Next Article in Journal
Insight into Risk Factors, Pharmacogenetics/Genomics, and Management of Adverse Drug Reactions in Elderly: A Narrative Review
Previous Article in Journal
Radiotracers for Imaging of Fibrosis: Advances during the Last Two Decades and Future Directions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 16
Issue 11
10.3390/ph16111541
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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database

by
Risako Hirai
and
Yoshihiro Uesawa
*
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan
*
Author to whom correspondence should be addressed.
Pharmaceuticals 2023, 16(11), 1541; https://doi.org/10.3390/ph16111541
Submission received: 30 August 2023 / Revised: 20 October 2023 / Accepted: 26 October 2023 / Published: 1 November 2023
(This article belongs to the Section Pharmacology)
Browse Figures
Versions Notes

Abstract

:
Adverse events associated with opioid use in palliative care have been extensively studied. However, predicting the occurrence of adverse events based on the specific opioid used remains difficult. This study aimed to comprehensively analyze the adverse events related to µ-opioid receptor stimulation of opioids approved in Japan and investigate the tendencies of adverse event occurrence among different opioids. We utilized the FDA Adverse Event Reporting System database to extract reported adverse events for opioids approved in Japan. Cluster analysis was performed on reporting odds ratios (RORs) of adverse event names among opioids to visualize relationships between opioids and adverse events, facilitating a comparative study of their classifications. We calculated the RORs of adverse events for the target opioids. Cluster analysis based on these RORs resulted in five broad clusters based on the reported adverse events: i.e., strong opioids, weak opioids, loperamide, tapentadol, and remifentanil. This study provides a comprehensive classification of the association between μ-opioid-receptor-stimulating opioids and adverse events.

1. Introduction

Opioids are important agents in the field of palliative medical treatment because of their powerful analgesic properties [1]. In the management of pain in particular, including cancer pain, opioids are key to improving patient quality of life. Although opioids are an important class of drugs in palliative care worldwide, opioid sales in the United States increased fourfold between 1999 and 2010 [2], causing a social problem known as the opioid crisis. In response, the World Health Organization (WHO) published the guidelines “Ensuring Balance in National Policies on Controlled Substances” in 2011 and “WH Guidelines on the Availability and Accessibility of Controlled Substances” in 2012 [3]. Regulation of controlled substance prescriptions has consequently been thoroughly enforced in the USA, and the prescribing and use of opioids have declined [4]. However, the number of opioid-related overdose deaths increased from 2010 to 2017 and stabilized until 2019. Then, a significant increase was observed in 2020 (68,630 deaths) and again in 2021 (80,411 deaths) [4].
In contrast, Japan has yet to experience such an opioid crisis. On the contrary, the current situation is one in which the quantity of opioids prescribed is not commensurate with patients’ pain due to doctors’ reluctance to prescribe opioids. This situation has been called into question by those who point out that opioid use in Japan tends to be low, both in general and in comparison to use in other countries [5].
Japan lags behind the USA not only in the prescription and use of opioids but also in the approval of opioid drugs. For example, tapentadol was approved for prescription in the USA in November 2008 [6] but was approved in Japan only in August 2014 [7]. Hydromorphone was approved in the USA in 1984 [8] but remained unapproved in Japan for over 30 years. Finally, after forceful requests from the Japanese Society for Palliative Medicine and others, the brand name Nalrapide®/Hydromorphone Hydrochloride was approved in Japan in June 2017 [9]. Because the approval of opioids in Japan is slower than in the USA, studies on adverse events tend to accumulate fewer cases in Japan than in the USA.
For these reasons, it is difficult to secure sufficient cases for robust analysis either in opioid case studies or using the Japanese Adverse Drug Event Report database (JADER), a large database in Japan. Although opioid prescription availability and guidelines vary from country to country as described above, Japanese guidelines for palliative medicine specify clearly how to respond to opioid side effects [10]. In clinical practice, coping therapies are used to deal with various symptoms of opioid side effects [10].
Known adverse events to opioids from a pharmacological point of view include nausea and vomiting, which are believed to be caused by chemoreceptor trigger zone stimulation due to μ-opioid receptor stimulation [11], and constipation, which is believed to be influenced by the peristaltic inhibitory effect unique to opioids. Respiratory depression, drowsiness, delirium, and somnolence have been observed due to the inhibitory effect on the medullary respiratory control center [12,13]. Although the involvement of μ-opioid receptors has been pointed out in these side effects, the details of the mechanism of occurrence of each of these symptoms have not been elucidated [14,15,16,17,18,19,20,21]. It has been reported that differences in the propensity to develop adverse events exist among opioids relative to adverse events for which the occurrence mechanism is not yet fully understood [22]. Thus, the goal of this study is to classify the association between adverse events and opioids comprehensively by extracting and clustering the μ-opioid receptor stimulating opioids from the database. Therefore, for this study, we conducted an adverse event analysis of opioids using the FDA Adverse Event Reporting System (FAERS) [23,24,25,26,27,28,29,30], a large database in the United States because of the problem of opioid use in Japan, the relatively new drug, and the low number of cases.
We conducted a comprehensive analysis of USA-based adverse events due to μ-opioid receptor stimulation by opioids approved in Japan in order to compare the incidence of adverse events among opioids. We hope that our findings will assist in drug selection, opioid switching, and the appropriate use of opioids.

2. Results

2.1. Number of Reported Adverse Events

According to the data from FAERS, the opioids oxycodone, morphine, and fentanyl exhibited the highest number of reported adverse events. It is worth noting that these drugs are commonly used in palliative care settings [31,32]. Please refer to Table 1 and Table S1 for more information.

