Next Article in Journal
Hypophosphatemia as a Potential Class Effect of Histone Deacetylase Inhibitors: Evidence from Disproportionality Analysis and Mendelian Randomization Analysis of Drug Targets
Previous Article in Journal
Advances in Strategies to Transport Nanoparticles Across the Blood–Brain Barrier for Drug Delivery into the Brain for the Treatment of Alzheimer’s Disease
Previous Article in Special Issue
Cardiac Safety of Intranasal Chlorpheniramine: An Exposure-Based Risk Assessment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 19
Issue 5
10.3390/ph19050688
pharmaceuticals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Disproportionality Analysis and Timing of Drug-Associated Guillain–Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database

by
Shinya Toriumi
1,2,*,
Yousuke Kurihara
2,
Komei Shimokawa
2,
Arihito Tanaka
3,
Norito Araki
3,
Osamu Kawai
3,
Yasoo Sugiura
4 and
Yoshihiro Uesawa
1,*
1
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Tokyo, Japan
2
Department of Pharmacy, National Hospital Organization Kanagawa Hospital, Hadano 257-8585, Kanagawa, Japan
3
Department of Respiratory Medicine, National Hospital Organization Kanagawa Hospital, Hadano 257-8585, Kanagawa, Japan
4
Department of General Thoracic Surgery, National Hospital Organization Saitama Hospital, Wako 351-0102, Saitama, Japan
*
Authors to whom correspondence should be addressed.
Pharmaceuticals 2026, 19(5), 688; https://doi.org/10.3390/ph19050688
Submission received: 2 March 2026 / Revised: 19 April 2026 / Accepted: 23 April 2026 / Published: 28 April 2026
(This article belongs to the Special Issue Pharmacovigilance Insights: Addressing Drug Misuse and Enhancing Drug Safety)
Browse Figures
Versions Notes

Abstract

Background: Guillain–Barré syndrome (GBS) is an autoimmune peripheral neuropathy that can lead to paralysis and respiratory failure. In addition to infections, several drugs have been suggested as potential triggers of GBS. This study investigated drug-associated GBS using a spontaneous adverse event reporting database through disproportionality analysis for signal detection and time-to-onset analysis. Methods: The Japanese Adverse Drug Event Report (JADER) database was analyzed to assess more than 4000 drugs for potential associations with GBS. Signal detection was performed using reporting odds ratios, Fisher’s exact test, and total report counts. For vaccines and immune checkpoint inhibitors, time-to-onset patterns were further evaluated using Weibull distribution analysis. Results: Disproportionality signals suggesting potential associations with GBS were identified for 45 drugs, including vaccines, immune checkpoint inhibitors, tumor necrosis factor-α inhibitors, other anticancer drugs, antifungal agents, and interferons. Reports following vaccination were most frequently observed within 1–3 weeks after administration of coronavirus disease 2019 (COVID-19), influenza, and pneumococcal vaccines, and within 1–3 months after human papillomavirus 2-valent vaccination, with a gradual decrease thereafter. Reports following immune checkpoint inhibitor use were most frequently observed 1–3 months after nivolumab, ipilimumab, and pembrolizumab administration, whereas atezolizumab showed a peak in reporting within 1–3 weeks. In contrast to vaccine-related reports, no clear temporal trend in reporting was observed. Conclusions: Drugs that modulate immune function, including vaccines and immune checkpoint inhibitors, may be associated with reported GBS events. Vaccine-related reports showed an early concentration in time to onset, whereas immune checkpoint inhibitor-related reports did not demonstrate a clear temporal pattern. These findings should be interpreted as hypothesis-generating and warrant further investigation.

Graphical Abstract

1. Introduction

Guillain–Barré syndrome (GBS) is an autoimmune peripheral neuropathy that develops after infections, such as diarrhea or the common cold [1]. GBS progresses over a period of days to weeks, with severe cases leading to paralysis and respiratory failure [2,3]. The pathogenesis of GBS has been attributed to cross-reactive antibodies induced by infection that target components of the neuronal membrane, resulting in demyelination [4,5]. The incidence of GBS is 1.12 cases per 100,000 people per year, although it varies by region and shows a slight male predominance [6]. Although treatments for GBS include intravenous immunoglobulin and plasma exchange, early diagnosis and treatment are crucial [7]. GBS is often triggered by infection; however, reports of drug-associated GBS, including reports following vaccination [8] and immune checkpoint inhibitor therapy [9], have also been described. Because drug-associated GBS is rare, data to support pharmaceutical management and adverse event monitoring remain limited.
Spontaneous reporting systems, which collect post-marketing adverse event reports from patients, healthcare professionals, pharmaceutical companies, and other sources, play an important role in epidemiological research and drug safety assessment, despite inherent limitations in establishing causality and various reporting biases [10,11,12]. These systems cover large populations, including children, older adults, and patients with renal or hepatic impairment, and they contain large numbers of reports that reflect prescribing patterns and real-world conditions of use. Accordingly, spontaneous adverse event reporting databases have been widely used to detect signals of potential associations between various drugs and adverse events [13,14,15]. The Japanese Adverse Drug Event Report (JADER) database, established by the Pharmaceuticals and Medical Devices Agency (PMDA), is a spontaneous reporting database containing approximately 1,500,000 adverse drug reaction records [16]. In spontaneous reporting system databases, such as JADER, disproportionality-based methods, including reporting odds ratio (ROR) analysis, can be used to explore potential drug–event associations in an exploratory, hypothesis-generating manner. Because JADER includes information on the interval between drug administration and adverse event onset, temporal analysis is also possible [17]. JADER-based evaluations can therefore provide useful information for adverse event management and drug safety.
The present study aimed to detect disproportionality signals of potential associations between drugs and GBS and to evaluate the time to onset and related reporting characteristics.

2. Results

2.1. JADER Analysis Dataset

The JADER dataset used in this study contained 4,707,221 records in the Drug table, 1,568,295 records in the Reaction table, and 949,124 records in the Demographic table (Figure 1). After merging these three tables, the analysis dataset consisted of 2,794,953 records, among which 1873 reports (0.067%) involved drug-associated GBS.

2.2. Drugs Showing Disproportionality Signals for GBS and Patient Characteristics

Among more than 4000 drugs that could be analyzed in JADER, 45 drugs were identified as potentially associated with GBS (Supplementary Table S1). By therapeutic class, these agents included 19 vaccines, 5 immune checkpoint inhibitors, 4 tumor necrosis factor α (TNF-α) inhibitors, 6 antiviral drugs, 3 anticancer drugs other than immune checkpoint inhibitors, 2 antifungal drugs, and 2 interferons. The breakdown of reported cases by drug category was as follows: vaccines, 927 reports (49.5%); immune checkpoint inhibitors, 268 reports (14.3%); TNF-α inhibitors, 54 reports (2.9%); antiviral drugs, 22 reports (1.2%); anticancer drugs other than immune checkpoint inhibitors, 17 reports (0.9%); antifungal drugs, 17 reports (0.9%); and interferons, 15 reports (0.8%). The disproportionality results for drugs and GBS are plotted in Figure 2.
The number of reports by vaccine type totaled 354 for coronavirus disease 2019 (COVID-19) vaccines, 98 for COVID-19 vaccines *, 250 for influenza HA vaccines, 23 for influenza HA vaccines (A/H1N1), 56 for human papillomavirus 2-valent vaccines, 19 for human papillomavirus 4-valent vaccines, 50 for pneumococcal vaccines, 14 for hepatitis B vaccines, 10 for Japanese encephalitis vaccines, nine for zoster vaccines, eight for measles-rubella combined vaccines, seven for mumps vaccines, six for diphtheria-tetanus combined toxoid, and six for tetanus toxoid. The JADER dataset did not include detailed information on COVID-19 vaccine types or influenza vaccine strains. An asterisk (*) was used only for COVID-19 vaccines when reports shared the same generic name but could not be distinguished as different medicinal products. No reports of GBS were identified for respiratory syncytial virus (RSV) vaccines in this study.
The number of reports by immune checkpoint inhibitor was as follows: 91 for nivolumab, 76 for pembrolizumab, 54 for ipilimumab, 43 for atezolizumab, and 4 for avelumab. Durvalumab, cemiplimab, and tremelimumab did not show disproportionality signals for drug-associated GBS.
The numbers of reports for TNF-α inhibitors were 26 for infliximab, 14 for adalimumab, 11 for etanercept, and 3 for infliximab (biosimilar 1).
The numbers of reports for antiviral drugs were 7 for lamivudine, 4 for lopinavir/ritonavir, 3 for ombitasvir/paritaprevir/ritonavir, 3 for stavudine, 3 for abacavir, and 2 for bictegravir/emtricitabine/tenofovir alafenamide fumarate.
Other drugs showing disproportionality signals for GBS included the anticancer drugs gilteritinib, nelarabine, and forodesine; the antifungal agents voriconazole and posaconazole; and the interferon preparations pegylated interferon alpha-2a and interferon beta-1b.
The characteristics of patients in reports of drug-associated GBS are presented in Table 1. Notably, reports of GBS associated with human papillomavirus 2-valent and 4-valent vaccines and the mumps vaccine were exclusively in female patients and were concentrated in younger individuals. In contrast, reports associated with tetanus toxoid involved only male patients, whereas most other drugs and vaccines were reported predominantly in adults.

2.3. Time-to-Event Analysis and Weibull Distribution

The time to onset of drug-associated GBS for vaccines and immune checkpoint inhibitors, together with the corresponding Weibull parameters, is presented in Table 2 and Figure 3. For vaccines, the median time to GBS onset (range) was 8.5 days (0.5–225.5) for COVID-19 vaccines, 10.5 days (0.5–347.5) for COVID-19 vaccines *, 10.5 days (0.5–212.5) for influenza HA vaccines, 10.5 days (1.5–51.5) for influenza HA vaccines (A/H1N1 strain), 4.5 days (0.5–212.5) for pneumococcal vaccines, and 32.5 days (1.5–352.5) for human papillomavirus 2-valent vaccines (Table 2). For COVID-19 vaccines, COVID-19 * vaccines, influenza HA vaccines, and pneumococcal vaccines, the Weibull shape parameter (β) indicated an early-failure pattern (β < 1, with the upper bound of the 95% confidence interval < 1), suggesting a higher reporting frequency early after administration followed by a decline over time (Table 2). A significant difference in time to onset was observed among vaccines (p < 0.001; Figure 3A).
For the immune checkpoint inhibitors nivolumab, ipilimumab, pembrolizumab, and atezolizumab, the median times to GBS onset (range) were 57.5 days (0.5–359.5), 45.5 days (5.5–169.5), 63.5 days (0.5–314.5), and 19.5 days (4.5–147.5), respectively. For many immune checkpoint inhibitors, the Weibull shape parameter (β) approximated a random-failure pattern (approximately β = 1), suggesting that the reporting frequency did not show a clear temporal change (Table 2). A significant difference in time to GBS onset was observed among immune checkpoint inhibitors (p = 0.016; Figure 3B).
As presented in Table 3, the median (range) time to GBS onset for COVID-19 vaccines * was 16.5 days (1.5–287.5) for men and 4.5 days (0.5–347.5) for women (p = 0.005). For influenza HA vaccines (A/H1N1 strain), the median time to GBS onset was significantly longer in men than in women (14.5 days [2.5–51.5] vs. 5.5 days [1.5–16.5], p = 0.032). No significant differences were observed for the other vaccines or immune checkpoint inhibitors. As shown in Table 4, no significant age-related differences in time to GBS onset were observed for vaccines, and immune checkpoint inhibitors were reported only in patients aged 20 years or older.

