Antiepileptic combination therapy with Stevens-Johnson syndrome and toxic epidermal necrolysis: Analysis of a Japanese pharmacovigilance database

Detection Algorithms for Simple Two-Group Comparisons Using Spontaneous Reporting Systems

Medical science has often used adult males as the standard to establish pathological conditions, their transitions, diagnostic methods, and treatment methods. However, it has recently become clear that sex differences exist in how risk factors contribute to the same disease, and these differences also exist in the efficacy of the same drug. Furthermore, the elderly and children have lower metabolic functions than adult males, and the results of clinical trials on adult males cannot be directly applied to these patients. Spontaneous reporting systems have become an important source of information for safety assessment, thereby reflecting drugs' actual use in specific populations and clinical settings. However, spontaneous reporting systems only register drug-related adverse events (AEs); thus, they cannot accurately capture the total number of patients using these drugs. Therefore, although various algorithms have been developed to exploit disproportionality and search for AE signals, there is no systematic literature on how to detect AE signals specific to the elderly and children or sex-specific signals. This review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

Contribution of aldehyde oxidase to methotrexate-induced hepatotoxicity: in vitro and pharmacoepidemiological approaches

BACKGROUND

Methotrexate (MTX) is partially metabolized by aldehyde oxidase (AOX) in the liver and its clinical impact remains unclear. In this study, we aimed to demonstrate how AOX contributes to MTX-induced hepatotoxicity in vitro and clarify the relationship between concomitant AOX inhibitor use and MTX-associated liver injury development using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).

METHODS

We assessed intracellular MTX accumulation and cytotoxicity using HepG2 cells. We used the FAERS database to detect reporting odds ratio (ROR)-based MTX-related hepatotoxicity event signals.

RESULTS

AOX inhibition by AOX inhibitor raloxifene and siRNA increased the MTX accumulation in HepG2 cells and enhanced the MTX-induced cell viability reduction. In the FAERS analysis, the ROR for MTX-related hepatotoxicity increased with non-overlap of 95% confidence interval when co-administered with drugs with higher Imax, u (maximum unbound plasma concentration)/IC50 (half-maximal inhibitory concentration for inhibition of AOX) calculated based on reported pharmacokinetic data.

CONCLUSION

AOX inhibition contributed to MTX accumulation in the liver, resulting in increased hepatotoxicity. Our study raises concerns regarding MTX-related hepatotoxicity when co-administered with drugs that possibly inhibit AOX activity at clinical concentrations.

Comparative study on the occurrence of adverse effects in the concomitant use of azathioprine and aldehyde oxidase inhibitors

OBJECTIVES

Aldehyde oxidase (AO) is a molybdenum-containing redox enzyme similar to xanthine oxidase that is involved in the thiopurine metabolism. This study investigated the effects of drug-drug interactions (DDIs) between azathioprine (AZA) and AO inhibitors on hematologic and hepatic disorders using the U.S. Food and Drug Administration Adverse Event Reporting System and the Japanese Adverse Drug Event Report database.

METHODS

The presence of DDI was assessed using the interaction signal scores (ISSs) calculated via the reporting odds ratios and 95% confidence intervals. The study used reports of 'azathioprine' as a suspect drug for adverse effects. AO inhibitors were selected based on previous in vitro reports.

RESULTS

Some drugs tested positive for ISSs in each database and type of adverse effect (hematologic or hepatic disorder) analysis. Among these drugs, chlorpromazine, clozapine, hydralazine, and quetiapine could inhibit AZA metabolism via AO, given the previously reported clinical blood concentration and inhibitory effects of each drug.

CONCLUSION

Concomitant use of AO inhibitors increased the signals for AZA-induced adverse effects. To date, no studies have evaluated the clinical importance of AO as a drug-metabolizing enzyme, and further in vitro and clinical research is needed to clarify the contribution of AO to the pharmacokinetics of thiopurines.

[Forty-three Years of Dedicated Efforts in Advancing Pharmacist Expertise: Aspiring to Become a Pharmacist and Cultivate Pharmacists Proficient in Recommending Prescription Medicines to Doctors]

When I graduated from university, my aim was to become a pharmacist capable of recommending prescription medicines to doctors and teaching others to do the same. To achieve this goal, I developed comprehensive curricula incorporating progressive educational tools such as problem-based learning and small group discussions. Subsequently, the effectiveness of these tools and curricula was evaluated, and the findings of these assessments were published in various peer-reviewed journal articles. Consequently, a body of evidence on the most effective ways to recommend prescription medicines to doctors was gradually established. This paper aims to summarize this comprehensive body of research spanning over 43 years, with the objective of highlighting the valuable insights gained thus far, identifying the best practice techniques, and exploring potential avenues for future research.