2.2. Top of the Adverse Event

We identified 47 preferred terms for adverse events based on the mean lnROR of the target opioids. Frequently observed side effects, such as somnolence, delirium, and constipation, which are known to be associated with µ-opioid receptor stimulation, were common among these adverse events. We extracted the top 47 adverse events that had been reported in >150,000 cases (Table 2). The ROR and 95% confidence intervals for each target opioid concerning the 47 adverse event terms with a positive average ROR are presented in the supplementary data. The table presents the top 47 adverse events with >150,000 reported cases, including columns for the adverse event, number of reports, average lnROR, and ROR. The ROR and 95% confidence interval for each target opioid concerning the 47 adverse event terms with a positive average ROR are presented in the supplementary data.

2.3. Hierarchical Cluster Classifications

In this study, the 11 analyzed opioids were classified into five distinct groups using hierarchical cluster analysis (Ward’s method) based on the adverse event names and lnROR (Figure 1). The clusters comprised two primary groups of opioids, including loperamide, tapentadol, and remifentanil. Cluster 1 included codeine, pethidine, and dihydrocodeine. In cluster 2, loperamide was identified. Cluster 3 included fentanyl, morphine, hydromorphone, oxycodone, and methadone. Cluster 4 contained tapentadol, and cluster 5 contained remifentanil. Meanwhile, adverse effects were clustered into seven types. Cluster 1 included pain, malignant neoplasm progression, urinary retention, vomiting, constipation, hallucinations, altered mental status, lethargy, and the wrong technique in the drug usage process. Cluster 2 comprised death, drug toxicity, overdose, drug abuse, intentional misuse, and medical malpractice. Cluster 3 comprised somnolence, confusion, intentional overdose, hyperhidrosis, suicide, and drug withdrawal symptoms. Cluster 4 contained hypotension, bradycardia, tachycardia, an anaphylactic reaction, desaturation of oxygen, and hypoxia. Cluster 5 featured liver failure and abnormal liver function tests. Cluster 6 included echocardiographic (ECG) QT prolongation, cardiac arrest, and cardio-respiratory arrest. Cluster 7 encompasses disorientation, restlessness, and delirium.
The vertical axis represents the 11 µ-opioid receptor agonists, and the horizontal axis represents the names of reported adverse events. Red indicates a high log odds ratio and a high incidence of adverse events, whereas blue indicates a low log odds ratio and a low incidence of relevant adverse events.
We extracted primary and secondary suspect drugs from the drug table, removing duplicates. We then combined the reaction, demographic, and indication tables to create an analysis table.
The study utilized a 2 × 2 contingency table to investigate the association between reported adverse events and target opioids. The RORs were computed to determine the magnitude of this association.

3. Discussion

3.1. FAERS Database

This study analyzed the adverse event reports for target opioids using the FAERS database, which collects reports from public institutions in the United States. Although the focus was on opioids used in Japan, a larger number of cases were reported in FAERS compared with the JADER database, which is constructed by the Pharmaceuticals and Medical Devices Agency. This could be due to the reluctance of Japanese medical professionals to use opioids [33]. To enhance comparative validation among opioid drugs, the US side effect database was used.
As FAERS primarily reports adverse reactions from both inside and outside the United States, there are many cases of hydromorphone and tapentadol usage for which there is still limited information available in Japan. Consequently, FAERS analysis may contribute to palliative care in Japan. JADER, which is based on Japanese data, was not used in the analysis conducted in this study because the amount of data is less than that in FAERS; however, a study specific to the Japanese population would provide new knowledge and could guide treatment for the Japanese ethnic group. Thus, further data analysis and development of corresponding clinical research are desirable. Of the adverse events extracted using the ROR, those with a positive lnROR value were highly related to the target opioid. Adverse events such as somnolence, delirium, and constipation [10,11,12] were frequently reported in 47 diseases that were related to opioids (Table 2). We believe that the ROR of FAERS has a relationship with the side effects owing to the stimulation of the μ-opioid receptors. While it is generally not recommended to interpret ROR quantitatively in the analysis of side effect databases, we assumed that a highly reliable ROR that can withstand quantitative verification can be extracted using the number of reported adverse events and the p-value in Fischer’s exact test.