3. Discussion

3.1. Drugs Showing Disproportionality Signals for GBS

Vaccines effectively prevent infectious diseases because pathogen-derived antigens are presented by antigen-presenting cells, which activate helper T cells and stimulate B cells, thereby inducing antibody production and immunological memory, enabling a rapid and powerful immune response in the event of reinfection [18]. Several vaccines have been reported to be associated with a risk of GBS, and this study also suggested possible associations. In this study, COVID-19 and influenza vaccines accounted for the majority of reports of vaccine-associated GBS.
This study identified disproportionality signals for GBS associated with “COVID-19 vaccine” and “COVID-19 vaccine *” (Figure 2). A meta-analysis reported an increased risk of GBS after adenovirus-vector COVID-19 vaccination [19]. Patone et al. reported a twofold increased risk of GBS within 28 days after adenovirus-vector COVID-19 vaccination (IRR = 2.04), whereas mRNA-based vaccines were not associated with an increased risk [20]. By contrast, SARS-CoV-2 infection itself increased the risk of GBS approximately fivefold (IRR = 5.25) [20]. Our findings are broadly consistent with previous reports, although direct comparisons among COVID-19 vaccine types were not possible because JADER does not provide sufficiently detailed product information.
This study identified disproportionality signals for GBS associated with “Influenza HA vaccine” and “Influenza HA vaccine (A/H1N1)” (Figure 2). Influenza vaccination has been reported to carry a relative risk of 1.41 for GBS [21]. The 1976 swine influenza vaccine was thought to induce anti-ganglioside antibodies, potentially contributing to an increased risk of GBS [22]. Our findings are broadly consistent with previous reports. However, comparisons among influenza vaccine products or strains were not possible because those details were unavailable in JADER.
The present study also identified potential disproportionality signals for GBS associated with vaccines other than COVID-19 and influenza vaccines (Figure 2). Reports of GBS associated with pneumococcal vaccine [23], human papillomavirus vaccine [24], Japanese encephalitis vaccine [25], zoster vaccine [26], measles-rubella combined vaccine [27], and diphtheria-tetanus combined toxoid vaccines [28] have been described previously, and signals were also identified for these vaccines in the present study. Using U.S. Vaccine Adverse Event Reporting System data (1990–2005), Souayah et al. identified few GBS reports across vaccines and no disproportionality signals [29]. Recent real-world evidence has suggested a possible association between RSV vaccines and GBS in older adults [30]. However, no reports of GBS associated with RSV vaccines were identified in this study. Epidemiological evidence regarding the risk of GBS associated with vaccines other than COVID-19 and influenza vaccines remains limited; therefore, further investigation is warranted [31].
In the present study, disproportionality signals for GBS were identified for the immune checkpoint inhibitors nivolumab, pembrolizumab, ipilimumab, atezolizumab, and avelumab (Figure 2). Immune checkpoint inhibitors exert their antitumor effects by inhibiting the cytotoxic T lymphocyte-associated protein 4 (CTLA-4) pathway, which suppresses the initial stage of T-cell activation, and the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway, which tumor cells use for immune evasion [32,33]. In addition to their expression in malignant tumors, PD-1/PD-L1 receptors are also expressed in activated immune cells and peripheral tissues [34]. Immune checkpoint inhibitors are therefore known to cause immune-related adverse events (irAEs), including T-cell- and antibody-mediated GBS [35,36]. The incidence of serious neurological adverse events associated with immune checkpoint inhibitors has been reported to be lower than 1% [37]. Nivolumab [38], pembrolizumab [39], ipilimumab [40], atezolizumab [41], and avelumab [42] have been reported to be potentially associated with the risk of GBS. In contrast, no disproportionality signals for GBS were detected for some immune checkpoint inhibitors, including the PD-L1 inhibitor durvalumab and the CTLA-4 inhibitor tremelimumab. A study based on the World Health Organization (WHO) VigiBase reported that no disproportionality signals for GBS were detected in association with PD-L1 inhibitors; however, a potential signal could not be excluded [43]. Like PD-1 inhibitors, PD-L1 inhibitors inhibit the binding of PD-L1 to PD-1, which reduces inhibitory signals to T cells, thereby promoting the activation of tumor-specific T cells and suppressing tumor growth [44]. As the mechanisms of irAEs are believed to be similar between PD-1 and PD-L1 inhibitors, future research is needed to determine differences in adverse-event profiles among PD-1, PD-L1, and CTLA-4 inhibitors [45].
In the present study, disproportionality signals for GBS were identified for the TNF-α inhibitors infliximab, adalimumab, etanercept, and infliximab (biosimilar 1) (Figure 2). TNF-α inhibitors are widely used to treat autoimmune diseases, such as rheumatoid arthritis and inflammatory bowel disease [46]. Adverse events reported in association with TNF-α inhibitors include demyelination [47] and forms of peripheral neuropathy, including GBS [48]. Adalimumab [49], infliximab [50], and etanercept [51] have been reported to be potentially associated with the risk of GBS. Hypothesized mechanisms include immune dysfunction attributable to multifunctional TNF-α inhibition [52] and autoantibody induction [53]; however, these mechanisms have not been fully elucidated [54]. The present study also identified a disproportionality signal for GBS associated with an infliximab biosimilar. Although there are insufficient data on the risk of GBS associated with TNF-α inhibitor biosimilars, the present findings suggest that, if the reference product carries a risk of GBS, similar precautions may also be warranted for its biosimilars.
In this study, we identified disproportionality signals suggesting a potential association between six antiviral drugs and GBS (Figure 2). Although the Japanese package inserts for antiviral drugs include a warning about GBS, no specific reports have directly linked these drugs to GBS. Restoration of immune function following antiviral therapy may be associated with the development of GBS; however, this may reflect an autoimmune response triggered by the virus itself or treatment-related immune reconstitution inflammatory syndrome rather than a direct effect of the drugs [55,56].
This study identified disproportionality signals for GBS associated with anticancer drugs other than immune checkpoint inhibitors, as well as antifungal drugs and interferon preparations (Figure 2). Although there have been reports of associations between nelarabine [57], voriconazole [58], posaconazole [59], and peginterferon alfa-2a [60] and GBS, further investigation is needed because the available evidence remains limited.
Disproportionality analysis is based on the number of spontaneously reported adverse event cases and does not account for detailed clinical information, such as disease course, risk factors, or alternative causes. For example, Guillain–Barré syndrome is often triggered by viral infections, which may confound the associations observed in spontaneous reporting databases. Therefore, the signals identified in this study should be interpreted as hypothesis-generating rather than as evidence of a causal relationship.

3.2. Timing of Vaccine- and Immune Checkpoint Inhibitor-Associated GBS Onset

In this study, the most common time to onset for vaccine-associated GBS was 1–3 weeks for COVID-19, influenza, and pneumococcal vaccines and 1–3 months for the human papillomavirus 2-valent vaccine, although the reporting frequency tended to decrease thereafter (Table 2, Figure 3A). A systematic review of GBS cases associated with COVID-19 vaccines reported that more than 80% of cases occurred within 3 weeks of vaccination [61]. An analysis using the U.S. Vaccine Adverse Event Reporting System reported that the median time to GBS onset following COVID-19 vaccination was 10 days, the median patient age was 55 years, and half of the patients were male [62]. In this study, the type of COVID-19 vaccine administered was unknown, and there was a slight female predominance. However, the median time to GBS onset was 8.5 days for the COVID-19 vaccine and 10.5 days for the COVID-19 vaccine *, consistent with previous reports (Table 2). Regarding the relationship between influenza vaccination and GBS, a prior report found that GBS cases were concentrated within 5 weeks of vaccination [63]. Analyses of seasonal influenza vaccines identified a peak incidence rate 2–4 weeks after vaccination [64,65], consistent with the median time to onset of 10.5 days for influenza vaccines in the present study (Table 2). Furthermore, women tended to develop symptoms earlier than men after COVID-19 and influenza vaccination (Table 3). A previous report suggested that women may develop earlier and more severe clinical symptoms of GBS than men after COVID-19 vaccination [66]. There have also been reports of GBS occurring within 2–4 weeks after pneumococcal vaccine administration [67,68], and a similar pattern was suggested in the present study (Table 2, Figure 3A). However, GBS most commonly emerged 1–3 months after human papillomavirus 2-valent vaccine administration in the present study, representing a later onset than that observed for other vaccines (Table 2, Figure 3A). Previous studies did not identify a link between human papillomavirus vaccination and GBS [69]. Because the human papillomavirus vaccine is intended primarily for the prevention of cervical cancer, its target population is young women. In this study, most patients with human papillomavirus vaccine-associated GBS were girls in their teens, which differed from the patient backgrounds observed for other vaccines (Table 3 and Table 4). This difference in time to onset likely reflects the target population of the vaccine and should be considered when interpreting the observed associations. Children have immature immune systems, including immature regulatory T cells and Th17 cells, and myelination is incomplete; the impact of demyelination may therefore differ from that in adults [70]. The incidence of GBS in children (0.62 cases/100,000 person-years) is lower than that in adults (2.66 cases/100,000 person-years) [71]. In addition, women appear to have a lower risk of developing GBS than men [72]. The human papillomavirus vaccine is also administered in multiple doses over a defined period. These differences could explain the observed later onset of GBS following human papillomavirus vaccination in this study.
In this study, the most common time to onset of drug-associated GBS was 1–3 months for nivolumab, ipilimumab, and pembrolizumab and 2–4 weeks for atezolizumab (Table 2, Figure 3B). Repeated administration of immune checkpoint inhibitors leads to continuous inhibition of the PD-1/PD-L1 and CTLA-4 pathways, which is believed to promote the expansion of autoreactive T cells and the production of anti-neuronal antibodies, resulting in an autoimmune response targeting peripheral nerves [73,74]. Previous reports have indicated that GBS associated with immune checkpoint inhibitors occurs approximately 5.4 [75] and 8.2 weeks [76] after treatment initiation, in line with the current findings for the PD-1 inhibitors nivolumab and pembrolizumab and the CTLA-4 inhibitor ipilimumab. In contrast, the time to GBS onset for the PD-L1 inhibitor atezolizumab was earlier in the present study, at 19.5 days (Table 2, Figure 3B). There are no published studies comparing the timing of drug-associated GBS onset among immune checkpoint inhibitors. However, PD-L1 inhibitors, such as atezolizumab, cause fewer irAEs than PD-1 inhibitors [77,78]. Although both PD-L1 and PD-1 inhibitors block the PD-1/PD-L1 pathway, PD-L1 inhibitors block the B7-1/PD-L1 pathway while sparing the PD-1/PD-L2 pathway, unlike PD-1 inhibitors [79,80,81]. The findings of this study suggest possible differences in the mechanisms underlying GBS associated with PD-1 and PD-L1 inhibitors, potentially involving the PD-1/PD-L2 or B7-1/PD-L1 pathways; however, these observations are hypothesis-generating. The impact of these pathways on immune function in drug-associated GBS warrants further investigation.

