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Mariko Kozaru, Hiroko Kambara, Akari Higuchi, Tatsuki Kagatsume, Keiko Hosohata Education and Research Center for Clinical Pharmacy, Faculty of Pharmacy, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, 569-1094, JapanCorrespondence: Keiko Hosohata, Education and Research Center for Clinical Pharmacy, Faculty of Pharmacy, Osaka Medical and Pharmaceutical University, 4-20-1 Nasahara, Takatsuki, Osaka, 569-1094, Japan, Tel +81-72-690-1271, Fax +81-72-690-362, Email keiko.hosohata@ompu.ac.jpBackground: The P2Y12 receptor inhibitors clopidogrel and prasugrel are widely used. Clopidogrel and prasugrel have different metabolic pathways, but whether their adverse event (AE) profiles differ significantly is unclear.Objective: This study aimed to compare the possible AEs induced by clopidogrel and prasugrel and to assess the rank-order of their AEs submitted to a spontaneous reporting database.Materials and Methods: Data were extracted from the Japanese Adverse Drug Event Report database (JADER). Reports of AEs associated with clopidogrel and prasugrel were analyzed to calculate the reporting odds ratios (RORs) and 95% confidence intervals (CIs).Results: Based on 5869 reports for clopidogrel (69.6%, men) and 513 reports for prasugrel (74.1%, men), 703 and 135 different AEs were identified, respectively. Bleeding complications including hemorrhage were commonly reported for both clopidogrel and prasugrel. As for AEs related to clopidogrel, unexpected AEs such as interstitial lung disease (227 reports; ROR, 1.77; 95% CI, 1.49– 2.10), abnormal hepatic function (137 reports; ROR, 1.27; 95% CI, 1.07– 1.51), and hepatocellular injury (96 reports; ROR, 120.0; 95% CI, 94.9– 151.8) ranked at relatively high positions based on the number of occurrences, unlike prasugrel.Conclusion: This analysis of the national pharmacovigilance database highlights distinct AE profiles for clopidogrel and prasugrel. Unexpected AEs associated with clopidogrel were identified, providing valuable insights for clinical monitoring and patient safety.Keywords: P2Y12 platelet inhibitor, pharmacovigilance, adverse events, reporting odds ratio, signal detection, spontaneous reporting system

Introduction: For assessing drug safety using spontaneous reporting system databases, quantitative measurements, such as proportional reporting rate (PRR) and reporting odds ratio (ROR), are widely employed to assess the relationship between a drug and a suspected adverse drug reaction (ADR). The databases contain numerous ADRs, and the quantitative measurements need to be calculated by performing the analysis multiple times for each ADR. We proposed a novel, simple, and easy-to-implement method to estimate the PRR and ROR of multiple ADRs in a single analysis using a generalized mixedeffects model for signal detection. Methods: The proposed method simultaneously analyzed the association between any drug and numerous ADRs, as well as estimated the PRR and ROR for a specific combination of drugs and suspected ADRs. Furthermore, the proposed method was applied to detect drug-drug interactions associated with the concurrent use of two or more drugs. Results and discussion: In our simulation studies, the false-positive rate and sensitivity of the proposed method were similar to those of the traditional PRR and ROR. The proposed method detected known ADRs when applied to the Food and Drug Administration Adverse Event Reporting System database. As an important advantage, the proposed method allowed the simultaneous evaluation of several ADRs using multiple drugs. [ABSTRACT FROM AUTHOR]

Urinary tract infection (UTI) and pyelonephritis cause urosepsis, which can become life threating. Sodium‐dependent glucose cotransporter 2 (SGLT2) inhibitors, a class of oral anti‐diabetic drugs, may increase the risk of UTI and pyelonephritis, as observed from their mechanism of action. Patients with diabetes receiving SGLT2 inhibitors are often concomitantly administered other antidiabetic, antidyslipidemic, or antihypertensive drugs. To determine if drug–drug interactions (DDIs) between SGLT2 inhibitors and these medications increased the risk of UTI and pyelonephritis, we analyzed the Food and Drug Administration Adverse Event Reporting System (FAERS) database. We applied Norén's and Gosho's methods for detecting DDIs. FAERS contains records describing 14 526 398 patient characteristics, 54 290 663 drug properties, and 47 252 416 reactions/events. We found 23 drug combinations that could induce UTI and pyelonephritis, such as SGLT2 inhibitors administered with dipeptidyl peptidase‐4 inhibitors, thiazolidinediones, glinides, statins, angiotensin II receptor blockers, and calcium channel blockers. Combination therapy with the drugs detected in our analyses would show potential interactions that could result in UTI and pyelonephritis in patients with diabetes mellitus. However, owing to various limitations, these results must be confirmed by additional analyses. [ABSTRACT FROM AUTHOR]