3.2. Categories of Opioids Based on Cluster Analysis

Cluster 1 included codeine, pethidine, and dihydrocodeine. This group tends to be less associated with adverse events such as overdose, substance abuse, and drug withdrawal symptoms compared with other groups. The reason is that drugs other than pethidine are clinically used for purposes other than narcotism and pain relief. Pethidine is used for purposes such as preanesthetic administration, adjunctive general anesthesia, and pain relief [34]. Adverse events such as overdose and substance abuse are classified into relatively small categories because they are often used under close supervision for purposes related to anesthesia.
Cluster 2 comprised loperamide, an over-the-counter drug commonly used to treat diarrhea symptoms. It is speculated that loperamide is closely related to diarrhea adverse events owing to its stimulation of peripheral μ-opioid receptors. Moreover, loperamide has a lower μ-opioid-receptor-stimulating effect in the central nervous system compared with other opioids, which suggests that it is less associated with adverse central nervous system events such as loss of consciousness and disorientation [35].
Cluster 3 comprised fentanyl, morphine, hydromorphone, oxycodone, and methadone, all classified as strong opioids used to manage cancer pain and chronic pain. These opioids have many reports of adverse events related to pain, and their association with malignant neoplasm progression and drug withdrawal syndrome was strongly classified. Methadone, also included in the same group, was associated with substance abuse, overdose, and drug withdrawal syndrome. This background is thought to be related to its use for heroin addiction treatment in the United States [36]. Methadone replacement therapy involves gradually withdrawing from the symptoms of addiction by administering methadone as a substitute for heroin.
Cluster 4 included tapentadol, which is expected to expand its efficacy range to include nervous system pain owing to its dual-acting mechanism that involves μ-opioid receptor agonistic action and noradrenaline reuptake inhibitory action [37]. Hence, it was clustered into a group distinct from other strong opioids. Tapentadol tends to be highly associated with overdoses and drug abuse [38], and its prescription for moderate or severe pain is approved, suggesting that it contributes significantly to improving the quality of life. The trend of other reported adverse events alludes to the fact that it is necessary to pay attention to delirium when using it, but it is possible that it is an opioid that can be used with relatively little effect on respiratory failure.
Cluster 5 contained remifentanil, which tends to cause fewer gastrointestinal adverse events, such as vomiting and constipation. A substantial tendency to develop respiratory failure, hypotension, and other adverse events was confirmed, which was different from that of other opioids. Remifentanil is used in the field of anesthesia and is dose- and rate-controlled for the induction and maintenance of anesthesia. This characteristic of remifentanil might have led to cluster classifications that differ from those of other opioids used for pain management [39].

3.3. Categories of Adverse Effects Based on Cluster Analysis

The adverse effects were clustered into seven types, each with specific characteristics and associated opioids:
  • Cluster 1: Pain, progression of malignant neoplasms, urinary retention, vomiting, constipation, hallucinations, altered mental status, lethargy, errors in the drug use process. The above adverse events tended to be reported more frequently with morphine and less frequently with remifentanil. Frequent occurrences of vomiting and constipation are known as adverse events attributed to μ-opioid receptor stimulation of opioids [10]. Based on these results, morphine increases the risk of adverse effects due to decreased renal function, which leads to decreased elimination of the metabolite M6G [40]. This finding strongly confirms the strong association of morphine with vomiting and constipation, which was observed in many patients with deteriorated renal function.
  • Cluster 2: Death, various drug toxicities, overdose, drug abuse, intentional misuse, medical malpractice. Among opioids, tapentadol was reported more frequently and remifentanil less frequently in this cluster, and tapentadol tended to be more involved in these adverse events than other strong opioids used in palliative care. The stronger association of tapentadol with drug abuse and overdose suggests that while tapentadol shows great promise for ease of use and efficacy in pain management, it should be used with caution due to its enhanced risks of illicit use.
  • Cluster 3: Somnolence, confusion, intentional overdose, hyperhidrosis, suicide, and drug withdrawal symptoms. The above adverse events were found to cluster with a high incidence of methadone. An analytical study using the Australian database found that fentanyl and methadone were more frequently involved in unintentional intoxication than other opioids [41]. Our results seem to support the above studies.
  • Cluster 4: Hypotension, bradycardia, tachycardia, anaphylactic reactions, decreased oxygen saturation, hypoxia. We observed a trend indicating a higher incidence for remifentanil and a lower incidence for tapentadol. As noted above, remifentanil is highly associated with intraoperative hypotension and the elevation of blood pressure, hence its use in anesthesiology. Remifentanil is an opioid used in an environment where it is prone to producing fluctuating circulatory dynamics that affect the supply–demand balance of oxygen supplied to the myocardium [42].
  • Cluster 5: Liver failure, abnormal liver function tests. These adverse events comprised a cluster with a high incidence of dihydrocodeine and codeine. Codeine and dihydrocodeine tended to have higher incidence of reported adverse events, including abnormal liver function tests and liver failure. Currently, the association of opioid receptors with drug-induced liver injury is limited to the finding that opioid receptors are not present in the liver [43]. However, the involvement of diverse stress response pathways (e.g., pathways related to oxidative stress, inflammatory stress, DNA damage, folded proteins, heat shock, and apoptosis) dependent on drug species has been reported with respect to the development of liver injury [44]. Unfortunately, our knowledge of opioid-induced liver injury is limited. The cluster classification performed in this study demonstrated a significant association with liver injury relative to codeine and dihydrocodeine; thus, future findings are expected to be accumulated.
  • Cluster 6: ECG QT prolongation, cardiac arrest, cardiac arrest—respiratory arrest. This cluster had a high incidence of methadone, and methadone tended to have a higher incidence of ECG QT prolongation, cardiac arrest, and cardiac arrest–respiratory arrest compared to other μ-opioid-receptor-stimulating opioids. The cluster was high in methadone, and methadone tended to have a higher incidence of ECG QT prolongation, cardiac arrest, and cardiac arrest–respiratory arrest compared to other μ-opioid-receptor-stimulating opioids. In addition, a previous study of methadone reported a stronger association with the adverse event of ECG QT prolongation [45]. The package insert [46] includes warnings for ECG QT prolongation and ventricular tachycardia (including torsades de pointes) [47]. However, the details of the causal mechanism for these events remain unknown [47]. Currently, guidelines also specify doses to be used with caution in the event of ECG QT prolongation [48].
  • Cluster 7: Disorientation, restlessness, and delirium. These adverse events were clusters that were more frequently associated with methadone and tapentadol than with other strong opioids used in palliative care. The hypothesis that delirium, the name of the adverse event, is caused by an imbalance of substances in the brain has been proposed, but no clear mechanism is known [49]. Future research on the relationship between methadone, tapentadol, and delirium is warranted to determine why side effects differ among opioids despite being mediated by opioid receptors, as well as differences in selectivity for opioid receptor subtypes (μ, κ, and δ receptors); we note the findings in previous studies [31,32,33,34,35,36,37,38,39,40,41]. Furthermore, differences in metabolism and excretion mechanisms among drugs [50] and individual genetic factors for opioids [51] may be the causes of differences in adverse drug reaction trends among the patient populations using the various opioids.
These analyses indicate that cluster analysis using a large database can be effectively used to compare adverse effect propensities among opioids. We believe that such comparisons will facilitate effective drug switching to avoid the risk of adverse events. The association of particular adverse event names with particular opioids may lead to a better understanding of the aforementioned differences in characteristics among opioids. Clearer associations may also help prompt the naming of diseases for which associations of particular symptoms with particular opioids would otherwise remain to be confirmed.