3.3. Limitations

Spontaneous adverse event reporting databases, such as JADER, are subject to inherent biases and limitations that should be considered when interpreting the results.
First, reporting bias may occur due to increased awareness of GBS following regulatory warnings or published reports, potentially leading to stimulated reporting for specific drugs. In addition, such databases rely on voluntary reports, and the total number of exposed patients is unknown, making direct risk estimation impossible [82]. Serious and well-recognized adverse events tend to be overreported, whereas mild or unknown events may be underreported [83,84]. When multiple drugs are administered, it is often difficult to identify the causative agent [85]. Furthermore, data quality issues should be considered, as JADER contains missing values and potential data entry errors; therefore, data cleaning was performed as thoroughly as possible. However, detailed analyses were limited by the lack of information on COVID-19 vaccine types and influenza vaccine strains.
Second, because this study employed disproportionality analysis for signal detection, the findings should be interpreted as exploratory, and false-positive signals cannot be excluded [86]. Disproportionality analysis is based on the number of reports rather than detailed clinical content of individual cases, and therefore does not allow for direct risk estimation or causal inference. In the signal evaluation of this study, signals were assessed using a combination of the ROR, Fisher’s exact test p-value, and a predefined threshold for the number of reports; however, because direct risk estimation and causal inference were not possible, careful interpretation is warranted.
Third, misclassification may have occurred because GBS diagnoses were not clinically validated at the individual case level and were based on reported terms. In addition, the use of standardized MedDRA queries (SMQs) may have introduced heterogeneity in case identification. The SMQ for GBS includes a broad range of Preferred Terms, some of which may represent conditions not directly related to drug exposure, such as autoimmune disorders (e.g., Bickerstaff’s encephalitis) or infection-associated syndromes (e.g., Zika virus-associated GBS). Furthermore, confounding factors, such as underlying diseases, concomitant medications, and infections (e.g., preceding viral or bacterial infections known to trigger GBS), could not be fully accounted for in this analysis. These factors may have influenced the observed disproportionality signals.
Fourth, the time-to-onset analysis may be subject to reporting bias, particularly for events with longer latency [87,88]. Reporting patterns may differ between products with well-established associations with GBS and those without, potentially affecting the observed distributions [89]. In addition, the Weibull distribution analysis should be interpreted with caution, as it is based on spontaneous reporting data and may be influenced by incomplete or biased reporting [87,89].
Finally, disproportionality analysis is based on the number of reports rather than detailed clinical content of individual cases. Because this study did not include a detailed clinical evaluation of individual cases, it was not possible to assess temporal relationships, alternative etiologies, or the strength of causality between drugs and GBS. Therefore, the identified signals should be interpreted with caution and further evaluated through detailed clinical review and well-designed epidemiological studies to validate these findings.

4. Materials and Methods

4.1. Detection of Disproportionality Signals for GBS

4.1.1. Construction of the JADER Analysis Data Table

This study analyzed data registered in the JADER database from April 2004 to February 2025 [16]. The JADER Drug (e.g., drug name, drug involvement, start date, end date), Reaction (e.g., adverse events, onset date), and Demographic tables (e.g., basic patient information, such as gender, age, weight) were used in this study. Drugs in the Drug table were assigned to three categories based on their involvement in adverse events: suspected drugs, concomitant drugs, and drug interactions. Only the suspected drug data were used in this study. Furthermore, each drug was assigned an Anatomical Therapeutic Chemical Classification System (ATC) code for drug classification [90]. Adverse events in the Reaction table were based on the ICH International Medical Dictionary for Regulatory Activities (MedDRA)/Japanese version 27.1 [91]. Adverse events in the Reaction table can be grouped into specific medical conditions using SMQs [92]. The SMQ was applied as a broad case-finding tool for signal detection, allowing for the identification of potentially relevant cases. In this study, drug-associated GBS was defined using the nine GBS and related disease preferred terms within GBS (SMQ code: 20000131, narrow scope): acute motor axonal neuropathy, acute motor–sensory axonal neuropathy, Bickerstaff’s encephalitis, chronic inflammatory demyelinating polyradiculoneuropathy, demyelinating polyneuropathy, GBS, Miller Fisher syndrome, subacute inflammatory demyelinating polyneuropathy, and Zika virus-associated GBS. Not all Preferred Terms included in the SMQ necessarily represent confirmed GBS, and the identified cases may include heterogeneous conditions, such as autoimmune or infection-related disorders. In this study, the term “drug-associated GBS” refers to GBS cases identified from spontaneous reports and does not imply a causal relationship between drugs and GBS. A column was added to the Reaction table to indicate the presence or absence of drug-associated GBS. Overlapping cases between the Drug and Reaction tables were eliminated using the method reported by Hirooka et al. [93,94]. In the Demographic table, age was treated as a continuous variable. Specifically, ages were converted to 105 years for those in their 100s, 95 years for those in their 90s, 85 years for those in their 80s, 75 years for those in their 70s, 65 years for those in their 60s, 55 years for those in their 50s, 45 years for those in their 40s, 35 years for those in their 30s, 25 years for those in their 20s, 15 years for those in their 10s, and 5 years for those under 10. The three tables were joined using identification numbers (IDs) to create a data table for JADER analysis (Figure 1).

4.1.2. Drugs Showing Disproportionate Reporting of Drug-Associated GBS and Patient Characteristics

In this study, all drugs available in the JADER analysis data table were evaluated. A 2 × 2 contingency table was constructed for each drug in relation to drug-associated GBS (Figure 4). Using each 2 × 2 contingency table, three indices were calculated and evaluated: the ROR, Fisher’s exact test p-value, and the total number of reports for each drug (Figure 4). The 2 × 2 contingency table was corrected by adding 0.5 to all cells (Haldane–Anscombe 1/2 correction) to avoid instability in estimates when any cell was zero [95,96]. The ROR is an important indicator of signal detection in disproportionality analysis of adverse events and safety in pharmacovigilance [97]. The ROR is widely used in pharmacoepidemiologic studies because of its high sensitivity and low bias [98]. However, traditional signal detection indices, such as the ROR, may overestimate signals and produce unstable statistical estimates when reports are infrequent [99]. To address this issue, the EudraVigilance guidelines recommend a minimum number of reports to ensure a stable signal [100]. To prevent overlooking commonly used drugs, we set the total number of reports for each drug at 100 as a threshold (a + b > 100 in Figure 4) [101]. Furthermore, we used Fisher’s exact test to assess the independence of drugs and GBS in the 2 × 2 contingency table (Figure 4). Therefore, in this study, a drug was considered to show a signal when it met all of the following criteria for disproportionate reporting of drug-associated GBS: ROR > 1, Fisher’s exact test p-value < 0.05, and ≥100 reports [102].
To visually interpret the associations of approximately 4000 drugs with GBS, we created a scatter plot (volcano plot) of the ROR and p-value calculated using Fisher’s exact test. This volcano plot presents lnROR on the x-axis and −log10 (p-value) on the y-axis [103,104]. This scatter plot is equivalent to the volcano plot frequently used in bioinformatics to understand trends in gene expression.
We also investigated the characteristics of patients with reported drug-associated GBS, including gender and age.
Due to the nature of data from spontaneous reporting systems, this study relied on quantitative signal detection using disproportionality analysis without adjustment for confounding factors or clinical validation of cases.

4.2. Onset Timing of Drug-Associated GBS

4.2.1. Construction of a Data Table for Time-to-Onset Analysis

We performed a time-to-event analysis of GBS reports in the JADER database for vaccines and immune checkpoint inhibitors and classified the onset patterns using the Weibull distribution [105,106]. From the Drug table, we used “identification number (ID)”, “drug involvement”, “drug name”, and “administration start date”. Regarding drug involvement, only suspected drugs were used. Administration start dates were converted to 8-digit YYYYMMDD format. Twelve-digit dates were truncated to the first eight digits, 8-digit dates were retained, 6-digit dates were converted by assigning the 15th day of the month, and 4-digit dates were treated as missing. We then extracted records with valid 8-digit administration start dates between 1 January 1960 and 28 February 2025. If a drug ID had multiple administration dates, the earliest was considered the administration start date. The Reaction table was used to extract reports of drug-associated GBS. The onset dates of drug-associated GBS in the Reaction table were cleaned to eight digits (yyyymmdd) using the same procedure applied to the Drug table. If a patient (same ID) had multiple dates of GBS onset, the earliest onset date was used. The Drug and Reaction tables were linked using IDs. The time to onset was calculated as the difference between the onset date and the administration start date after conversion to date format, plus 0.5 days as a continuity correction. Only values between 0.5 and 365.5 days were included in the analysis to reduce the influence of outliers, such as implausible values due to data entry errors or very long intervals for which causal interpretation is difficult, and to mitigate potential reporting bias because time to onset may be reported differently depending on prior knowledge of drug-GBS associations. For drugs administered multiple times, we examined the interval from treatment initiation (first dose) to the date of GBS onset.

4.2.2. Evaluation of Adverse Event Onset Profiles

The timing of onset of drug-associated GBS for vaccines and immune checkpoint inhibitors was evaluated, and the onset patterns were classified using the Weibull distribution [107]. In the Weibull distribution, the scale parameter (α) represents the dispersion of the distribution, and the shape parameter (β) characterizes the failure pattern. A β value < 1 indicates early failure, with a higher hazard shortly after exposure that decreases over time; β = 1 indicates random failure with a constant hazard; and β > 1 indicates wear-out failure, with an increasing hazard over time [108]. In this study, the time to onset of drug-associated GBS was analyzed using the Weibull distribution to evaluate onset patterns.