The role of antipsychotics and other drugs on the development and progression of neuroleptic malignant syndrome

Neuroleptic malignant syndrome (NMS) is a rare but serious and sometimes fatal complication in patients taking antipsychotic drugs, and its underlying mechanism still remains unclear. The pharmacotherapy for psychotic disorders is complicated and often involves a combination of two or more drugs, including drugs other than antipsychotics. In the present study, we used the Japanese Adverse Drug Event Report (JADER) database to broadly investigate the drugs associated with NMS, following their related pathways, as well as the drug-drug interactions (DDIs) in NMS. All analyses were performed using data from the JADER database from April 2004 to May 2022. Single-drug signals were evaluated using the reporting odds ratio (ROR) and proportional reporting ratio (PRR), and drug pathways were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG). DDIs were evaluated using the Ω shrinkage measure and Chi-square statistics models. All drugs associated with 20 or more NMS cases in the JADER database exhibited signals for NMS, including non-antipsychotics. Pathways associated with the drugs included the dopaminergic or serotonergic synapses related to antipsychotics. DDIs leading to NMS were confirmed for several drug combinations exhibiting single-drug signals. This study confirmed the significant association of various drugs, including non-psychotics, with NMS and suggested that various pathways related to these drugs may be involved in the progression of NMS. In addition, several combinations of these drugs were found to interact (DDI), increasing the risk of NMS, which suggests that appropriate caution should be taken when administering these drugs.

A Case of Toxic Epidermal Necrolysis Caused by Lamotrigine Combined With Valproic Acid and Literature Review

After suffering from frequent symptomatic seizures secondary to cerebral hemorrhage, a 58-year-old male patient was prescribed a one-time 50 mg dose of lamotrigine, which he took for a week. However, the patient's seizure symptoms were not controlled until a dosage of 500 mg of sodium valproate tablets was taken twice daily, which ultimately resolved his seizures. Unfortunately, about two weeks after the combination, the patient developed a rash. Nine days later, the patient developed new blisters, necrotizing epidermal desquamation, and lesions over 80% of their body surface area. This was diagnosed as toxic epidermal necrolysis (TEN) resulting from the combination of lamotrigine and sodium valproate. The sodium valproate and lamotrigine were discontinued and treated symptomatically for about one month. The patient's condition improved as the fatal rash gradually subsided. However, after the onset of TEN, unexpected psychiatric symptoms such as poor sleep, less than four hours of sleep, irritability, paranoia, crying, fear of rash recurrence, and suspicious hallucinations and delusions emerged in the patient. Surprisingly, after discontinuation of lamotrigine and sodium valproate due to the rash, the patient did not experience any further seizures.

Identification of novel signal of clobazam-associated drug reaction with eosinophilia and systemic symptoms syndrome: A disproportionality analysis

OBJECTIVES

Clobazam is a well-known benzodiazepine used as an anti-anxiety drug as well as an anti-epileptic, particularly for patients who are not responding to first-line treatments. Recent case reports have indicated the association of clobazam with drug reaction with eosinophilia systemic symptoms syndrome (DRESS Syndrome). However, DRESS syndrome is not known to be associated with clobazam. Thus, the main objective of the current study was to identify the potential signal of clobazam-associated DRESS Syndrome.

MATERIALS & METHODS

US FDA Adverse event reporting system (US FAERS), pharmacovigilance data 2004Q1-2021Q3 was extracted using OpenVigil 2.1-MedDRA-v24. The Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR) with a Chi-Square value (95% confidence interval), and number of cases (≥3) were used as disproportionality analysis parameters.

RESULTS

A total of 141 drug-event combinations were reported and results of disproportionality analysis indicate the positive signal of DRESS syndrome with clobazam. The signal strength was decreased after removing the cases of concomitantly administered drugs (phenytoin, levetiracetam, and valproic acid); however, the association of clobazam with DRESS syndrome remains statistically significant. The subgroup analysis results have shown a greater number of cases in the age group (18-64 years) as compared to other age groups whereas the number of cases in the male and female groups is almost similar.

CONCLUSION

The DRESS syndrome is identified as a novel signal with clobazam. However, further causality assessment is required.

Angioedema Caused by Drugs That Prevent the Degradation of Vasoactive Peptides: A Pharmacovigilance Database Study