3.4. Limitations

Several limitations are inherent in this study. Firstly, the constraints are related to the utilized database. This database encapsulates information on adverse effects derived from spontaneous reports, thus restricting the cases to those recognized as adverse effects. In this inquiry, the total number of patients who utilized opioids remained undetermined, impeding an accurate evaluation of the adverse events. To address this, we endeavored to enhance the analytical value by instituting a filter for the number of reports, thereby bypassing simplistic p-value and ROR comparisons. Secondly, mild adverse effects may be under-reported, while severe adverse effects are likely reported with greater frequency, illustrating a phenomenon known as reporting bias, a common characteristic of self-reporting databases. Thirdly, it is a known fact that certain FAERS data can be incomplete, potentially containing blank cells indicative of missing values or inaccurately inputted characters or numbers. Fourthly, the co-administration of multiple drugs complicates the identification of the precise cause of adverse events. The ROR values extracted this time are merely referential, with the hope that they will be validated through subsequent appropriate clinical trials.

4. Materials and Methods

4.1. Data Table Creation

To extract data on adverse reactions associated with opioid drugs, we used 153,673,177 records of reported data from the FAERS database spanning the years 2004–2020 [23,24,25,26,27,28,29,30]. The data tables were categorized based on their characteristics, including a case information table (demographic), a drug information table (drug), an adverse event information table (reaction), and an underlying disease information table (indication). Information across these tables was linked using registration IDs.
The drug table classified the reported drugs into four categories: primary suspect drug, secondary suspect drug, concomitant drugs, and interactions. For this study, we extracted drugs reported as primary or secondary suspect drugs. Each data table was linked by its registration ID to create a data table for analysis for each opioid (see Figure 2).
To selectively extract opioids, we examined target drugs based on their μ-opioid receptor affinity, as reported in previous studies [42,52,53,54,55,56,57,58,59]. We selected 11 opioids, which are μ-opioid receptor agonists used in Japan, including morphine, fentanyl, oxycodone, codeine, dihydrocodeine, hydromorphone, methadone, tapentadol, pethidine, loperamide, and remifentanil. Each opioid extraction case was extracted as the total opioid, including drug names such as hydrate and hydrochloride. For morphine, the drug name excluding apomorphine was extracted as the relevant morphine. For fentanyl, drug names excluding remifentanil were extracted as fentanyl.
To determine the number of adverse event reports for each opioid, we extracted the report counts from the reaction table (see Table 1).

4.2. MedDRA

The Medical Dictionary for Regulatory Activities (MedDRA) is an international medical terminology developed by the International Conference on Harmonization of Drug Regulations to facilitate international information exchange and regulatory harmonization. In this study, we used the preferred terms of MedDRA ver22.0 for the disease names of reported adverse events.
From the analysis data table, we created a 2 × 2 contingency table to determine whether all reported adverse events and each suspected opioid were present. This contingency table allowed us to estimate the relationship between the adverse event of interest and the drug of interest (see Table 3). In this study, we employed a combination of the reporting odds ratio (ROR) and p-value in Fischer’s exact test as indicators of signal detection. If there are any 0 cells in the contingency table, the ROR calculation cannot be performed, and if the frequency is low, the estimation becomes unstable. To rectify bias, we applied the Haldane–Anscombe 1/2 correction by adding 0.5 to all cells [60]. We then calculated the RORs and Fisher’s exact tests for various adverse events for each opioid.

4.3. Hierarchical Cluster Classification

In this study, we used the hierarchical cluster classification method to classify reported adverse events among opioids. This method creates a tree diagram based on the similarity of distinguishing features of objects. We analyzed 21,334 reported adverse events in the reaction table of FAERS and calculated the lnROR for each adverse event of the 11 target opioid drugs. We then extracted 310 adverse event names that were reported >100,000 times.
To identify adverse event names closely related to opioids, we extracted those with a positive mean lnROR value for opioids (Table S1). Subsequently, we performed hierarchical cluster analysis (using Ward’s method) based on the adverse event names and the lnROR of the target opioids (Figure 1).

4.4. Statistical Analysis

We performed all statistical analyses using JMP Pro 14.2.0 (SAS Institute Inc., Cary, NC, USA) and set the level of statistical significance at 0.05.

5. Conclusions

In summary, the current study analyzed opioid usage in Japan and provided valuable insight into the occurrence of adverse effects related to μ-opioid receptor stimulation by using a novel clustering method for classification. The findings showed obvious differences in the risk of adverse events by opioid strength, and also supported the examination of differences in the incidence of adverse effects by the type of μ-opioid receptor stimulant (including opioids prescribed for pain relief) used. The results of this study can facilitate better decision making with regard to the use of opioids in clinical practices in Japan. Although opioids may be prescribed for palliative and nonpalliative care, the databases used in the current study prevented stratification of the analysis by purpose of use. Future cohort studies and clinical trials should aim to provide more robust evidence in this field by stratifying their analyses into palliative and nonpalliative care. In conclusion, this study provides valuable insight into the relationship between opioid usage and the occurrence of associated adverse events, thus allowing the development of a better understanding of the risks and benefits associated with these drugs in Japan.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph16111541/s1, Table S1: Positive mean lnROR value for opioids.