4.3. Statistical Analysis

All analyses were performed using JMP Pro 18.0.0 (SAS Institute Inc., Cary, NC, USA), and p < 0.05 was considered statistically significant.

5. Conclusions

This study identified disproportionality signals for GBS associated with several drugs and vaccines using the JADER database. Disproportionality signals for drug-associated GBS were detected for vaccines, including COVID-19 and influenza vaccines; immune checkpoint inhibitors, including nivolumab and pembrolizumab; TNF-α inhibitors; antivirals; anticancer drugs other than immune checkpoint inhibitors; antifungal agents; and interferons, suggesting possible involvement of drugs that affect immune function. Regarding time-to-onset patterns, GBS reports were most frequently observed within 1–3 weeks after administration of COVID-19, influenza, and pneumococcal vaccines and within 1–3 months after administration of human papillomavirus 2-valent vaccines, with reporting frequency decreasing thereafter. Among immune checkpoint inhibitors, the most frequently reported time to onset was 1–3 months for nivolumab, ipilimumab, and pembrolizumab and 1–3 weeks for atezolizumab; however, no clear temporal trend in reporting was observed. The signals identified in this study through disproportionality analysis should be interpreted cautiously, as they may reflect reporting biases and other limitations inherent in spontaneous reporting systems and do not constitute evidence of causal relationships. These findings are primarily intended for hypothesis generation and early signal detection. Further pharmacoepidemiological and clinical studies are required to validate these findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph19050688/s1, Table S1: Drugs associated with disproportionality signals for Guillain–Barré syndrome (GBS).

Author Contributions

Conceptualization, S.T. and Y.U.; methodology, S.T. and Y.U.; software, Y.U.; validation, S.T., Y.K., K.S., A.T., Y.S., N.A., O.K. and Y.U.; formal analysis, S.T. and Y.U.; investigation, S.T. and Y.U.; resources, S.T. and Y.U.; data curation, S.T. and Y.U.; writing—original draft preparation, S.T. and Y.U.; writing—review and editing, S.T., Y.K., K.S., A.T., Y.S., N.A., O.K. and Y.U.; visualization, S.T. and Y.U.; supervision, Y.U.; project administration, S.T. and Y.U.; funding acquisition, Y.U. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by the Grants-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (JSPS), Grant Number JP22K06707.

Institutional Review Board Statement

This study used fully anonymized data obtained from the publicly available Japan Adverse Drug Event Report (JADER) database. According to the policies of Meiji Pharmaceutical University, ethical approval was not required for studies using anonymized, open-access secondary data.

Informed Consent Statement

Patient consent was waived because all data analyzed in this study were anonymized and publicly accessible in the JADER database.

Data Availability Statement

The data presented in this study are openly available from the JADER (Japanese Adverse Drug Event Report) database on the PMDA website (accessed on 16 March 2025).

Acknowledgments

We would like to thank the Department of Pharmacy at National Hospital Organization Kanagawa Hospital and the Department of Medical Molecular Informatics at Meiji Pharmaceutical University for their support in conducting this study. During the preparation of this manuscript, the authors used AI-based tools, Claude Opus 4.6 (Anthropic PBC, San Francisco, CA, USA) and ChatGPT-5.3 (OpenAI OpCo, LLC, San Francisco, CA, USA), to assist in preparing the graphical abstract. The authors reviewed and edited the generated content and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ATCAnatomical Therapeutic Chemical Classification System
COVID-19 vaccineCoronavirus disease 2019 vaccine
CTLA-4Cytotoxic T lymphocyte-associated protein 4
GBSGuillain–Barré syndrome
IDIdentification number
irAEsImmune-related adverse events
JADERJapanese Adverse Drug Event Report
MedDRA/JMedical Dictionary for Regulatory Activities/Japanese version
PD-1Programmed death-1
PD-L1Programmed death-ligand 1
PMDAPharmaceuticals and Medical Devices Agency
RORReporting odds ratio
RSVRespiratory syncytial virus
SMQStandardized MedDRA Query
TNF-αTumor necrosis factor alpha
WHOWorld Health Organization