Due to the high intensity of the COVID‐19 vaccination campaigns and heightened attention for safety issues, the number of spontaneous reports has surged. In the Netherlands, pharmacovigilance centre Lareb has received more than 100 000 reports on adverse events following immunization (AEFI) associated with Covid‐19 vaccination. It is tempting to interpret absolute numbers of reports of AEFIs in signal detection. Signal detection of spontaneously reported adverse drug reactions has its origin in case‐by‐case analysis, where all case reports are assessed by clinically qualified assessors. The concept of clinical review of cases—even if only a few per country—followed by sharing concerns of suspicions of potential adverse reactions again proved the strength of the system. Disproportionality analysis can be useful in signal identification, and comparing reported cases with expected based on background incidence can be useful to support signal detection. However, they cannot be used without an in‐depth analysis of the underlying clinical data and pharmacological mechanism. This in‐depth analysis has been performed, and is ongoing, for the signal of vaccine‐induced immune thrombotic thrombocytopenia (VITT) in relation to the AstraZeneca and Janssen Covid‐19 vaccines. Although not frequency or incidence rates, reporting rates can provide an impression of the occurrence of the event. But the unknown underreporting should also be part of this context. To quantify the incidence rates, follow‐up epidemiological studies are needed. [ABSTRACT FROM AUTHOR]

Rhabdomyolysis is one of the most concerning complications of antidyslipidemic drugs. Most patients with dyslipidemia take multiple medications. Our objective was to explore which two‐drug combinations lead to a higher risk of rhabdomyolysis. We analyzed data from the Japanese Adverse Drug Event Report (JADER) database between April 2004 and September 2017. The primary outcome was the report of rhabdomyolysis. We assessed the risk of rhabdomyolysis for the two‐drug concomitant use of antidyslipidemic drugs (statin, fibrate, and ezetimibe) with antihypertensive and antidiabetic medications. The Norén and Gosho methods were used for detecting two‐drug interactions. The JADER contained 468 292 records for patient characteristics, 2 973 172 drug information records, and 741 016 adverse reactions records. Rhabdomyolysis was reported in 5 017 patients. Concomitant use of pravastatin/fenofibrate, simvastatin/mefruside, fluvastatin/temocapril, bezafibrate/temocapril, bezafibrate/spironolactone, bezafibrate/metoprolol, bezafibrate/losartan, fluvastatin/mitiglinide, or fenofibrate/glibenclamide was detected as the signal of rhabdomyolysis in this analysis. Combination therapy with the drugs listed above has the potential of drug–drug interactions that could result in rhabdomyolysis in patients with dyslipidemia. [ABSTRACT FROM AUTHOR]

Abstract: Hypoglycemia is the most important complication of antidiabetic medications. Most patients with diabetes mellitus take multiple medications. In this study, we explored clinical drug‐drug interactions that result in hypoglycemia by analyzing the Japanese Adverse Drug Event Report (JADER) database. The primary outcome was the report of hypoglycemia. The Norén and Gosho methods, which quantitatively measure the discrepancy between the observed and expected number of adverse events under the combination of 2 drugs, were used as the criteria for detecting drug‐drug interactions. The JADER database contained patient characteristics with 468 292 records, drug information with 2 973 172 records, and adverse reactions with 741 016 records. We noted that hypoglycemia was reported in 6208 patients. Concomitant use of linagliptin/glibenclamide, sitagliptin/glibenclamide, sitagliptin/buformin, exenatide/voglibose, mitiglinide/perindopril, repaglinide/barnidipine, alogliptin/cilnidipine, teneligliptin/barnidipine, teneligliptin/urapidil, exenatide/candesartan, voglibose/barnidipine, voglibose/guanabenz, or exenatide/tocopherol was noted using the 2 criteria for drug‐drug interactions. Concomitant use of teneligliptin and barnidipine produced the highest values for the 2 criteria. The second‐and third‐highest values were observed for teneligliptin/urapidil and exenatide/candesartan combinations, respectively. The same analysis was performed using the US Food and Drug Administration Adverse Event Reporting System database to assess the validity of the result from the JADER database. The concomitant use of sitagliptin/buformin, exenatide/voglibose, repaglinide/barnidipine, teneligliptin/barnidipine, teneligliptin/urapidil, exenatide/candesartan, voglibose/barnidipine, voglibose/guanabenz, or exenatide/tocopherol was detected in both the JADER and Food and Drug Administration Adverse Event Reporting System databases. Combination therapy with the drugs listed above would show potential interactions that could result in hypoglycemia in patients with diabetes mellitus. [ABSTRACT FROM AUTHOR]