Author Contributions

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

Funding

This research received partial support from KAKENHI, granted by the Japan Society for the Promotion of Science (JSPS) (22K06707).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gaertner, J.; Boehlke, C.; Simone, C.B.; Hui, D. Early palliative care and the opioid crisis: Ten pragmatic steps towards a more rational use of opioids. Ann. Palliat. Med. 2019, 8, 490–497. [Google Scholar] [CrossRef] [PubMed]
  2. Centers for Disease Control and Prevention (CDC). Vital signs: Overdoses of prescription opioid pain relievers. United States, 1999–2008. MMWR Morb. Mortal. Wkly. Rep. 2011, 60, 1487–1492. [Google Scholar]
  3. World Health Organization. Ensuring Balance in National Policies on Controlled Substances: Guidance for Availability and Accessibility of Controlled Medicines. 2011. Available online: https://apps.who.int/iris/handle/10665/44519 (accessed on 25 October 2023).
  4. National Institute on Drug Abuse. Drug Overdose Death Rate. Trends and Statistics. 2023. Available online: https://nida.nih.gov/research-topics/trends-statistics/overdose-death-rates (accessed on 25 October 2023).
  5. Duthey, B.; Scholten, W. Adequacy of Opioid Analgesic Consumption at Country, Global, and Regional Levels in 2010, Its Relationship with Development Level, and Changes Compared with 2006. J. Pain Symptom Manag. 2014, 47, 283–297. [Google Scholar] [CrossRef]
  6. U.S. Food & Drug Administration. Drug Approval Package. 2009. Available online: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2008/022304s000_TOC.cfm (accessed on 25 October 2023).
  7. Available online: https://www.info.pmda.go.jp/go/pack/8219003G1024_1_08/ (accessed on 25 October 2023).
  8. Mallinckrodt. Exalgo (Hydromorphone Hydrochloride) Tablet Label. 2012. Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2013/021217s005lbl.pdf (accessed on 25 October 2023).
  9. Available online: https://www.info.pmda.go.jp/go/pack/8119003F1023_1_06 (accessed on 25 October 2023).
  10. Swegle, J.M.; Logemann, C. Management of common opioid-induced adverse effects. Am. Fam. Physician 2006, 74, 1347–1354. [Google Scholar] [PubMed]
  11. Benyamin, R.; Trescot, A.M.; Datta, S.; Buenaventura, R.; Adlaka, R.; Sehgal, N.; Glaser, S.E.; Vallejo, R. Opioid complications and side effects. Pain Physician 2008, 11, S105–S120. [Google Scholar] [CrossRef]
  12. Naeim, A.; Dy, S.M.; Lorenz, K.A.; Sanati, H.; Walling, A.; Asch, S.M. Evidence-Based Recommendations for Cancer Nausea and Vomiting. J. Clin. Oncol. 2008, 26, 3903–3910. [Google Scholar] [CrossRef]
  13. Wiffen, P.J.; Wee, B.; Derry, S.; Bell, R.F.; Moore, R.A. Opioids for cancer pain—An overview of Cochrane reviews. Cochrane Database Syst. Rev. 2017, 7, CD012592. [Google Scholar]
  14. Els, C.; Jackson, T.D.; Kunyk, D.; Lappi, V.G.; Sonnenberg, B.; Hagtvedt, R.; Sharma, S.; Kolahdooz, F.; Straube, S. Adverse events associated with medium- and long-term use of opioids for chronic non-cancer pain: An overview of cochrane reviews. Cochrane Database Syst. Rev. 2017, 10, CD012509. [Google Scholar]
  15. Lichter, I. Results of Antiemetic Management in Terminal Illness. J. Palliat. Care 1993, 9, 19–21. [Google Scholar] [CrossRef]
  16. Passik, S.D.; Lundberg, J.; Kirsh, K.L.; Theobald, D.; Donaghy, K.; Holtsclaw, E.; Cooper, M.; Dugan, W. A Pilot Exploration of the Antiemetic Activity of Olanzapine for the Relief of Nausea in Patients with Advanced Cancer and Pain. J. Pain Symptom Manag. 2002, 23, 526–532. [Google Scholar] [CrossRef]
  17. Agra, Y.; Sacristán, A.; González, M.; Ferrari, M.; Portugués, A.; Calvo, M.J. Efficacy of senna versus lactulose in terminal cancer patients treated with opioids. J. Pain Symptom Manag. 1998, 15, 1–7. [Google Scholar] [CrossRef] [PubMed]
  18. Mystakidou, K.; Tsilika, E.; Parpa, E.; Kouloulias, V.; Kouvaris, I.; Georgaki, S.; Vlahos, L. Long-term cancer pain management in morphine pre-treated and opioid naive patients with transdermal fentanyl. Int. J. Cancer 2003, 107, 486–492. [Google Scholar] [CrossRef] [PubMed]
  19. Breitbart, W.; Marotta, R.; Platt, M.M.; Weisman, H.; Derevenco, M.; Grau, C.; Corbera, K.; Raymond, S.; Lund, S.; Jacobson, P. A double-blind trial of haloperidol, chlorpromazine, and lorazepam in the treatment of delirium in hospitalized AIDS patients. Am. J. Psychiatry 1996, 153, 231–237. [Google Scholar] [CrossRef] [PubMed]
