastatin,
atorvastatin, and rosuvastatin groups. PC12 cell viability was reduced by exposure to oxaliplatin, but the reduction in cell viability was attenuated by the addition of
statins. Data are expressed as mean ± standard error. (N = 8–16, **P < .01 vs. vehicle. †P < .05, ††P < .01 vs. oxaliplatin alone; Tukey’s test). (C) Effect of statins on
oxaliplatin-induced Gstm1 knockdown cell death (N = 8–16, **P < .01 vs. vehicle; Tukey’s test). Statins did not attenuate oxaliplatin-induced decrease in theviability of PC12 cells with Gstm1 knockdown. Data are expressed as mean ± standard error. DRG, dorsal root ganglion.
6

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

their safety is well-established. Therefore, statins may serve as newagents for OIPN management.
Herein, we elucidated the mechanism underlying OIPN managementwith statins. GST promotes the detoxification of reactive oxygen species(ROS) via glutathione conjugation [31]. Oxidative stress induction
through mitochondrial dysfunction is a mechanism underlying OIPN
pathogenesis, and antioxidants reportedly suppressed OIPN in animalstudies and clinical trials [29]. Statins may suppress peripheral neu­
ropathy by reducing ROS levels in nerve cells via increased Gstm1
expression. Therefore, Gstm1 may be involved in the neuroprotective
mechanism of statins. We examined this using gene expression data andperforming in vitro and in vivo experiments. Statins increased Gstm1
expression in DRG tissue from model rats. Gstm1 knockdown in PC12
cells reduced the inhibitory effect of statins on peripheral neuropathy.
Thus, our method can predict drug efficacy in humans and infer action
mechanism.
This study had some limitations. First, the gene expression data
included in LINCS were based on cell line experiments and did not fullyreflect the condition in humans. Second, the FAERS database curates
reports that may contain biases including underreporting and over­reporting. Therefore, we conducted a retrospective survey using elec­
tronic medical record data providing more detailed patient information.
The results also suggest that statins suppressed OIPN development.
Currently, we are conducting a multicenter retrospective study toinvestigate statin efficacy in more detail.
The candidate drugs identified using a single method are yet to bevalidated; however, utilizing information obtained through multiple
methods and from multiple databases can mitigate these limitations.
Using data from a gene expression database, relevant genes may be
identified, and their detailed action mechanisms can be examined using
basic experiments. Additionally, clinical data can be used to verify drug
efficacy and predict corresponding therapeutic and AEs.
This study shows that repositioning conventional drugs may be
useful as a rapid and inexpensive drug development platform. All the
databases used in this study are publicly available. Additionally,
numerous other drug-related databases are available nowadays. More­over, these databases are continuously updated as new drugs or data
become available, thereby providing a continuously growing source of
information that may be used for drug repositioning. The application of
our approach to investigate drug-induced AEs other than peripheral
neuropathy may contribute to improving the quality of life of cancer
survivors.

Table 3
Peripheral neuropathy incidence between the non-statin and statin groups.
Non-statin
user

Statin user

Odds
ratio

P
value

Peripheral neuropathy

220 (87.65%)

0.31

.012

Without peripheral
neuropathy

31 (12.35%)

17(65.38%)9 (34.62%)

Statistical analysis was performed using the logistic regression analysis with age
and statin used as covariates.