Inhibition of serotonin uptake in platelets seems to be the crucial mechanism underlying SSRI-associated haemorrhages. This effect is also present in antidepressants featuring non-selective serotonin reuptake inhibition (non-SSRI). Impact of selectivity of serotonin reuptake and/or affinity to the serotonin reuptake transporter on the bleeding risk have not yet been studied sufficiently. We retrieved country- and SSRI-/non-SSRI-specific data from the Uppsala Monitoring Centre and used a case/non-case approach to calculate substance-specific reporting odds ratios (ROR) to evaluate the statistical association of treatment with SSRI/non-SSRI and haemorrhages. Country-specific analysis revealed no clear trends towards an increased risk of bleeding related to particular agents of group SSRI/non-SSRI (sporadically ROR>1 for citalopram, duloxetine, escitalopram, fluvoxamine, paroxetine, sertraline, St. John's wort). There was a clear trend in the total dataset towards a “reduced protective effect” (suggested by ROR<1) on the development of haemorrhages with agents featuring comparatively high affinity to the 5-HTT and/or selective serotonin reuptake inhibition (as with escitalopram, citalopram, duloxetine or venlafaxine) in comparison to agents with lower affinity or non-selective serotonin reuptake inhibition (as with mirtazapine or doxepin). Comparison of group-specific aggregated data (SSRI vs. non-SSRI) revealed significant differences regarding the “protective effect” on the development of haemorrhages between groups SSRI vs. non-SSRI in favour of non-SSRI in nearly all countries as well as in the total dataset. Our findings provide preliminary evidence that agents with increased affinity to the 5-HTT and/or selective serotonin reuptake inhibition may be associated with an increased risk of bleeding. [ABSTRACT FROM AUTHOR]

ABSTRACT Purpose Progressive multifocal leukoencephalopathy (PML) is a rare demyelinating disease of the central nervous system that has been reported as rare adverse drug reaction (ADR) of immunosuppressive drugs. We aimed to study signals of PML for immunosuppressants using a disproportionality analysis of spontaneous adverse event reports. Methods Within the US Adverse Event Reporting System, we analyzed all reports of ADRs submitted to the US Food and Drug Administration between January 1, 2004 and September 30, 2010. We used univariate and multivariate logistic regression analysis to calculate reporting odds ratios with 95% confidence intervals of PML for immunosuppressants according to the Anatomical Therapeutic Chemical classification system (L04), rituximab and cyclophosphamide compared to all other drugs. Results We identified 635 PML cases in a total of 1 978 706 patients eligible for analysis. Altogether, 21 out of 36 analyzed immunosuppressants were reported at least once with PML. In the univariate analyses, we found a signal for 11 of these drugs (azathioprine, cyclosporine, cyclophosphamide, efalizumab, leflunomide, methotrexate, mycophenolate mofetil, natalizumab, rituximab, tacrolimus and sirolimus). In the multivariate analysis, the signal was no longer present for sirolimus, leflunomide and methotrexate. Discussion Our study revealed signals of PML for a substantial number of immunosuppressants, including some drugs less considered so far as a risk factor of PML, especially when used for the treatment of autoimmune disorders. These drugs and possible interactions between different immunosuppressants should be studied more closely in future studies. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: For all medications, there is a trade-off between benefits and potential for harm. It is important for patient safety to detect drug-event combinations and analyze by appropriate statistical methods. Mefloquine is used as chemoprophylaxis for travelers going to regions with known chloroquine-resistant Plasmodium falciparum malaria. As such, there is a concern about serious adverse events associated with mefloquine chemoprophylaxis. The objective of the present study was to assess whether any signal would be detected for the serious adverse events of mefloquine, based on data in clinicoepidemiological studies. Materials and methods: We extracted data on adverse events related to mefloquine chemoprophylaxis from the two published datasets. Disproportionality reporting of adverse events such as neuropsychiatric events and other adverse events was presented in the 2 × 2 contingency table. Reporting odds ratio and corresponding 95% confidence interval [CI] data-mining algorithm was applied for the signal detection. The safety signals are considered significant when the ROR estimates and the lower limits of the corresponding 95% CI are ⩾2. Results: Two datasets addressing adverse events of mefloquine chemoprophylaxis (one from a published article and one from a Cochrane systematic review) were included for analyses. Reporting odds ratio 1.58, 95% CI: 1.49-1.68 based on published data in the selected article, and 1.195, 95% CI: 0.94-1.44 based on data in the selected Cochrane review. Overall, in both datasets, the reporting odds ratio values of lower 95% CI were less than 2. Conclusion: Based on available data, findings suggested that signals for serious adverse events pertinent to neuropsychiatric event were not detected for mefloquine. Further studies are needed to substantiate this. [ABSTRACT FROM AUTHOR]

Pharmacovigilance systems aim at early detection of adverse effects of marketed drugs. They maintain large spontaneous reporting databases for which several automatic signaling methods have been developed. One limit of those methods is that the decision rules for the signal generation are based on arbitrary thresholds. In this article, we propose a new signal-generation procedure. The decision criterion is formulated in terms of a critical region for the P-values resulting from the reporting odds ratio method as well as from the Fisher's exact test. For the latter, we also study the use of mid-P-values. The critical region is defined by the false discovery rate, which can be estimated by adapting the P-values mixture model based procedures to one-sided tests. The methodology is mainly illustrated with the location-based estimator procedure. It is studied through a large simulation study and applied to the French pharmacovigilance database. [ABSTRACT FROM AUTHOR]