  20. Candy, B.; Jackson, K.C.; Jones, L.; Leurent, B.; Tookman, A.; King, M. Drug therapy for delirium in terminally ill adult patients. Cochrane Database Syst. Rev. 2012, 11, CD004770. [Google Scholar] [CrossRef]
  21. Enting, R.H.; Oldenmenger, W.H.; van der Rijt, C.C.D.; Wilms, E.B.; Elfrink, E.J.; Elswijk, I.; Smitt, P.A.E.S. A prospective study evaluating the response of patients with unrelieved cancer pain to parenteral opioids. Cancer 2002, 94, 3049–3056. [Google Scholar] [CrossRef]
  22. Nagai, J.; Uesawa, Y.; Kagaya, H. Analyses of opioid-induced adverse effects based on Japanese Adverse Drug Event Report database: Distinctive tendencies of the adverse events induced by morphine, fentanyl and oxycodone. Palliat. Care Res. 2015, 10, 113–119. [Google Scholar] [CrossRef]
  23. FDA. FDA Adverse Event Reporting System (FAERS); FDA: Silver Spring, MD, USA, 2012.
  24. United States Food and Drug Administration. Questions and Answers on FDA’s Adverse Event Reporting System (FAERS). What Is FAERS? 2018. Available online: https://www.fda.gov/drugs/surveillance/fda-adverse-event-reporting-system-faers (accessed on 9 August 2019).
  25. FDA. Potential Signals of Serious Risks/New Safety Information Identified from the FDA Adverse Event Reporting System (FAERS) (Formerly AERS); FDA: Silver Spring, MD, USA, 2014; pp. 6–7.
  26. Sakaeda, T.; Kadoyama, K.; Minami, K.; Okuno, Y. Commonality of Drug-associated Adverse Events Detected by 4 Commonly Used Data Mining Algorithms. Int. J. Med. Sci. 2014, 11, 461–465. [Google Scholar] [CrossRef]
  27. Veronin, M.A.; Schumaker, R.P.; Dixit, R.R.; Elath, H. Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic. Drug Healthc. Patient Saf. 2019, 11, 65–70. [Google Scholar] [CrossRef]
  28. Hoffman, K.B.; Demakas, A.R.; Dimbil, M.; Tatonetti, N.P.; Erdman, C.B. Stimulated Reporting: The Impact of US Food and Drug Administration-Issued Alerts on the Adverse Event Reporting System (FAERS). Drug Saf. 2014, 37, 971–980. [Google Scholar] [CrossRef]
  29. FDA. Data Mining of the Public Version of the FDA; Adverse Event Reporting System; FDA: Silver Spring, MD, USA, 2013.
  30. FDA. Follow-UP to the November 2009 Early Communication about an Ongoing Safety Review of Sibutamine, Marketed as Meridia. 2010. Available online: www.fda/gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/DrugSafetyInformationforHealthcareProfesionals/ucm198206.htm (accessed on 25 October 2023).
  31. Anekar, A.A.; Cascella, M. WHO Analgesic Ladder—StatPearls—NCBI Bookshelf. 2022 WHO Analgesic Ladder Aabha A. Available online: nih.gov (accessed on 25 October 2023).
  32. Schuster, M.; Bayer, O.; Heid, F.; Laufenberg-Feldmann, R. Opioid Rotation in Cancer Pain Treatment. Dtsch. Arztebl. Int. 2018, 115, 135–142. [Google Scholar] [CrossRef]
  33. Onishi, E.; Kobayashi, T.; Dexter, E.; Marino, M.; Maeno, T.; Deyo, R.A. Comparison of Opioid Prescribing Patterns in the United States and Japan: Primary Care Physicians’ Attitudes and Perceptions. J. Am. Board Fam. Med. 2017, 30, 248–254. [Google Scholar] [CrossRef]
  34. Available online: https://www.info.pmda.go.jp/go/pack/8211400A1049_1_06/?view=frame&style=SGML&lang=ja (accessed on 25 October 2023).
  35. Vetel, J.; Berard, H.; Fretault, N.; Lecomte, J. Comparison of racecadotril and loperamide in adults with acute diarrhoea. Aliment. Pharmacol. Ther. 1999, 13 (Suppl. S6), 21–26. [Google Scholar] [CrossRef]
  36. Toce, M.S.; Chai, P.R.; Burns, M.M.; Boyer, E.W. Pharmacologic treatment of opioid use disorder: A review of pharma-cotherapy, adjuncts, and toxicity. J. Med. Toxicol. 2018, 14, 306–322. [Google Scholar] [CrossRef]
  37. Kress, H.G. Tapentadol and its two mechanisms of action: Is there a new pharmacological class of centrally-acting analgesics on the horizon? Eur. J. Pain 2010, 14, 781–783. [Google Scholar] [CrossRef]
  38. Cepeda, M.S.; Fife, D.; Ma, Q.; Ryan, P.B. Comparison of the Risks of Opioid Abuse or Dependence between Tapentadol and Oxycodone: Results from a Cohort Study. J. Pain 2013, 14, 1227–1241. [Google Scholar] [CrossRef]
  39. Ren, W.; Matsusaki, T.; Bright, A.O.; Morimatsu, H. Association between the Remifentanil Dose during Anesthesia and Postoperative pain. Acta Med. Okayama 2022, 76, 187–193. [Google Scholar] [CrossRef]