patients with a history of peripheral neuropathy, 42 clinical trial par­ticipants, and 48 patients who discontinued treatment within one cycleor less. The data of the remaining 277 subjects were analyzed; only 26 ofthese patients were treated with statins. The patients’ age in the statingroup was significantly higher than that in the non-statin group. There
were no significant differences in sex, renal function, hepatic function,
cancer type, or cancer stage between the groups (eTable 4 in the Sup­
plement). The incidence of peripheral neuropathy was 87.6% in the nonstatin group versus 66.7% in the statin group (Table 3). The KaplanMeier analysis showed that peripheral neuropathy incidence in thestatin group was lower than that in the non-statin group (eFig. 4).
4. Discussion
We aimed to identify prophylactic drugs for OIPN by drug reposi­
tioning using information hosted on medical and life science databases.
We verified the usefulness of the proposed research method for inves­tigating OIPN, which decreases the quality of life of cancer survivors.
Simvastatin was examined as a therapeutic drug candidate based on the
AE report database and drug discovery tool analyses. In OIPN model
rats, statins including simvastatin significantly suppressed the hyper­
algesic reaction. PCR using neural tissue samples from OIPN model rats
and knockdown experiments suggested that the inhibitory effect ofstatins on OIPN was mediated by increased Gstm1 expression. We
further confirmed in a tumor-bearing mouse model that statins do not
reduce antitumor effects, even in combination with oxaliplatin. A
retrospective chart review of clinical patient data suggested that statins
inhibit OIPN. Thus, statins may suppress peripheral neuropathy that
occurs as an AE without interfering with the therapeutics of oxaliplatin.
The United States has more than 13 million cancer survivors, with 2
million surviving for ≥ 15 years. However, the AEs of anticancer drugs
are associated with decreased quality of life and increased duration of
hospital stay and medical costs, resulting in significant economic loss
[28]. The frequency of drug-induced AEs is difficult to predict. Pre­
dicting AEs in drug discovery is more difficult than achieving appro­
priate treatment efficacy; consequently, progress in the development ofapproaches to avoid and treat drug-induced AEs is lacking.
Drug repositioning enables the development of new therapeutic
agents for rare and intractable diseases. The medical information data­base searched in this study contains extensive clinical data on approved
drugs; therefore, it can be used to identify new indications for approved
drugs, anticipate AEs and their occurrence rate, and predict drug safety
and efficacy.
The AE report database analysis revealed that statins including
simvastatin tended to suppress OIPN. A retrospective observational
study using medical chart data suggested that the occurrence of pe­ripheral neuropathy may be low in patients treated with statins, sug­gesting that this may be a class effect of statins. However, more detailed
studies on the inhibitory effect of statins on OIPN are needed, including
randomized controlled trials. OIPN is a dose-limiting factor that in­
terferes with treatment, but there are no effective preventive drugs [29].
In 2020, the American Society of Clinical Oncology published guidelines
on the management of drug-induced neurotoxicity, but made no rec­ommendations for preventive drugs [30]. Statins are widely used, andFunding
This work was supported by the Japan Research Foundation for
Clinical Pharmacology Grant [Grant no. 2018A10]; Japan Society for
the Promotion of Science Grant-in-Aid for Transformative Research
Areas (B) [Grant no.: 20H05798]; Grants-in-Aid for Scientific Research[Grant no.: 18K06785]; and Platform Project for Supporting Drug Dis­
covery and Life Science Research (Basis for Supporting Innovative Drug
Discovery and Life Science Research (BINDS)) from AMED [Grant no.:JP18am0101085]. The authors also received crowdfunding support
launched by Otsucle (Organization for People with Universities) that
aimed to aid the development of preventive drugs against the adverse
effects of anticancer agents (Tokushima, Japan).
CRediT authorship contribution statement
Yoshito Zamami: Conceptualization, Investigation, Writing – orig­
inal draft, Funding acquisition. Takahiro Niimura: Investigation,
Formal analysis, Writing – review & editing. Takehiro Kawashiri:
Investigation, Writing – review & editing. Mitsuhiro Goda: Investiga­
tion, Formal analysis, Writing – review & editing. Yutaro Naito:
Investigation, Formal analysis, Writing – review & editing. Keijo
Fukushima: Investigation, Writing – review & editing. Soichiro Ushio:7

Y. Zamami et al.

Biomedicine & Pharmacotherapy 148 (2022) 112744

Writing – review & editing. Fuka Aizawa: Investigation, Writing – re­view & editing. Hirofumi Hamano: Formal analysis, Writing – review &editing. Naoto Okada: Writing – review & editing. Kenta Yagi: Writing– review & editing. Kenshi Takechi: Writing – review & editing.
Masayuki Chuma: Writing – review & editing. Toshihiro Koyama:
Investigation. Daisuke Kobayashi: Writing – review & editing. Takao
Shimazoe: Writing – review & editing. Hiromichi Fujino: Writing –review & editing. Yuki Izawa-Ishizawa: Writing – review & editing.
Keisuke Ishizawa: Conceptualization, Supervision, Writing – review &editing.