References

  1. van Doorn, P.A.; Van den Bergh, P.Y.K.; Hadden, R.D.M.; Avau, B.; Vankrunkelsven, P.; Attarian, S.; Blomkwist-Markens, P.H.; Cornblath, D.R.; Goedee, H.S.; Harbo, T.; et al. European Academy of Neurology/Peripheral Nerve Society Guideline on diagnosis and treatment of Guillain-Barré syndrome. Eur. J. Neurol. 2023, 30, 3646–3674. [Google Scholar] [CrossRef]
  2. Shahrizaila, N.; Lehmann, H.C.; Kuwabara, S. Guillain-Barré syndrome. Lancet 2021, 397, 1214–1228. [Google Scholar] [CrossRef]
  3. Yuki, N.; Hartung, H.P. Guillain-Barré syndrome. N. Engl. J. Med. 2012, 366, 2294–2304. [Google Scholar] [CrossRef]
  4. Yuki, N.; Taki, T.; Inagaki, F.; Kasama, T.; Takahashi, M.; Saito, K.; Handa, S.; Miyatake, T. A bacterium lipopolysaccharide that elicits Guillain-Barré syndrome has a GM1 ganglioside-like structure. J. Exp. Med. 1993, 178, 1771–1775. [Google Scholar] [CrossRef]
  5. Hahn, A.F. Guillain-Barré syndrome. Lancet 1998, 352, 635–641. [Google Scholar] [CrossRef]
  6. Xu, L.; Zhao, C.; Bao, Y.; Liu, Y.; Liang, Y.; Wei, J.; Liu, G.; Wang, J.; Zhan, S.; Wang, S.; et al. Variation in worldwide incidence of Guillain-Barré syndrome: A population-based study in urban China and existing global evidence. Front. Immunol. 2024, 15, 1415986. [Google Scholar] [CrossRef] [PubMed]
  7. van den Berg, B.; Walgaard, C.; Drenthen, J.; Fokke, C.; Jacobs, B.C.; van Doorn, P.A. Guillain-Barré syndrome: Pathogenesis, diagnosis, treatment and prognosis. Nat. Rev. Neurol. 2014, 10, 469–482. [Google Scholar] [CrossRef] [PubMed]
  8. Haber, P.; Sejvar, J.; Mikaeloff, Y.; DeStefano, F. Vaccines and Guillain-Barré syndrome. Drug Saf. 2009, 32, 309–323. [Google Scholar] [CrossRef] [PubMed]
  9. Schneiderbauer, R.; Schneiderbauer, M.; Wick, W.; Enk, A.H.; Haenssle, H.A.; Hassel, J.C. PD-1 Antibody-induced Guillain-Barré Syndrome in a Patient with Metastatic Melanoma. Acta Derm.-Venereol. 2017, 97, 395–396. [Google Scholar] [CrossRef]
  10. Fusaroli, M.; Salvo, F.; Begaud, B.; AlShammari, T.M.; Bate, A.; Battini, V.; Brueckner, A.; Candore, G.; Carnovale, C.; Crisafulli, S.; et al. The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement. Drug Saf. 2024, 47, 575–584. [Google Scholar] [CrossRef]
  11. Fusaroli, M.; Salvo, F.; Begaud, B.; AlShammari, T.M.; Bate, A.; Battini, V.; Brueckner, A.; Candore, G.; Carnovale, C.; Crisafulli, S.; et al. The Reporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration. Drug Saf. 2024, 47, 585–599. [Google Scholar] [CrossRef]
  12. Nomura, K.; Takahashi, K.; Hinomura, Y.; Kawaguchi, G.; Matsushita, Y.; Marui, H.; Anzai, T.; Hashiguchi, M.; Mochizuki, M. Effect of database profile variation on drug safety assessment: An analysis of spontaneous adverse event reports of Japanese cases. Drug Des. Dev. Ther. 2015, 9, 3031–3041. [Google Scholar] [CrossRef]
  13. Watanabe, A.; Uesawa, Y. Steroid-Induced Thrombosis: A Comprehensive Analysis Using the FAERS Database. Pharmaceuticals 2025, 18, 1463. [Google Scholar] [CrossRef]
  14. Yamaoka, K.; Masago, S.; Uchida, M.; Uesawa, Y.; Muroi, N.; Shimizu, T. Adverse events of antibody-drug conjugates: Comparative analysis of agents with a common payload using the adverse event spontaneous reporting database. Oncologist 2025, 30, oyaf298. [Google Scholar] [CrossRef]
  15. Toriumi, S.; Kobayashi, A.; Sueki, H.; Yamamoto, M.; Uesawa, Y. Exploring the Mechanisms Underlying Drug-Induced Fractures Using the Japanese Adverse Drug Event Reporting Database. Pharmaceuticals 2021, 14, 1299. [Google Scholar] [CrossRef]
  16. Pharmaceutical and Medical Devices Agency. Available online: https://www.pmda.go.jp/safety/info-services/drugs/adr-info/suspected-adr/0005.html (accessed on 16 March 2025).
  17. Toriumi, S.; Mimori, R.; Sakamoto, H.; Sueki, H.; Yamamoto, M.; Uesawa, Y. Examination of Risk Factors and Expression Patterns of Atypical Femoral Fractures Using the Japanese Adverse Drug Event Report Database: A Retrospective Pharmacovigilance Study. Pharmaceuticals 2023, 16, 626. [Google Scholar] [CrossRef] [PubMed]
  18. Pulendran, B.; Ahmed, R. Immunological mechanisms of vaccination. Nat. Immunol. 2011, 12, 509–517. [Google Scholar] [CrossRef]
  19. Censi, S.; Bisaccia, G.; Gallina, S.; Tomassini, V.; Uncini, A. Guillain-Barré syndrome and COVID-19 vaccination: A systematic review and meta-analysis. J. Neurol. 2024, 271, 1063–1071. [Google Scholar] [CrossRef] [PubMed]
  20. Patone, M.; Handunnetthi, L.; Saatci, D.; Pan, J.; Katikireddi, S.V.; Razvi, S.; Hunt, D.; Mei, X.W.; Dixon, S.; Zaccardi, F.; et al. Neurological complications after first dose of COVID-19 vaccines and SARS-CoV-2 infection. Nat. Med. 2021, 12, 2144–2153. [Google Scholar] [CrossRef] [PubMed]
  21. Martín Arias, L.H.; Sanz, R.; Sáinz, M.; Treceño, C.; Carvajal, A. Guillain-Barré syndrome and influenza vaccines: A meta-analysis. Vaccine 2015, 33, 3773–3778. [Google Scholar] [CrossRef]
  22. Nachamkin, I.; Shadomy, S.V.; Moran, A.P.; Cox, N.; Fitzgerald, C.; Ung, H.; Corcoran, A.T.; Iskander, J.K.; Schonberger, L.B.; Chen, R.T. Anti-ganglioside antibody induction by swine (A/NJ/1976/H1N1) and other influenza vaccines: Insights into vaccine-associated Guillain-Barré syndrome. J. Infect. Dis. 2008, 198, 226–233. [Google Scholar] [CrossRef] [PubMed]
  23. Sato, S.; Katsuta, T.; Kawazoe, Y.; Takahashi, M.; Murata, F.; Maeda, M.; Fukuda, H.; Kamidani, S. Immune thrombocytopenic purpura and Guillain-Barré syndrome after 23-valent pneumococcal polysaccharide vaccination in Japan: The vaccine effectiveness, networking, and universal safety (VENUS) study. Vaccine 2024, 42, 4–7. [Google Scholar] [CrossRef]
  24. Mbata, O.E.; Atiku, P.M. Rapid Onset of Guillain Barré Syndrome following Quadrivalent Human Papilloma Virus Vaccination in a Young Female: A Case Report. Ann. Vaccines Immun. 2025, 9, 1026. [Google Scholar]
  25. Hegde, N.R.; Gore, M.M. Japanese encephalitis vaccines: Immunogenicity, protective efficacy, effectiveness, and impact on the burden of disease. Hum. Vaccin. Immunother. 2017, 13, 1320–1337. [Google Scholar] [CrossRef]
  26. Nelson, J.C.; Ulloa-Pérez, E.; Yu, O.; Cook, A.J.; Jackson, M.L.; Belongia, E.A.; Daley, M.F.; Harpaz, R.; Kharbanda, E.O.; Klein, N.P.; et al. Active Postlicensure Safety Surveillance for Recombinant Zoster Vaccine Using Electronic Health Record Data. Am. J. Epidemiol. 2023, 192, 205–216. [Google Scholar] [CrossRef]
  27. Di Pietrantonj, C.; Rivetti, A.; Marchione, P.; Debalini, M.G.; Demicheli, V. Vaccines for measles, mumps, rubella, and varicella in children. Cochrane Database Syst. Rev. 2021, 11, CD004407. [Google Scholar] [CrossRef]
  28. Pan, M.; Sun, T.; Zhu, W.; Liu, H.; Dong, H. Guillain Barré syndrome after combined diphtheria, tetanus, and acellular pertussis (DTaP) vaccine: A rare pediatric case report and review of literature. Hum. Vaccin. Immunother. 2023, 19, 2261199. [Google Scholar] [CrossRef]
  29. Souayah, N.; Nasar, A.; Suri, M.F.; Qureshi, A.I. Guillain-Barré syndrome after vaccination in United States: Data from the Centers for Disease Control and Prevention/Food and Drug Administration Vaccine Adverse Event Reporting System (1990–2005). J. Clin. Neuromuscul. Dis. 2009, 11, 1–6. [Google Scholar] [CrossRef]
  30. Trusinska, D.; Lee, B.; Ferdous, S.; Lansbury, L.; Burden, C.; Anand, A.; Stowe, J.; Mensah, A.; Lim, W.; Marsh, K.; et al. Real-world evidence on RSV vaccine uptake, effectiveness, and safety in older adults: A systematic review and meta-analysis. Lancet Reg. Health Eur. 2026, 64, 101623. [Google Scholar] [CrossRef]
  31. Principi, N.; Esposito, S. Vaccine-preventable diseases, vaccines and Guillain-Barre’ syndrome. Vaccine 2019, 37, 5544–5550. [Google Scholar] [CrossRef] [PubMed]
  32. Leach, D.R.; Krummel, M.F.; Allison, J.P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 1996, 271, 1734–1736. [Google Scholar] [CrossRef]
  33. Ishida, Y.; Agata, Y.; Shibahara, K.; Honjo, T. Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J. 1992, 11, 3887–3895. [Google Scholar] [CrossRef]
  34. Sonpavde, G.P.; Grivas, P.; Lin, Y.; Hennessy, D.; Hunt, J.D. Immune-related adverse events with PD-1 versus PD-L1 inhibitors: A meta-analysis of 8730 patients from clinical trials. Future Oncol. 2021, 17, 2545–2558. [Google Scholar] [CrossRef]
  35. Salim, A.; Tapia Rico, G.; Shaikh, A.; Brown, M.P. A systematic review of immune checkpoint inhibitor-related neurological adverse events and association with anti-neuronal autoantibodies. Expert Opin. Biol. Ther. 2021, 21, 1237–1251. [Google Scholar] [CrossRef]
  36. Möhn, N.; Beutel, G.; Gutzmer, R.; Ivanyi, P.; Satzger, I.; Skripuletz, T. Neurological Immune Related Adverse Events Associated with Nivolumab, Ipilimumab, and Pembrolizumab Therapy-Review of the Literature and Future Outlook. J. Clin. Med. 2019, 8, 1777. [Google Scholar] [CrossRef]
  37. Cuzzubbo, S.; Javeri, F.; Tissier, M.; Roumi, A.; Barlog, C.; Doridam, J.; Lebbe, C.; Belin, C.; Ursu, R.; Carpentier, A.F. Neurological adverse events associated with immune checkpoint inhibitors: Review of the literature. Eur. J. Cancer 2017, 73, 1–8. [Google Scholar] [CrossRef] [PubMed]
  38. Kyriazoglou, A.; Liontos, M.; Papadopoulos, C.; Bilali, A.; Kostouros, E.; Pagoni, S.; Doumas, K.; Dimopoulos, M.A.; Bamias, A. Guillain-Barré Syndrome Related to Nivolumab: Case Report of a Patient With Urothelial Cancer and Review of the Literature. Clin. Genitourin. Cancer 2019, 17, e360–e364. [Google Scholar] [CrossRef] [PubMed]
  39. Oguri, T.; Sasada, S.; Shimizu, S.; Shigematsu, R.; Tsuchiya, Y.; Ishioka, K.; Takahashi, S.; Oki, K.; Kimura, Y.; Seki, R.; et al. A Case of Guillain-Barré Syndrome and Stevens-Johnson Syndrome/Toxic Epidermal Necrosis Overlap After Pembrolizumab Treatment. J. Investig. Med. High Impact Case Rep. 2021, 9, 23247096211037462. [Google Scholar] [CrossRef] [PubMed]