  40. Owsiany, M.T.; Hawley, C.E.; Triantafylidis, L.K.; Paik, J.M. Opioid Management in Older Adults with Chronic Kidney Disease: A Review. Am. J. Med. 2019, 132, 1386–1393. [Google Scholar] [CrossRef]
  41. Lam, T.; Hayman, J.; Berecki-Gisolf, J.; Sanfilippo, P.; Lubman, D.I.; Nielsen, S. Pharmaceutical opioid poisonings in Victoria, Australia: Rates and characteristics of a decade of emergency department presentations among nine pharmaceutical opioids. Addiction 2022, 117, 623–636. [Google Scholar] [CrossRef]
  42. Muellejans, B.; Matthey, T.; Scholpp, J.; Schill, M. Sedation in the intensive care unit with remifentanil/propofol versus midazolam/fentanyl: A randomised, open-label, pharmacoeconomic trial. Crit. Care 2006, 10, R91. [Google Scholar] [CrossRef]
  43. Chartoff, E.H.; Connery, H.S. It’s more exciting than mu: Crosstalk between mu opioid receptors and glutamatergic transmission in the mesolimbic dopamine system. Front. Pharmacol. 2014, 5, 116. [Google Scholar] [CrossRef]
  44. Kuijper, I.A.; Yang, H.; Van De Water, B.; Beltman, J.B. Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling. Expert Opin. Drug Metab. Toxicol. 2017, 13, 5–17. [Google Scholar] [CrossRef]
  45. Cruciani, R.A. Methadone: ECG or not to ECG…that is still the question. J. Pain Symptom Manag. 2008, 36, 545–552. [Google Scholar] [CrossRef]
  46. FDA. Methadone Hydrochloride (Marketed as Dolophine) Information. Postmarket Drug Safety Information for Patients and Providers. 2015. Available online: https://www.fda.gov/drugs/postmarket-drug-safety-information-patients-and-providers/methadone-hydrochloride-marketed-dolophine-information (accessed on 25 October 2023).
  47. Modesto-Lowe, V.; Brooks, D.; Petry, N. Methadone Deaths: Risk Factors in Pain and Addicted Populations. J. Gen. Intern. Med. 2010, 25, 305–309. [Google Scholar] [CrossRef]
  48. Chou, R.; Cruciani, R.A.; Fiellin, D.A.; Compton, P.; Farrar, J.T.; Haigney, M.C.; Inturrisi, C.; Knight, J.R.; Otis-Green, S.; Marcus, S.M.; et al. Methadone safety guidelines methadone safety: A clinical practice guideline from the American Pain Society and College on Problems of Drug Dependence. J. Pain 2014, 15, 321–337. [Google Scholar] [CrossRef]
  49. Wilson, J.E.; Mart, M.F.; Cunningham, C.; Shehabi, Y.; Girard, T.D.; MacLullich, A.M.J.; Slooter, A.J.C.; Ely, E.W. Delirium. Nat. Rev. Dis. Primers 2020, 6, 90. [Google Scholar] [CrossRef]
  50. Wagmann, L.; Gampfer, T.M.; Meyer, M.R. Recent trends in drugs of abuse metabolism studies for mass spectrometry–based analytical screening procedures. Anal. Bioanal. Chem. 2021, 413, 5551–5559. [Google Scholar] [CrossRef]
  51. Pieretti, S.; Di Giannuario, A.; Di Giovannandrea, R.; Marzoli, F.; Piccaro, G.; Minosi, P.; Aloisi, A.M. Gender differences in pain and its relief. Ann. Dell’Istituto Super. Sanita 2016, 52, 184–189. [Google Scholar]
  52. Olson, K.M.; Duron, D.I.; Womer, D.; Fell, R.; Streicher, J.M. Comprehensive molecular pharmacology screening reveals potential new receptor interactions for clinically relevant opioids. PLoS ONE 2019, 14, e0217371. [Google Scholar] [CrossRef]
  53. Lipiński, P.F.J.; Kosson, P.; Matalińska, J.; Roszkowski, P.; Czarnocki, Z.; Jarończyk, M.; Misicka, A.; Dobrowolski, J.C.; Sadlej, J. Fentanyl Family at the Mu-Opioid Receptor: Uniform Assessment of Binding and Computational Analysis. Molecules 2019, 24, 740. [Google Scholar] [CrossRef]
  54. Hill, R.; Santhakumar, R.; Dewey, W.; Kelly, E.; Henderson, G. Fentanyl depression of respiration: Comparison with heroin and morphine. Br. J. Pharmacol. 2020, 177, 254–266. [Google Scholar] [CrossRef]
  55. Crews, K.R.; Gaedigk, A.; Dunnenberger, H.M.; Klein, T.E.; Shen, D.D.; Callaghan, J.T.; Kharasch, E.D.; Skaar, T.C.; Clinical Pharmacogenetics Implementation Consortium. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for Codeine Therapy in the Context of Cytochrome P450 2D6 (CYP2D6) Genotype. Clin. Pharmacol. Ther. 2012, 91, 321–326. [Google Scholar] [CrossRef] [PubMed]
  56. Yu, Y.; Zhang, L.; Yin, X.; Sun, H.; Uhl, G.R.; Wang, J.B. Mu Opioid receptor phosphorylation, desensitization, and ligand efficacy. J. Biol. Chem. 1997, 272, 28869–28874. [Google Scholar] [CrossRef] [PubMed]