[9] Y. Horinouchi, Y. Ikeda, K. Fukushima, M. Imanishi, H. Hamano, Y. IzawaIshizawa, Y. Zamami, K. Takechi, L. Miyamoto, H. Fujino, K. Ishizawa,
Renoprotective effects of a factor Xa inhibitor: fusion of basic research and a
database analysis, Sci. Rep. 8 (2018) 10858.
[10] N. Hashikawa, Y. Utaka, T. Ogawa, R. Tanoue, Y. Morita, S. Yamamoto,S. Yamaguchi, M. Kayano, Y. Zamami, N. Hashikawa-Hobara N, HSP105 prevents
depression-like behavior by increasing hippocampal brain-derived neurotrophic
factor levels in mice, Sci. Adv. 3 (2017), e1603014.
[11] U.D. Vempati, C. Chung, C. Mader, A. Koleti, N. Datar, D. Vidović, D. Wrobel,
S. Erickson, J.L. Muhlich, G. Berriz, C.H. Benes, Metadata standard and dataexchange specifications to describe, model, and integrate complex and diverse
high-throughput screening data from the library of integrated network-based
cellular signatures (LINCS), J. Biomol. Screen. 19 (2014) 803–816.
[12] J.T. Dudley, M. Sirota, M. Shenoy, R.K. Pai, S. Roedder, A.P. Chiang, A.A. Morgan,
M.M. Sarwal, P.J. Pasricha, A.J. Butte, Computational repositioning of the
anticonvulsant topiramate for inflammatory bowel disease, Sci. Transl. Med. 3(2011) 96ra76.
[13] L.M. Pasetto, M.R. D’Andrea, A.A. Brandes, E. Rossi, S. Monfardini, The
development of platinum compounds and their possible combination, Crit. Rev.
Oncol. Hematol. 60 (2006) 59–75.
[14] S. Ushio, N. Egashira, H. Sada, T. Kawashiri, M. Shirahama, K. Masuguchi, R. Oishi,
Goshajinkigan reduces oxaliplatin-induced peripheral neuropathy without
affecting anti-tumour efficacy in rodents, Eur. J. Cancer 48 (2012) 1407–1413.
[15] H.S. Hochster, A. Grothey, L. Hart, K. Rowland, R. Ansari, S. Alberts, N. Chowhan,
R.K. Ramanathan, M. Keaton, J.D. Hainsworth, B.H. Childs, Improved time totreatment failure with an intermittent oxaliplatin strategy: results of CONcePT,Ann. Oncol. 25 (2014) 1172–1178.
[16] E.P. van Puijenbroek, A. Bate, H.G.M. Leufkens, M. Lindquist, R. Orre, A.C.
G. Egberts, A comparison of measures of disproportionality for signal detection in
spontaneous reporting systems for adverse drug reactions, Pharmacoepidemiol.
Drug Saf. 11 (2002) 3–10.
[17] L.M. Pasetto, M.R. D’Andrea, E. Rossi, S. Monfardini, Oxaliplatin-related
neurotoxicity: how and why? Crit. Rev. Oncol./Hematol. 59 (2006) 159–168.
[18] G.M. Higa, C. Sypult, Molecular biology and clinical mitigation of cancer
treatment-induced neuropathy, Clin. Med. Insights Oncol. 10 (2016) 27–34.
[19] H. Starobova, I. Vetter, Pathophysiology of chemotherapy-induced peripheral
neuropathy, Front. Mol. Neurosci. 10 (2017) 174.
[20] O. Kanat, H. Ertas, B. Caner, Platinum-induced neurotoxicity: A review of possible
mechanisms, World J. Clin. Oncol. 8 (2017) 329–335.
[21] Q. Duan, S.P. Reid, N.R. Clark, Z. Wang, N.F. Fernandez, A.D. Rouillard,
B. Readhead, S.R. Tritsch, R. Hodos, M. Hafner, M. Niepel, L1000CDS2: LINCS
L1000 characteristic direction signatures search engine, NPJ Syst. Biol. Appl. 2(2016) 16015.
[22] A. Subramanian, R. Narayan, S.M. Corsello, D.D. Peck, T.E. Natoli, X. Lu, J. Gould,
J.F. Davis, A.A. Tubelli, J.K. Asiedu, D.L. Lahr, A next generation connectivity map:
L1000 platform and the first 1,000,000 profiles, Cell 171 (2017) 1437–1452, e17.
[23] T. Kawashiri, N. Egashira, H. Watanabe, Y. Ikegami, S. Hirakawa, Y. Mihara,
T. Yano, H. Ikesue, R. Oishi, Prevention of oxaliplatin-induced mechanicalallodynia and neurodegeneration by neurotropin in the rat model, Eur. J. Pain 15(2011) 344–350.
[24] American Type Culture Collection, PC-12 (ATCC® CRL-1721™). 〈https://www.at
cc.org/products/all/CRL-1721.aspx#culturemethod〉. (Accessed 1 March 2020).
[25] H. Hamano, T. Niimura, Y. Horinouchi, Y. Zamami, K. Takechi, M. Goda,
M. Imanishi, M. Chuma, Y. Izawa-Ishizawa, L. Miyamoto, K. Fukushima, Toxicol.
Lett. 318 (2020) 86–91.
[26] K. Chamoto, P.S. Chowdhury, A. Kumar, K. Sonomura, F. Matsuda, S. Fagarasan,
T. Honjo, Mitochondrial activation chemicals synergize with surface receptor PD-1
blockade for T cell-dependent antitumor activity, Proc. Natl. Acad. Sci. USA 114(2017) E761–E770.
[27] S.M. Amos, C.P.M. Duong, J.A. Westwood, D.S. Ritchie, R.P. Junghans, P.K. Darcy,
M.H. Kershaw, Autoimmunity associated with immunotherapy of cancer, Blood
118 (2011) 499–509.
[28] D.C. Classen, S.L. Pestotnik, R.S. Evans, J.F. Lloyd, J.P. Burke, Adverse drug eventsin hospitalized patients. Excess length of stay, extra costs, and attributablemortality, JAMA 277 (1997) 301–306.
[29] A. Chiorazzi, S. Semperboni, P. Marmiroli, Current view in platinum drug
mechanisms of peripheral neurotoxicity, Toxics 3 (2015) 304–321.
[30] C.L. Loprinzi, C. Lacchetti, J. Bleeker, G. Cavaletti, C. Chauhan, D.L. Hertz, M.
R. Kelley, A. Lavino, M.B. Lustberg, J.A. Paice, B.P. Schneider, Prevention andmanagement of chemotherapy-induced peripheral neuropathy in survivors of adultcancers: ASCO guideline update, J. Clin. Oncol. 38 (2020) 3325–3348.
[31] E. De Mattia, E. Cecchin, G. Toffoli, Pharmacogenomics of intrinsic and acquired
pharmacoresistance in colorectal cancer: toward targeted personalized therapy,
Drug Resist Updat. 20 (2015) 39–70.

Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data and material availability
The data and code that support the findings of this study are avail­able from the corresponding authors upon request.
Acknowledgments
We thank S. Ishida, Ph.D., (Tokushima University Hospital) for
providing technical assistance; Y. Kanda, Ph.D., (National Institute of
Health Sciences) and T. Sakurada, Ph.D., (Tokushima University Hos­pital) for the critical review of the draft; H. Yanagawa, Ph.D.,(Tokushima University Hospital) and K. Tsuchiya, PhD, (Tokushima
University) for providing critical advice for this study; and H. Sakoga­
shira, BS, (Tokushima University), H. Kajimoto, (Tokushima Univer­sity), and M. Nozaki, (Tokushima University) for supporting theexperiment.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.biopha.2022.112744.
References[1] H. Xue, J. Li, H. Xie, Y. Wang, Review of drug repositioning approaches andresources, Int. J. Biol. Sci. 14 (2018) 1232–1244.
[2] T.T. Ashburn, K.B. Thor, Drug repositioning: identifying and developing new uses
for existing drugs, Nat. Rev. Drug Discov. 3 (8) (2004) 673–683.
[3] J. Langedijk, A.K. Mantel-Teeuwisse, D.S. Slijkerman, M.D.B. Schutjens, Drugrepositioning and repurposing: terminology and definitions in literature, Drug
Discov. Today 20 (2015) 1027–1034.
[4] T. Mizushima, Drug re-positioning strategy, J. Jpn. Stom. Soc. 64 (2015) 241–253.
[5] C.H. Bartlett, J. Mardekian, M. Yu-Kite, M.J. Cotter, S. Kim, J. Decembrino,
T. Snow, K.R. Carson, J. Motyl Rockland, A.L. Kraus, K.D. Wilner, Real-world
evidence of male breast cancer (BC) patients treated with palbociclib (PAL) in
combination with endocrine therapy (ET), J. Clin. Oncol. 37 (2019) 1055.
[6] Q. Hu, F. Zhang, W. Teng, F. Hao, J. Zhang, M. Yin, N. Wang, Efficacy and safety ofantiepileptic drugs for refractory partial-onset epilepsy: a network meta-analysis,
J. Neurol. 265 (2018) 1–11.
[7] S. Zhao, T. Nishimura, Y. Chen, E.U. Azeloglu, O. Gottesman, C. Giannarelli, M.
U. Zafar, L. Benard, J.J. Badimon, R.J. Hajjar, J. Goldfarb, Systems pharmacologyof adverse event mitigation by drug combinations, Sci. Trans. Med. 5 (2013)
206ra140.
[8] Y. Izawa-Ishizawa, M. Imanishi, Y. Zamami, H. Toya, T. Nagao, M. Morishita,
K. Tsuneyama, Y. Horinouchi, Y. Kihira, K. Takechi, Y. Ikeda, Development of anovel aortic dissection mouse model and evaluation of drug efficacy using in-vivo
assays and database analyses, J. Hypertens. 37 (2019) 73–83.

8