  40. Kelly Wu, W.; Broman, K.K.; Brownie, E.R.; Kauffmann, R.M. Ipilimumab-induced Guillain-Barré Syndrome Presenting as Dysautonomia: An Unusual Presentation of a Rare Complication of Immunotherapy. J. Immunother. 2017, 40, 196–199. [Google Scholar] [CrossRef]
  41. Yamanaka, N.; Oishi, M.; Shimizu, F.; Koga, M.; Kanda, T. Atezolizumab-induced Guillain-Barré syndrome-like acute demyelinating polyneuropathy responsive to steroid therapy: A case report. Rinsho Shinkeigaku 2021, 61, 653–657. [Google Scholar] [CrossRef]
  42. Abrahao, A.; Tenório, P.H.M.; Rodrigues, M.; Mello, M.; Nascimento, O.J.M. Guillain-Barré syndrome and checkpoint inhibitor therapy: Insights from pharmacovigilance data. BMJ Neurol. Open 2024, 6, e000544. [Google Scholar] [CrossRef]
  43. Johnson, D.B.; Manouchehri, A.; Haugh, A.M.; Quach, H.T.; Balko, J.M.; Lebrun-Vignes, B.; Mammen, A.; Moslehi, J.J.; Salem, J.E. Neurologic toxicity associated with immune checkpoint inhibitors: A pharmacovigilance study. J. Immunother. Cancer 2019, 7, 134. [Google Scholar] [CrossRef]
  44. Freeman, G.J.; Long, A.J.; Iwai, Y.; Bourque, K.; Chernova, T.; Nishimura, H.; Fitz, L.J.; Malenkovich, N.; Okazaki, T.; Byrne, M.C.; et al. Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J. Exp. Med. 2000, 192, 1027–1034. [Google Scholar] [CrossRef]
  45. Buchbinder, E.I.; Desai, A. CTLA-4 and PD-1 Pathways: Similarities, Differences, and Implications of Their Inhibition. Am. J. Clin. Oncol. 2016, 39, 98–106. [Google Scholar] [CrossRef] [PubMed]
  46. Zhang, H.; Shi, N.; Diao, Z.; Chen, Y.; Zhang, Y. Therapeutic potential of TNFα inhibitors in chronic inflammatory disorders: Past and future. Genes. Dis. 2020, 8, 38–47. [Google Scholar] [CrossRef] [PubMed]
  47. Mohan, N.; Edwards, E.T.; Cupps, T.R.; Oliverio, P.J.; Sandberg, G.; Crayton, H.; Richert, J.R.; Siegel, J.N. Demyelination occurring during anti-tumor necrosis factor alpha therapy for inflammatory arthritides. Arthritis Rheum. 2001, 44, 2862–2869. [Google Scholar] [CrossRef]
  48. Silburn, S.; McIvor, E.; McEntegart, A.; Wilson, H. Guillain-Barré syndrome in a patient receiving anti-tumour necrosis factor alpha for rheumatoid arthritis: A case report and discussion of literature. Ann. Rheum. Dis. 2008, 67, 575–576. [Google Scholar] [CrossRef] [PubMed]
  49. Natividade, N.B.; Felix, P.A.O.; Lerer, C. Guillain-Barré syndrome in a patient on adalimumab for the treatment of psoriasis. An. Bras. Dermatol. 2017, 92, 85–87. [Google Scholar] [CrossRef]
  50. Ferreira, S.D.C.; Vasconcelos, J.R.O.; Rezende, R.E.F.; Aprile, L.R.O.; Troncon, L.E.A. Guillain-Barré Syndrome in a Patient Receiving Anti-Tumor Necrosis Factor for Crohn Disease: Coincidence or Consequence? Am. J. Case Rep. 2024, 25, e943709. [Google Scholar] [CrossRef]
  51. Doden, M.H.; Manasra, M.R.; AbuIrayyeh, B.M.; Al-Ihribat, A.R.; Albandak, M. Guillain-Barrè syndrome after treatment with anti-tumour necrosis factor α (etanercept) in a rheumatoid arthritis patient: Case report and literature review. Sci. Prog. 2024, 107, 368504241304203. [Google Scholar] [CrossRef]
  52. Billmeier, U.; Dieterich, W.; Neurath, M.F.; Atreya, R. Molecular mechanism of action of anti-tumor necrosis factor antibodies in inflammatory bowel diseases. World J. Gastroenterol. 2016, 22, 9300–9313. [Google Scholar] [CrossRef]
  53. Beigel, F.; Schnitzler, F.; Paul Laubender, R.; Pfennig, S.; Weidinger, M.; Göke, B.; Seiderer, J.; Ochsenkühn, T.; Brand, S. Formation of antinuclear and double-strand DNA antibodies and frequency of lupus-like syndrome in anti-TNF-α antibody-treated patients with inflammatory bowel disease. Inflamm. Bowel Dis. 2011, 17, 91–98. [Google Scholar] [CrossRef]
  54. Kunchok, A.; Aksamit, A.J., Jr.; Davis, J.M., 3rd; Kantarci, O.H.; Keegan, B.M.; Pittock, S.J.; Weinshenker, B.G.; McKeon, A. Association Between Tumor Necrosis Factor Inhibitor Exposure and Inflammatory Central Nervous System Events. JAMA Neurol. 2020, 77, 937–946. [Google Scholar] [CrossRef]
  55. Girgin, N.K.; İşçimen, R.; Yılmaz, E.; Kahveci, Ş.F.; Kutlay, O. Guillain-Barré Syndrome and Human Immunodeficiency Virus. Turk. J. Anaesthesiol. Reanim. 2014, 42, 100–102. [Google Scholar] [CrossRef]
  56. Piliero, P.J.; Fish, D.G.; Preston, S.; Cunningham, D.; Kinchelow, T.; Salgo, M.; Qian, J.; Drusano, G.L. Guillain-Barré syndrome associated with immune reconstitution. Clin. Infect. Dis. 2003, 36, e111–e114. [Google Scholar] [CrossRef] [PubMed]
  57. Braish, J.S.; Kugler, E.; Jabbour, E.; Woodman, K.; Ravandi, F.; Nicholas, S.; Jain, N.; Kantarjian, H.; Sasaki, K. Incidence and Clinical Presentation of Severe Neurotoxicity from Nelarabine in Patients with T-Cell Acute Lymphoblastic Leukemia. Clin. Lymphoma Myeloma Leuk. 2024, 24, 783–788. [Google Scholar] [CrossRef]
  58. Tsiodras, S.; Zafiropoulou, R.; Kanta, E.; Demponeras, C.; Karandreas, N.; Manesis, E.K. Painful peripheral neuropathy associated with voriconazole use. Arch. Neurol. 2005, 62, 144–146. [Google Scholar] [CrossRef] [PubMed]
  59. Benitez, L.L.; Carver, P.L. Adverse Effects Associated with Long-Term Administration of Azole Antifungal Agents. Drugs 2019, 79, 833–853. [Google Scholar] [CrossRef] [PubMed]
  60. Niazi, M.A.; Azhar, A.; Tufail, K.; Feyssa, E.L.; Penny, S.F.; McGregory, M.; Araya, V.; Ortiz, J.A. Guillain-Barre syndrome associated with peginterferon alfa-2a for chronic hepatitis C: A case report. World J. Hepatol. 2010, 2, 162–166. [Google Scholar] [CrossRef]
  61. Ogunjimi, O.B.; Tsalamandris, G.; Paladini, A.; Varrassi, G.; Zis, P. Guillain-Barré Syndrome Induced by Vaccination Against COVID-19: A Systematic Review and Meta-Analysis. Cureus 2023, 15, e37578. [Google Scholar] [CrossRef] [PubMed]
  62. Chalela, J.A.; Andrews, C.; Bashmakov, A.; Kapoor, N.; Snelgrove, D. Reports of Guillain-Barre Syndrome Following COVID-19 Vaccination in the USA: An Analysis of the VAERS Database. J. Clin. Neurol. 2023, 19, 179–185. [Google Scholar] [CrossRef]
  63. Schonberger, L.B.; Bregman, D.J.; Sullivan-Bolyai, J.Z.; Keenlyside, R.A.; Ziegler, D.W.; Retailliau, H.F.; Eddins, D.L.; Bryan, J.A. Guillain-Barre syndrome following vaccination in the National Influenza Immunization Program, United States, 1976–1977. Am. J. Epidemiol. 1979, 110, 105–123. [Google Scholar] [CrossRef] [PubMed]
  64. Lasky, T.; Terracciano, G.J.; Magder, L.; Koski, C.L.; Ballesteros, M.; Nash, D.; Clark, S.; Haber, P.; Stolley, P.D.; Schonberger, L.B.; et al. The Guillain-Barré syndrome and the 1992-1993 and 1993-1994 influenza vaccines. N. Engl. J. Med. 1998, 339, 1797–1802. [Google Scholar] [CrossRef]
  65. Kwong, J.C.; Vasa, P.P.; Campitelli, M.A.; Hawken, S.; Wilson, K.; Rosella, L.C.; Stukel, T.A.; Crowcroft, N.S.; McGeer, A.J.; Zinman, L.; et al. Risk of Guillain-Barré syndrome after seasonal influenza vaccination and influenza health-care encounters: A self-controlled study. Lancet Infect. Dis. 2013, 13, 769–776. [Google Scholar] [CrossRef] [PubMed]
  66. Kaneda, Y.; Hashimoto, T.; Kaneda, U.; Higuchi, Y.; Murakami, J.; Inada, M.; Senoo, Y.; Fujieda, T.; Murata, Y.; Tanimoto, T. Guillain-Barre Syndrome After COVID-19 Vaccination: A Secondary Analysis of Domestic Safety Data by the Japanese Government. Cureus 2022, 14, e30905. [Google Scholar] [CrossRef]
  67. Ravishankar, N. Guillain-Barre Syndrome Following PCV Vaccine. Clin. Surg. 2017, 2, 1413. [Google Scholar]
  68. Oliveira, M.; Marquez, P.; Ennulat, C.; Blanc, P.; Welsh, K.; Nair, N.; Taminato, M.; Moro, P.L. Post-licensure Safety Surveillance of 20-Valent Pneumococcal Conjugate Vaccine (PCV20) Among US Adults in the Vaccine Adverse Event Reporting System (VAERS). Drug Saf. 2025, 48, 279–286. [Google Scholar] [CrossRef]
  69. Boender, T.S.; Bartmeyer, B.; Coole, L.; Wichmann, O.; Harder, T. Risk of Guillain-Barré syndrome after vaccination against human papillomavirus: A systematic review and meta-analysis, 1 January 2000 to 4 April 2020. Eurosurveillance 2022, 27, 2001619. [Google Scholar] [CrossRef] [PubMed]
  70. Roodbol, J.; de Wit, M.C.; Walgaard, C.; de Hoog, M.; Catsman-Berrevoets, C.E.; Jacobs, B.C. Recognizing Guillain-Barre syndrome in preschool children. Neurology 2011, 76, 807–810. [Google Scholar] [CrossRef]
  71. Hagen, K.M.; Ousman, S.S. The Neuroimmunology of Guillain-Barré Syndrome and the Potential Role of an Aging Immune System. Front. Aging Neurosci. 2021, 12, 613628. [Google Scholar] [CrossRef]
  72. Sejvar, J.J.; Baughman, A.L.; Wise, M.; Morgan, O.W. Population incidence of Guillain-Barré syndrome: A systematic review and meta-analysis. Neuroepidemiology 2011, 36, 123–133. [Google Scholar] [CrossRef]
  73. O’Hare, M.; Guidon, A.C. Peripheral nervous system immune-related adverse events due to checkpoint inhibition. Nat. Rev. Neurol. 2024, 20, 509–525. [Google Scholar] [CrossRef]
  74. Albarrán, V.; Chamorro, J.; Rosero, D.I.; Saavedra, C.; Soria, A.; Carrato, A.; Gajate, P. Neurologic Toxicity of Immune Checkpoint Inhibitors: A Review of Literature. Front. Pharmacol. 2022, 13, 774170. [Google Scholar] [CrossRef]
  75. Fan, Q.; Hu, Y.; Wang, X.; Zhao, B. Guillain-Barré syndrome in patients treated with immune checkpoint inhibitors. J. Neurol. 2021, 268, 2169–2174. [Google Scholar] [CrossRef] [PubMed]
  76. Li, Y.; Zhang, X.; Zhao, C. Guillain-Barré Syndrome-Like Polyneuropathy Associated with Immune Checkpoint Inhibitors: A Systematic Review of 33 Cases. Biomed. Res. Int. 2021, 2021, 9800488. [Google Scholar] [CrossRef]