  57. Sadeghi, M.; Tzschentke, T.M.; Christie, M.J. μ-opioid receptor activation and noradrenaline transport inhibition by tapentadol in rat single locus coeruleus neurons. Br. J. Pharmacol. 2015, 172, 460–468. [Google Scholar] [CrossRef]
  58. Liu, Z.-H.; He, Y.; Jin, W.-Q.; Chen, X.-J.; Zhang, H.-P.; Shen, Q.-X.; Chi, Z.-Q. Binding affinity to and dependence on some opioids in Sf9 insect cells expressing human mu-opioid receptor. Acta Pharmacol. Sin. 2003, 24, 859–863. [Google Scholar] [PubMed]
  59. Chen, W.; Chung, H.H.; Cheng, J.T. Opiate-induced constipation related to activation of small intestine opioid μ2-receptors. World J. Gastroenterol. 2012, 18, 1391–1396. [Google Scholar] [CrossRef]
  60. Lawson, R. Small Sample Confidence Intervals for the Odds Ratio. Commun. Stat. Simul. Comput. 2004, 33, 1095–1113. [Google Scholar] [CrossRef]
Pharmaceuticals 16 01541 g001
Figure 1. Cluster analysis based on lnROR of reported adverse event names for target opioids.
Figure 1. Cluster analysis based on lnROR of reported adverse event names for target opioids.
Pharmaceuticals 16 01541 g001
Pharmaceuticals 16 01541 g002
Figure 2. Flowchart for data analysis construction.
Figure 2. Flowchart for data analysis construction.
Pharmaceuticals 16 01541 g002
Table 1. Number of reported adverse events.
Table 1. Number of reported adverse events.
OpioidNumber of Reports
Oxycodone925,184
Morphine525,135
Fentanyl449,050
Codeine315,510
Hydromorphone259,911
Loperamide208,812
Methadone139,804
Tapentadol61,718
Pethidine49,948
Dihydrocodeine29,518
Remifentanil12,419
Number of reported adverse events for targeted µ-opioid receptor agonists in FAERS.
Table 2. Top 47 adverse events.
Table 2. Top 47 adverse events.
Adverse EventNumber of ReportsAverage lnRORROR
PAIN1,391,6760.04 1.04
DEATH1,319,2990.49 1.62
VOMITING1,245,2410.16 1.18
HYPOTENSION650,8880.12 1.12
CONSTIPATION607,2890.06 1.06
DRUG INTERACTION570,1340.29 1.33
SOMNOLENCE538,0910.37 1.45
CONFUSIONAL STATE511,9260.29 1.34
TOXICITY TO VARIOUS AGENTS448,3321.65 5.21
DRUG HYPERSENSITIVITY440,7630.53 1.70
OVERDOSE383,5781.36 3.88
LOSS OF CONSCIOUSNESS366,7520.21 1.23
HYPERHIDROSIS350,8400.34 1.40
RESPIRATORY FAILURE276,7410.14 1.15
CARDIAC ARREST263,6430.61 1.84
TACHYCARDIA256,0460.36 1.43
MALIGNANT NEOPLASM PROGRESSION221,2640.15 1.17
COMPLETED SUICIDE212,4320.50 1.65
AGITATION205,6090.39 1.48
BLOOD PRESSURE DECREASED203,2940.04 1.04
HALLUCINATION191,1230.31 1.36
BRADYCARDIA183,5470.09 1.10
LETHARGY167,8870.22 1.24
PULMONARY OEDEMA161,4260.35 1.41
DRUG ABUSE159,4611.73 5.66
INTENTIONAL PRODUCT MISUSE157,6430.49 1.63
OXYGEN SATURATION DECREASED148,1310.30 1.34
CARDIO-RESPIRATORY ARREST143,6790.66 1.93
COMA140,2760.60 1.82
DISORIENTATION134,1320.35 1.41
FOETAL EXPOSURE DURING PREGNANCY133,6080.21 1.23
HYPOXIA132,8510.35 1.42
DRUG WITHDRAWAL SYNDROME124,5921.01 2.75
DEPRESSED LEVEL OF CONSCIOUSNESS122,4980.69 2.00
DYSKINESIA116,3350.18 1.19
LIVER FUNCTION TEST ABNORMAL114,1530.05 1.05
MENTAL STATUS CHANGES113,5900.16 1.17
INTENTIONAL OVERDOSE112,6170.29 1.33
ANAPHYLACTIC REACTION111,0920.47 1.60
MEDICATION ERROR108,3770.48 1.62
DELIRIUM106,6170.85 2.35
URINARY RETENTION106,4560.09 1.09
EXPOSURE DURING PREGNANCY105,5840.24 1.27
RESTLESSNESS102,8560.26 1.30
ELECTROCARDIOGRAM QT PROLONGED100,5220.24 1.27
HEPATIC FAILURE100,4340.08 1.08
WRONG TECHNIQUE IN DRUG USAGE PROCESS100,2010.20 1.22
Top 47 adverse events with >150,000 reported cases and associated reporting odds ratio values for target opioids.
Table 3. Generation of a 2 × 2 contingency table and calculation of RORs.
Table 3. Generation of a 2 × 2 contingency table and calculation of RORs.
Adverse Event (+)Adverse Event (−)
Reports with
the suspected drugs		ab
All of reportscd
ROR = (a/b)/(c/d) = a × d/c × b.
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

Hirai, R.; Uesawa, Y. Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database. Pharmaceuticals 2023, 16, 1541. https://doi.org/10.3390/ph16111541

AMA Style

Hirai R, Uesawa Y. Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database. Pharmaceuticals. 2023; 16(11):1541. https://doi.org/10.3390/ph16111541

Chicago/Turabian Style

Hirai, Risako, and Yoshihiro Uesawa. 2023. "Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database" Pharmaceuticals 16, no. 11: 1541. https://doi.org/10.3390/ph16111541

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