Angioedema results from the decreased degradation of vasoactive peptides such as substance P and bradykinin. In this study, we sought to clarify whether dipeptidyl peptidase-4 (DPP-4) and angiotensin-converting enzyme (ACE) inhibitors that suppress the degradation of substance P and bradykinin are involved in angioedema onset. We calculated information coefficients (ICs) by performing a disproportionality analysis to evaluate DPP-4/ACE inhibitor-induced angioedema using the Japanese Adverse Drug Event Report (JADER) database. No angioedema signals were detected for DPP-4 inhibitors; however, a signal was detected for ACE inhibitors (IC: 2.42, 95% confidence interval (CI): 2.19 to 2.65). Of the patients treated with DPP-4 inhibitors, four developed drug-induced angioedema in combination with ACE inhibitors, and all were taking vildagliptin. Signals were detected for enalapril (IC: 2.39, 95% CI: 2.06 to 2.71), imidapril (IC: 2.83, 95% CI: 2.38 to 3.27), lisinopril (IC: 2.28, 95% CI: 1.55 to 3.00), temocapril (IC: 1.35, 95% CI: 0.29 to 2.40), and trandolapril (IC: 1.57, 95% CI: 0.19 to 2.95). Both inhibitors inhibited the degradation of substance P and bradykinin and were thus expected to cause angioedema. However, no signal of angioedema was detected with the DPP-4 inhibitors, in contrast to some ACE inhibitors. This study found that ACE inhibitors and DPP-4 inhibitors, which inhibit the degradation of substance P and bradykinin, tended to have different effects on the onset of angioedema in clinical practice.

Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions

The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.

Toxic epidermal necrosis associated with phenobarbitone: a case report and brief review of the literatures

BACKGROUND

Toxic epidermal necrolysis (TEN)/Stevens-Johnson syndrome (SJS) is the spectrum of severe, acute, mucocutaneous, T-cell mediated delayed type IV hypersensitivity reaction and universally related to different drugs. Phenobarbitone is known to cause hypersensitivity reactions with benign pattern; ranging from a mild to moderate rashes but not life-threatening reactions such as TEN/SJS.

CASE REPORT

We report a 14-year-old asthmatic male patient admitted to a local hospital for an acute exacerbation of asthma, after he presented with shortness of breath, cough, and fever. He was treated with bronchodilator and antibiotics. On subsequent days, the patient developed new onset generalized tonic clonic seizure in the hospital for which he was started on phenobarbitone of 100 mg twice daily. Two weeks after initiation of phenobarbitone, the patient developed extensive blistering skin eruptions; which subsequently exfoliated unevenly. Associated with the hypersensitivity skin reaction, the patient reported low grade fever, sore throat, and dysphagia. The exfoliation also involved oral and conjunctival mucosa; with estimated 65% body surface area involvement. The laboratory investigations were relevant for mild leucocytosis, prolonged prothrombin time, and reduced albumin. Phenobarbitone was discontinued and replaced with clonazepam; and the patient was managed with fluids replacement, IV antibiotics, twice daily wound care, analgesics, and naso gastric tube feeding. On subsequent days the patients' clinical condition started improving; the skin lesion also started to heal and exfoliate in most of the affected skin surface areas, and the patient was discharged improved after ten days of intensive care unit.

CONCLUSION

In summary, the present case describes, a 14-years-old young child with history of asthma and seizure disorder; and developed toxic epidermal necrosis following exposure to Phenobarbitone. This case also highlighted the better prognosis observed in pediatric population with TEN.

Differences in detected safety signals between benzodiazepines and non-benzodiazepine hypnotics: pharmacovigilance study using a spontaneous reporting system

Introduction: In recent years, there has been an increasing number of people who feel sleep-deprived owing to sudden changes in the social environment. Patients prescribed benzodiazepine-based hypnotics (BZ drugs) also develop movement disorder action and memory disorders as adverse events (AEs), and they have further problems such as dependency and tolerance because of long-term use. Therefore, the use of non-benzodiazepine-based hypnotics (Z-drugs) is recommended for patients with insomnia. However, as AEs have also been reported for Z-drugs, it is important to identify these when switching hypnotics. Methods: To understand AEs to be noted when switching from BZ drugs to Z-drugs, we evaluated the differences in AEs developed by both these drugs using volcano plots and safety signals. For this, data registered in the Japanese Adverse Drug Event Report database were used. Results: The volcano plot and safety signals revealed six characteristic Z-drug-induced AEs. Parasomnias (ln odds ratio [OR]: 3.28, -log P: 4.34, proportional reporting ratio [PRR]: 23.47, χ ²: 309.27), Cortical dysfunction NEC (ln OR: 2.76, -log P: 4.34, PRR: 3.62, χ ²: 16.14), and Psychiatric symptoms NEC (ln OR: 2.66, -log P: 2.18, PRR: 2.51, χ ²: 6.63) were detected only in Z-drugs, and safety signals of Suicidal and self-injurious behaviour, Deliria, and Overdoses NEC were also detected with BZ drugs. However, the strength of safety signals was much higher with the Z-drugs. Conclusion: AEs related to falls and bone fractures are expected to be more strongly onset in BZ drugs than in Z-drugs, which are said to have less muscle relaxant action. However, there was no particularly significant difference in this parameter between the two drug classes. Understanding the difference between these AEs of Z-drugs and BZ drugs is important for the proper use of hypnotics.

Subset Analysis for Screening Drug-Drug Interaction Signal Using Pharmacovigilance Database

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions. Although there are several algorithms for detecting drug-drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug-drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; "hypothetical" true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden's index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug-drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug-drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.