  77. Sun, X.; Roudi, R.; Dai, T.; Chen, S.; Fan, B.; Li, H.; Zhou, Y.; Zhou, M.; Zhu, B.; Yin, C.; et al. Immune-related adverse events associated with programmed cell death protein-1 and programmed cell death ligand 1 inhibitors for non-small cell lung cancer: A PRISMA systematic review and meta-analysis. BMC Cancer 2019, 19, 558. [Google Scholar] [CrossRef]
  78. Kanaoka, K.; Matsumoto, K.; Shiroyama, T.; Tsukaguchi, A.; Shoshihara, N.; Moritomo, K.; Kinehara, Y.; Mihashi, Y.; Kuge, T.; Yoneda, M.; et al. A Multicenter, Retrospective, Real-World Study of Atezolizumab Plus Chemotherapy and Pembrolizumab Plus Chemotherapy for Older Patients With NSCLC. JTO Clin. Res. Rep. 2025, 6, 100891. [Google Scholar] [CrossRef] [PubMed]
  79. Chen, D.S.; Irving, B.A.; Hodi, F.S. Molecular pathways: Next-generation immunotherapy--inhibiting programmed death-ligand 1 and programmed death-1. Clin. Cancer Res. 2012, 18, 6580–6587. [Google Scholar] [CrossRef]
  80. Paterson, A.M.; Brown, K.E.; Keir, M.E.; Vanguri, V.K.; Riella, L.V.; Chandraker, A.; Sayegh, M.H.; Blazar, B.R.; Freeman, G.J.; Sharpe, A.H. The programmed death-1 ligand 1: B7-1 pathway restrains diabetogenic effector T cells in vivo. J. Immunol. 2011, 187, 1097–1105. [Google Scholar] [CrossRef]
  81. Yang, J.; Riella, L.V.; Chock, S.; Liu, T.; Zhao, X.; Yuan, X.; Paterson, A.M.; Watanabe, T.; Vanguri, V.; Yagita, H.; et al. The novel costimulatory programmed death ligand 1/B7.1 pathway is functional in inhibiting alloimmune responses in vivo. J. Immunol. 2011, 187, 1113–1119. [Google Scholar] [CrossRef] [PubMed]
  82. Alemayehu, D. Evaluation of Reporting Bias in Postmarketing Risk Assessment Based on Spontaneous Reporting Systems. Pharm. Med. 2009, 23, 195–200. [Google Scholar] [CrossRef]
  83. Matsuda, S.; Aoki, K.; Kawamata, T.; Kimotsuki, T.; Kobayashi, T.; Kuriki, H.; Nakayama, T.; Okugawa, S.; Sugimura, Y.; Tomita, M.; et al. Bias in spontaneous reporting of adverse drug reactions in Japan. PLoS ONE 2015, 10, e0126413. [Google Scholar] [CrossRef]
  84. Pariente, A.; Avillach, P.; Salvo, F.; Thiessard, F.; Miremont-Salamé, G.; Fourrier-Reglat, A.; Haramburu, F.; Bégaud, B.; Moore, N. Effect of competition bias in safety signal generation: Analysis of a research database of spontaneous reports in France. Drug Saf. 2012, 35, 855–864. [Google Scholar] [CrossRef]
  85. García-Abeijon, P.; Costa, C.; Taracido, M.; Herdeiro, M.T.; Torre, C.; Figueiras, A. Factors Associated with Underreporting of Adverse Drug Reactions by Health Care Professionals: A Systematic Review Update. Drug Saf. 2023, 46, 625–636. [Google Scholar] [CrossRef]
  86. Caster, O.; Aoki, Y.; Gattepaille, L.M.; Grundmark, B. Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations. Drug Saf. 2020, 43, 479–487. [Google Scholar] [CrossRef]
  87. Noguchi, Y.; Tachi, T.; Teramachi, H. Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems. Front. Pharmacol. 2019, 10, 1319. [Google Scholar] [CrossRef]
  88. Hazell, L.; Shakir, S.A. Under-reporting of adverse drug reactions: A systematic review. Drug Saf. 2006, 29, 385–396. [Google Scholar] [CrossRef] [PubMed]
  89. Bate, A.; Evans, S.J. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol. Drug Saf. 2009, 6, 427–436. [Google Scholar] [CrossRef] [PubMed]
  90. ATC/DDD Index. Available online: https://atcddd.fhi.no/atc_ddd_index/ (accessed on 3 March 2025).
  91. MedDRA Japanese Maintenance Organization. Available online: https://www.pmrj.jp/jmo/php/indexj.php (accessed on 16 February 2025).
  92. Medical Dictionary for Regulatory Activities. Available online: https://www.meddra.org/standardised-meddra-queries (accessed on 10 February 2025).
  93. Hirooka, T.; Yamada, M. Evaluation of AEs Risk Using the “Japanese Adverse Drug Event Report Database” of PMDA. In Proceedings of the SAS User General Assembly, Tokyo, Japan, 1–3 December 2012; pp. 263–270. [Google Scholar]
  94. Hosoya, R.; Ishii-Nozawa, R.; Kurosaki, K.; Uesawa, Y. Analysis of Factors Associated with Hiccups Using the FAERS Database. Pharmaceuticals 2021, 15, 27. [Google Scholar] [CrossRef] [PubMed]
  95. Ruxton, G.D.; Neuhäuser, M. Review of alternative approaches to calculation of a confidence interval for the odds ratio of a 2 × 2 contingency table. Methods Ecol. Evol. 2013, 4, 9–13. [Google Scholar] [CrossRef]
  96. Yajima, A.; Uesawa, Y. A Comprehensive Analysis of Adverse Events Associated with HER2 Inhibitors Approved for Breast Cancer Using the FDA Adverse Event Report System (FAERS). Pharmaceuticals 2025, 18, 1510. [Google Scholar] [CrossRef]
  97. van Puijenbroek, E.P.; Bate, A.; Leufkens, H.G.; Lindquist, M.; Orre, R.; Egberts, A.C. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol. Drug Saf. 2002, 11, 3–10. [Google Scholar] [CrossRef]
  98. Rothman, K.J.; Lanes, S.; Sacks, S.T. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol. Drug Saf. 2004, 13, 519–523. [Google Scholar] [CrossRef]
  99. Harpaz, R.; DuMouchel, W.; LePendu, P.; Bauer-Mehren, A.; Ryan, P.; Shah, N.H. Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin. Pharmacol. Ther. 2013, 93, 539–546. [Google Scholar] [CrossRef] [PubMed]
  100. European Medicines Agency. Guideline on the Use of Statistical Signal Detection Methods in the Eudravigilance Data Analysis System. Available online: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/draft-guideline-use-statistical-signal-detection-methods-eudravigilance-data-analysis-system_en.pdf (accessed on 5 March 2025).
  101. Kurosaki, K.; Uesawa, Y. Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions. Biomolecules 2021, 11, 944. [Google Scholar] [CrossRef]
  102. Toriumi, S.; Shimokawa, K.; Yamamoto, M.; Uesawa, Y. Development of a Medication-Related Osteonecrosis of the Jaw Prediction Model Using the FDA Adverse Event Reporting System Database and Machine Learning. Pharmaceuticals 2025, 18, 423. [Google Scholar] [CrossRef]
  103. Toriumi, S.; Kobayashi, A.; Uesawa, Y. Comprehensive Study of the Risk Factors for Medication-Related Osteonecrosis of the Jaw Based on the Japanese Adverse Drug Event Report Database. Pharmaceuticals 2020, 13, 467. [Google Scholar] [CrossRef] [PubMed]
  104. Kan, Y.; Doi, M.; Uesawa, Y. Investigation of the total anticholinergic load of reported anticholinergic drug-related adverse events using the Japanese adverse drug event report database: A retrospective pharmacovigilance study. J. Pharm. Health Care Sci. 2025, 11, 8. [Google Scholar] [CrossRef] [PubMed]
  105. Kan, Y.; Nagai, J.; Uesawa, Y. Evaluation of antibiotic-induced taste and smell disorders using the FDA adverse event reporting system database. Sci. Rep. 2021, 11, 9625. [Google Scholar] [CrossRef]
  106. Nakao, S.; Hasegawa, S.; Umetsu, R.; Shimada, K.; Mukai, R.; Tanaka, M.; Matsumoto, K.; Yoshida, Y.; Inoue, M.; Satake, R.; et al. Pharmacovigilance study of anti-infective-related acute kidney injury using the Japanese adverse drug event report database. BMC Pharmacol. Toxicol. 2021, 22, 47. [Google Scholar] [CrossRef]
  107. Hasegawa, S.; Ikesue, H.; Satake, R.; Inoue, M.; Yoshida, Y.; Tanaka, M.; Matsumoto, K.; Wakabayashi, W.; Oura, K.; Muroi, N.; et al. Osteonecrosis of the Jaw Caused by Denosumab in Treatment-Naïve and Pre-Treatment with Zoledronic Acid Groups: A Time-to-Onset Study Using the Japanese Adverse Drug Event Report (JADER) Database. Drugs-Real World Outcomes 2022, 9, 659–665. [Google Scholar] [CrossRef]
  108. Sauzet, O.; Carvajal, A.; Escudero, A.; Molokhia, M.; Cornelius, V.R. Illustration of the weibull shape parameter signal detection tool using electronic healthcare record data. Drug Saf. 2013, 36, 995–1006. [Google Scholar] [CrossRef] [PubMed]
Pharmaceuticals 19 00688 g001
Figure 1. Procedure for creating the analysis data table from JADER. Duplicate data were removed from the Drug, Reaction, and Demographic tables. Only “suspected drugs” were extracted from the Drug table. ATC codes for therapeutic classification were added to the Drug table. Drug-associated GBS was defined using the Guillain–Barré syndrome Standardized MedDRA Query (SMQ) among the adverse events in the Reaction table. Furthermore, the Drug, Reaction, and Demographic tables were combined using identification numbers. Of the 2,794,953 reports included in the JADER analysis data table, 1873 involved drug-associated GBS. Red text indicates the number of records and, where applicable, the number of drugs.
Figure 1. Procedure for creating the analysis data table from JADER. Duplicate data were removed from the Drug, Reaction, and Demographic tables. Only “suspected drugs” were extracted from the Drug table. ATC codes for therapeutic classification were added to the Drug table. Drug-associated GBS was defined using the Guillain–Barré syndrome Standardized MedDRA Query (SMQ) among the adverse events in the Reaction table. Furthermore, the Drug, Reaction, and Demographic tables were combined using identification numbers. Of the 2,794,953 reports included in the JADER analysis data table, 1873 involved drug-associated GBS. Red text indicates the number of records and, where applicable, the number of drugs.
Pharmaceuticals 19 00688 g001
Pharmaceuticals 19 00688 g002
Figure 2. Relationships between drugs and GBS. In this scattergram volcano plot, the x-axis represents lnROR, and the y-axis represents −log10 (p-value). The dotted horizontal line indicates a p-value of 0.05 based on Fisher’s exact test. The color of each point represents the therapeutic class of the drug. Drugs showing signals for GBS are located in the upper right area of the plot. The asterisks (* and **) indicate COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Figure 2. Relationships between drugs and GBS. In this scattergram volcano plot, the x-axis represents lnROR, and the y-axis represents −log10 (p-value). The dotted horizontal line indicates a p-value of 0.05 based on Fisher’s exact test. The color of each point represents the therapeutic class of the drug. Drugs showing signals for GBS are located in the upper right area of the plot. The asterisks (* and **) indicate COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Pharmaceuticals 19 00688 g002
Pharmaceuticals 19 00688 g003
Figure 3. Time to onset of GBS associated with vaccines and immune checkpoint inhibitors. (A) Comparison among vaccines. (B) Comparison among immune checkpoint inhibitors. For drugs administered multiple times, the time to onset was defined as the interval from treatment initiation (first dose) to onset. The numbers in parentheses indicate the number of reported cases for each drug. Box-and-whisker plots show the time to onset. The center line indicates the median; the box represents the interquartile range (IQR, 25th–75th percentiles); and the whiskers extend to 1.5 × IQR or to the minimum and maximum values if no data fall beyond this range. The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Figure 3. Time to onset of GBS associated with vaccines and immune checkpoint inhibitors. (A) Comparison among vaccines. (B) Comparison among immune checkpoint inhibitors. For drugs administered multiple times, the time to onset was defined as the interval from treatment initiation (first dose) to onset. The numbers in parentheses indicate the number of reported cases for each drug. Box-and-whisker plots show the time to onset. The center line indicates the median; the box represents the interquartile range (IQR, 25th–75th percentiles); and the whiskers extend to 1.5 × IQR or to the minimum and maximum values if no data fall beyond this range. The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Pharmaceuticals 19 00688 g003
Pharmaceuticals 19 00688 g004
Figure 4. Cross-tabulation and formula used to calculate the ROR for an adverse event. The cross-tabulation is structured with reports for the suspected drug, all other reports, reports with an adverse event, and reports without an adverse event (a–d indicate the number of reports).
Figure 4. Cross-tabulation and formula used to calculate the ROR for an adverse event. The cross-tabulation is structured with reports for the suspected drug, all other reports, reports with an adverse event, and reports without an adverse event (a–d indicate the number of reports).
Pharmaceuticals 19 00688 g004
Table 1. Characteristics of patients involved in reports of drug-associated GBS.
Table 1. Characteristics of patients involved in reports of drug-associated GBS.
DrugNGender
Male/Female/Unknown and Not Reported		Age
Median (Min–Max)
Vaccines
COVID-19 vaccine354165/185/455 (5–95)
COVID-19 vaccine *9856/42/045 (15–85)
Influenza HA vaccine250131/115/455 (5–85)
Influenza HA vaccine (A/H1N1)2313/10/065 (5–75)
Human papillomavirus 2-valent vaccine560/56/015 (15–35)
Human papillomavirus 4-valent vaccine190/19/015 (15–45)
Pneumococcal vaccine5028/15/775 (45–85)
Hepatitis B vaccine147/7/025 (5–35)
Japanese encephalitis vaccine105/5/010 (5–15)
Zoster vaccine94/5/065 (55–75)
Measles-rubella combined vaccine87/1/015 (5–55)
Mumps vaccine70/7/015 (5–15)
Diphtheria-tetanus combined toxoid62/4/015 (15–15)
Tetanus toxoid66/0/030 (25–45)
Immune checkpoint inhibitors
Nivolumab9162/26/365 (35–85)
Pembrolizumab7644/27/575 (25–85)
Ipilimumab5437/17/065 (45–85)
Atezolizumab4326/14/375 (45–95)
Avelumab43/0/175 (75–75)
Others
Infliximab2622/4/065 (15–75)
The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Table 2. Onset status and Weibull distribution of drug-associated GBS.
Table 2. Onset status and Weibull distribution of drug-associated GBS.
DrugTime to Onset (Days) Weibull DistributionN
Median
(Min–Max)		Scale Parameter
α (95% CI)		Shape Parameter
β (95% CI)		Failure Pattern
Vaccines
COVID-19 vaccine8.5
(0.5–225.5)		13.41
(11.21–16.00)		0.71
(0.65–0.77)		Early failure type271
COVID-19 vaccine *10.5
(0.5–347.5)		27.43
(16.27–45.28)		0.61
(0.49–0.75)		Early failure type46
Influenza HA vaccine10.5
(0.5–212.5)		15.44
(12.66–18.76)		0.87
(0.77–0.98)		Early failure type148
Influenza HA vaccine (A/H1N1)10.5
(1.5–51.5)		13.36
(8.23–21.12)		1.09
(0.74–1.51)		Random failure type18
Pneumococcal vaccine4.5
(0.5–212.5)		10.83
(5.44–20.94)		0.57
(0.43–0.72)		Early failure type31
Human papillomavirus 2-valent vaccine32.5
(1.5–352.5)		81.92
(35.55–179.17)		0.77
(0.48–1.13)		Random failure type13
Immune checkpoint inhibitors
Nivolumab57.5
(0.5–359.5)		83.75
(62.50–110.90)		1.03
(0.82–1.27)		Random failure type51
Ipilimumab45.5
(5.5–169.5)		65.52
(50.42–84.10)		1.35
(1.03–1.71)		Wear-out failure type38
Pembrolizumab63.5
(0.5–314.5)		92.13
(57.51–144.19)		0.86
(0.63–1.14)		Random failure type29
Atezolizumab19.5
(4.5–147.5)		33.68
(21.36–51.90)		1.02
(0.74–1.33)		Random failure type23
The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Table 3. Time to onset and Weibull distribution of drug-associated GBS by gender.
Table 3. Time to onset and Weibull distribution of drug-associated GBS by gender.
DrugGenderTime to Onset (Days)Weibull DistributionNp Value #
Median
(Min–Max)		Scale Parameter
α (95% CI)		Shape Parameter
β (95% CI)
Vaccines
COVID-19 vaccine 0.432
Male8.5 (0.5–175.5)13.64 (10.77–17.18)0.81 (0.71–0.92)122
Female8 (0.5–242.5)13.44 (10.28–17.48)0.66 (0.58–0.74)144
COVID-19 vaccine * 0.005
Male16.5 (1.5–287.5)40.01 (22.62–68.82)0.70 (0.53–0.90)30
Female4.5 (0.5–347.5)11.49 (4.04–31.15)0.53 (0.37–0.72)16
Influenza HA vaccine 0.416
Male9.5 (0.5–71.5)13.71 (10.74–17.36)0.98 (0.83–1.15)79
Female10 (0.5–108.5)16.20 (12.11–21.46)0.89 (0.74–1.06)68
Influenza HA vaccine (A/H1N1) 0.032
Male14.5 (2.5–51.5)19.65 (11.20–33.32)1.33 (0.77–2.03)10
Female5.5 (1.5–16.5)7.03 (3.57–13.24)1.28 (0.68–2.11)8
Human papillomavirus 2-valent vaccine —
Male————
Female32.5 (1.5–352.5)81.92 (35.55–179.17)0.77 (0.48–1.13)13
Pneumococcal vaccine 0.496
Male3 (0.5–23.5)6.07 (3.09–11.39)0.80 (0.53–1.12)18
Female4.5 (0.5–31.5)8.23 (3.98–16.23)0.98 (0.58–1.47)11
Immune checkpoint inhibitors
Nivolumab 0.422
Male57.5 (0.5–359.5)76.83 (53.49–108.62)1.01 (0.77–1.29)35
Female67 (7.5–215.5)99.97 (59.12–163.25)1.09 (0.71–1.59)16
Ipilimumab 0.649
Male46.5 (5.5–112.5)59.91 (45.95–76.97)1.64 (1.17–2.20)26
Female44.5 (7.5–169.5)77.04 (42.21–135.01)1.13 (0.68–1.71)12
Pembrolizumab 0.853
Male63.5 (1.5–314.5)104.55 (55.05–191.02)0.89 (0.57–1.28)16
Female97 (0.5–293.5)81.32 (35.13–179.83)0.85 (0.49–1.33)11
Atezolizumab 0.203
Male20.5 (4.5–147.5)46.27 (23.87–85.81)0.97 (0.61–1.43)13
Female15.5 (8.5–20.5)16.97 (14.36–19.85)4.85 (2.63–7.91)9
# Wilcoxon/Kruskal–Wallis test. The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Table 4. Time to onset and Weibull distribution of drug-associated GBS by age.
Table 4. Time to onset and Weibull distribution of drug-associated GBS by age.
DrugAgeTime to Onset (Days)Weibull DistributionNp Value #
Median
(Min–Max)		Scale Parameter
α (95% CI)		Shape Parameter
β (95% CI)
Vaccines
COVID-19 vaccine 0.610
<20 y9.5 (0.5–153.5)14.96 (7.54–28.65)0.75 (0.52–1.00)19
≥20 y7.5 (0.5–242.5)13.41 (11.12–16.12)0.71 (0.65–0.77)250
COVID-19 vaccine * 0.524
<20 y347.5 (347.5–347.5)——1
≥20 y9.5 (0.5–287.5)24.57 (14.81–39.98)0.64 (0.51–0.78)45
Influenza HA vaccine 0.311
<20 y12.5 (1.5–108.5)18.52 (12.42–27.18)0.98 (0.75–1.23)31
≥20 y9.5 (0.5–90.5)13.94 (11.27–17.15)0.92 (0.80–1.06)116
Influenza HA vaccine (A/H1N1) 0.333
<20 y16.5 (16.5–16.5)——1
≥20 y10.5 (1.5–51.5)12.98 (7.73–21.16)1.06 (0.71–1.47)17
Human papillomavirus 2-valent vaccine 0.894
<20 y32.5 (1.5–352.5)82.62 (33.25–193.62)0.74 (0.45–1.11)12
≥20 y52.5 (52.5–52.5)——1
Pneumococcal vaccine —
<20 y———0
≥20 y3.5 (0.5–31.5)6.86 (4.25–10.79)0.86 (0.63–1.12)29
Immune checkpoint inhibitors
Nivolumab —
<20 y———0
≥20 y57.5 (0.5–359.5)83.75 (62.50–110.90)1.03 (0.82–1.27)51
Ipilimumab —
<20 y———0
≥20 y45.5 (5.5–169.5)65.52 (50.42–84.10)1.35 (1.03–1.71)38
Pembrolizumab —
<20 y———0
≥20 y63.5 (0.5–314.5)90.05 (55.02–143.74)0.84 (0.61–1.12)28
Atezolizumab —
<20 y———0
≥20 y19.5 (4.5–147.5)33.68 (21.36–51.90)1.02 (0.74–1.33)23
# Wilcoxon/Kruskal–Wallis test. The asterisk (*) indicates COVID-19 vaccine reports that shared the same generic name but could not be distinguished as separate medicinal products in JADER.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Toriumi, S.; Kurihara, Y.; Shimokawa, K.; Tanaka, A.; Araki, N.; Kawai, O.; Sugiura, Y.; Uesawa, Y. Disproportionality Analysis and Timing of Drug-Associated Guillain–Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database. Pharmaceuticals 2026, 19, 688. https://doi.org/10.3390/ph19050688

AMA Style

Toriumi S, Kurihara Y, Shimokawa K, Tanaka A, Araki N, Kawai O, Sugiura Y, Uesawa Y. Disproportionality Analysis and Timing of Drug-Associated Guillain–Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database. Pharmaceuticals. 2026; 19(5):688. https://doi.org/10.3390/ph19050688

Chicago/Turabian Style

Toriumi, Shinya, Yousuke Kurihara, Komei Shimokawa, Arihito Tanaka, Norito Araki, Osamu Kawai, Yasoo Sugiura, and Yoshihiro Uesawa. 2026. "Disproportionality Analysis and Timing of Drug-Associated Guillain–Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database" Pharmaceuticals 19, no. 5: 688. https://doi.org/10.3390/ph19050688

APA Style

Toriumi, S., Kurihara, Y., Shimokawa, K., Tanaka, A., Araki, N., Kawai, O., Sugiura, Y., & Uesawa, Y. (2026). Disproportionality Analysis and Timing of Drug-Associated Guillain–Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database. Pharmaceuticals, 19(5), 688. https://doi.org/10.3390/ph19050688

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop