scholarly journals Verification of the “Upward Variation in the Reporting Odds Ratio Scores” to Detect the Signals of Drug–Drug Interactions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1531
Author(s):  
Yoshihiro Noguchi ◽  
Shunsuke Yoshizawa ◽  
Keisuke Aoyama ◽  
Satoaki Kubo ◽  
Tomoya Tachi ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Odds Ratio ◽  
Drug Event ◽  
Stevens Johnson Syndrome ◽  
Reporting Odds Ratio ◽  
Positive Signal ◽  
False Positive Signal ◽  
Johnson Syndrome ◽  
Drug Drug Interaction ◽  
Score Model

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.

scholarly journals Correlation between drug–drug interaction-induced Stevens–Johnson syndrome and related deaths in Taiwan

2016 ◽  
Vol 24 (2) ◽  
pp. 427-432 ◽  
Author(s):  
Fu-Jen Cheng ◽  
Fei-Kai Syu ◽  
Kuo-Hsin Lee ◽  
Fu-Cheng Chen ◽  
Chien-Hung Wu ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Stevens Johnson Syndrome ◽  
Johnson Syndrome ◽  
Drug Drug Interaction

Risk of Hematologic Events With Coadministration of Methotrexate and the Breast Cancer Resistance Protein Inhibitor Febuxostat

2021 ◽  
pp. 106002802110557
Author(s):  
Satoru Mitsuboshi ◽  
Takahiro Niimura ◽  
Masaya Kanda ◽  
Shunsuke Ishida ◽  
Yoshito Zamami ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Interaction ◽  
Breast Cancer Resistance Protein ◽  
Multiple Logistic Regression Analysis ◽  
Drug Event ◽  
Cancer Resistance ◽  
Logistic Analysis ◽  
Increased Risk ◽  
Resistance Protein ◽  
Drug Drug Interaction

Background: The breast cancer resistance protein (BCRP) is a key drug transporter found in the liver, kidney, central nervous system, and gastrointestinal tract. Due to the wide expression of BCRP, interactions of other drugs with methotrexate (MTX) may differ in oral and intravenous MTX users, and understanding of these interactions may be useful in preventing severe adverse events. Febuxostat, a urate-lowering drug, inhibits BCRP. Objective: The objective of this study was to clarify the differences in the drug-drug interaction profiles of oral and intravenous methotrexate, associated with BCRP. Methods: We analyzed the Japanese Adverse Drug Event Report database and compared the frequency of hematologic events in patients taking oral and intravenous MTX, with or without the concomitant use of febuxostat or allopurinol. Hematologic events were defined as pancytopenia and neutropenia. Multiple logistic regression analysis was then used to identify the risk factors for hematologic events in oral and intravenous MTX users. Results: We identified 8 453 oral and 810 intravenous MTX users with 546 and 126 cases of hematologic events, respectively. Compared with those not using febuxostat, a disproportionate number of hematologic events was observed in intravenous MTX users concomitantly using febuxostat ( P < 0.01). The multivariate logistic analysis of intravenous MTX users showed that hematologic events were significantly associated with febuxostat use ( P < 0.01) and age ≥ 60 years ( P < 0.01). Conclusion and Relevance: Our findings suggest that patients being treated with intravenous MTX who concomitantly use febuxostat may be at an increased risk of hematologic events, presumably due to BCRP-mediated drug-drug interaction.

scholarly journals Pharmacovigilance Assessment of Drug-Induced Acute Pancreatitis Using a Spontaneous Reporting Database

2019 ◽  
Vol 38 (6) ◽  
pp. 487-492 ◽  
Author(s):  
Iku Niinomi ◽  
Keiko Hosohata ◽  
Saki Oyama ◽  
Ayaka Inada ◽  
Tomohito Wakabayashi ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Odds Ratio ◽  
Spontaneous Reporting ◽  
Adverse Drug Events ◽  
Drug Event ◽  
Reporting Odds Ratio ◽  
Event Report ◽  
Disproportionality Analysis ◽  
Drug Induced ◽  
Spontaneous Reporting Database

Background: Acute pancreatitis (AP) is associated with risks of morbidity and mortality. The incidence of AP recently increased compared to that traditionally reported in the literature. Objective: The purpose of this study was to evaluate the possible association between AP and drugs using the Japanese Adverse Drug Event Report (JADER) database, which is a spontaneous reporting database of adverse drug events. Methods: Adverse event reports submitted to the JADER database between 2004 and 2017 were analyzed. Disproportionality analysis was performed by calculating the reporting odds ratio (ROR) with 95% confidence intervals for signal detection. Results: A total of 3,443 reports (0.17% of all adverse events) were identified as drug-induced AP, in which 431 different drugs were involved. Acute pancreatitis was frequently reported in men (58.5%) in their 60s (19.1%); 40.6% developed AP within 4 weeks after the treatment. Among the most frequently reported drugs, signals were detected for prednisolone, ribavirin, sitagliptin, mesalazine, tacrolimus, and l-asparaginase, which are well-known causes of AP. Telaprevir, donepezil, and ustekinumab also generated signals. As for drugs with high RORs, l-asparaginase and alogliptin were noteworthy. Conclusion: Most of the identified drugs were already known to induce AP, but the likelihood of the reporting of AP varied among the drugs. Our results should raise physicians’ awareness of drugs associated with AP, but further investigation of these medications is warranted.

HLA-A*24:07 as a potential biomarker for carbamazepine-induced Stevens–Johnson syndrome/toxic epidermal necrolysis in Filipino patients

2021 ◽  
Author(s):  
Francis Capule ◽  
Pramote Tragulpiankit ◽  
Surakameth Mahasirimongkol ◽  
Jiraphun Jittikoon ◽  
Nuanjun Wichukchinda ◽  
...  
Keyword(s):  
Toxic Epidermal Necrolysis ◽  
Medical Records ◽  
Odds Ratio ◽  
Genomic Dna ◽  
Case Control Study ◽  
Case Control ◽  
Stevens Johnson Syndrome ◽  
Johnson Syndrome ◽  
Potential Biomarker ◽  
Control Study

Aim: A case-control study was conducted in Filipino patients to determine the association between HLA alleles and carbamazepine-induced Stevens–Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). Materials & methods: A retrospective review of medical records and data collection were performed. A total of 10 carbamazepine-induced SJS/TEN cases and 40 tolerant controls were recruited. Genomic DNA extracted from saliva samples was genotyped. Statistical analysis was done. Results: The HLA-B75 serotype (p = 0.003; odds ratio [OR] = 13.8; 95% CI = 2.5–76.8), HLA-B*15:21 (p = 0.041; OR = 4.7; 95% CI = 1.1–20.8) and HLA-A*24:07 (p = 0.032; OR = 6; 95% CI = 1.2–30.7) were significantly associated with carbamazepine-induced SJS/TEN. Conclusion: The HLA-B75 serotype, HLA-B*15:21 or HLA-A*24:07 may be used for pharmacogenetic screening prior to prescribing carbamazepine in Filipinos.

scholarly journals Associations Between Stevens–Johnson Syndrome and Infection: Overview of Pharmacoepidemiological Studies

2021 ◽  
Vol 8 ◽  
Author(s):  
Takuya Imatoh ◽  
Yoshiro Saito
Keyword(s):  
Risk Factor ◽  
Immune System ◽  
Medical Information ◽  
Adverse Drug Reaction Reporting ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Stevens Johnson Syndrome ◽  
Johnson Syndrome ◽  
Spontaneous Adverse Drug Reaction

Stevens–Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are classified as type B adverse drug reactions, and are severe, potentially fatal rare disorders. However, the pathogenesis of SJS/TEN is not fully understood. The onset of SJS/TEN is triggered by the immune system in response to antigens with or by drugs. As activation of the immune system is important, infection could be a risk factor for the onset of SJS/TEN. Based on the hypothesis that infections induce the onset of SJS/TEN, we conducted pharmacoepidemiological investigations using two spontaneous adverse drug reaction reporting databases (Japanese Adverse Drug Event Report database and Food and Drug Administration Adverse Event Reporting System) and Japanese medical information database. These data suggest that infection could be a risk factor for the development of SJS/TEN. In this mini-review, we discuss the association between infection and the development of SJS/TEN.

scholarly journals Potential drug–drug interactions and associated factors among hospitalized cardiac patients at Jimma University Medical Center, Southwest Ethiopia

2019 ◽  
Vol 7 ◽  
pp. 205031211985735 ◽  
Author(s):  
Netsanet Diksis ◽  
Tsegaye Melaku ◽  
Desta Assefa ◽  
Andualem Tesfaye
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Hospital Stay ◽  
Prevalence Rate ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Cardiac Patients ◽  
Potential Drug ◽  
Drug Drug Interaction

Background: Concomitant use of several drugs for a patient is often imposing increased risk of drug–drug interactions. Drug–drug interactions are a major cause for concern in patients with cardiovascular disorders due to multiple co-existing conditions and the wide class of drugs they receive. This study is aimed to assess the prevalence of potential drug–drug interactions and associated factors among hospitalized cardiac patients at medical wards of Jimma University Medical Center, Southwest Ethiopia. Methods: A hospital-based prospective observational study was conducted among hospitalized cardiac adult patients based on the inclusion criteria. Patient-specific data were collected using structured data collection tool. Potential drug–drug interaction was analyzed using Micromedex 3.0 DRUG-REAX® System. Data were analyzed using statistical software package, version 20.0. To identify the independent predictors of potential drug–drug interaction, multiple stepwise backward logistic regression analysis was done. Statistical significance was considered at a p-value < 0.05. Written informed consent from patients was obtained and the patients were informed about confidentiality of the information obtained. Results: Of the total 200 patients, majority were male (52.50%) and with a mean(±standard deviation) age of 42.54(±7.89) years. Out of 673 patients’ prescriptions analyzed, 521 prescriptions comprised potential drug interactions and it was found that 967 drug interactions were present. The prevalence rate of potential drug–drug interactions among the study unit was 4.83 per patient and 1.44 per prescription regardless of the severity during their hospital stay. Overall the prevalence rate of potential drug interactions was 74.41%. Older age (adjusted odds ratio (95% confidence interval): 1.067 (2.33–27.12), p = 0.049), long hospital stay (⩾7 days) (adjusted odds ratio (95% confidence interval): 2.80 (1.71–4.61), p = 0.024), and polypharmacy (adjusted odds ratio (95% confidence interval): 1.64 (0.66–4.11), p = 0.041) were independent predictors for the occurrence of potential drug–drug interactions. Conclusion: This study demonstrated a high prevalence of potential DIs among hospitalized cardiac patients in medical wards due to the complexity of pharmacotherapy. The prevalence rate is directly related to age, number of prescribed drugs, and length of hospital stay. Pharmacodynamic drug–drug interaction was the common mechanism of drug–drug interactions. Therefore, close monitoring of hospitalized patients is highly recommended.

scholarly journals Safety of BRAF+MEK Inhibitor Combinations: Severe Adverse Event Evaluation

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1650
Author(s):  
Tomer Meirson ◽  
Nethanel Asher ◽  
David Bomze ◽  
Gal Markel
Keyword(s):  
Adverse Event ◽  
Adverse Event Reporting System ◽  
Mek Inhibitor ◽  
Adverse Event Reporting ◽  
Stevens Johnson Syndrome ◽  
Reporting Odds Ratio ◽  
Serious Adverse Events ◽  
Johnson Syndrome ◽  
Increased Risk ◽  
Melanoma Patients

Aim: The selective BRAF and MEK inhibitors (BRAFi+MEKi) have substantially improved the survival of melanoma patients with BRAF V600 mutations. However, BRAFi+MEKi can also cause severe or fatal outcomes. We aimed to identify and compare serious adverse events (sAEs) that are significantly associated with BRAFi+MEKi. Methods: In this pharmacovigilance study, we reviewed FDA Adverse Event Reporting System (FAERS) data in order to detect sAE reporting in patients treated with the combination therapies vemurafenib+cobimetinib (V+C), dabrafenib+trametinib (D+T) and encorafenib+binimetinib (E+B). We evaluated the disproportionate reporting of BRAFi+MEKi-associated sAEs. Significant associations were further analyzed to identify combination-specific safety signals among BRAFi+MEKi. Results: From January 2018 through June 2019, we identified 11,721 sAE reports in patients receiving BRAFi+MEKi. Comparison of BRAFi+MEKi combinations demonstrates that skin toxicities, including Stevens–Johnson syndrome, were disproportionally reported using V+C, with an age-adjusted reporting odds ratio (adj. ROR) of 3.4 (95%CI, 2.9–4.0), whereas fever was most significantly associated with D+T treatment with an adj. ROR of 1.9 (95%CI, 1.5–2.4). Significant associations using E+B treatment include peripheral neuropathies (adj. ROR 2.7; 95%CI, 1.2–6.1) and renal disorders (adj. ROR 4.1; 95%CI, 1.3–12.5). Notably, we found an increase in the proportion of Guillain–Barré syndrome reports (adj. ROR 8.5; 95%CI, 2.1–35.0) in patients administered E+B. Conclusion: BRAFi+MEKi combinations share a similar safety profile attributed to class effects, yet concomitantly, these combinations display distinctive effects that can dramatically impact patients’ health. Owing to the limitations of pharmacovigilance studies, some findings warrant further validation. However, the possibility of an increased risk for these events should be considered in patient care.

scholarly journals Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Pharmaceutics ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 762 ◽  
Author(s):  
Yoshihiro Noguchi ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Signal Detection ◽  
Predictive Value ◽  
Drug Event ◽  
Detection Accuracy ◽  
Analysis Model ◽  
Subset Analysis ◽  
Pharmacovigilance Database ◽  
Drug Drug Interaction

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.

scholarly journals Drug-Drug interaction among admitted patients at primary, district and referral hospitals’ medical wards in East Gojjam Zone, Amhara Regional State, Ethiopia

2021 ◽  
Vol 9 ◽  
pp. 205031212110350
Author(s):  
Zenaw Tessema ◽  
Desalegn Yibeltal ◽  
Muluken Wubetu ◽  
Bekalu Dessie ◽  
Yalew Molla
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Potential Drug ◽  
Comorbid Conditions ◽  
Regional State ◽  
Referral Hospitals ◽  
Drug Drug Interaction ◽  
Medical Wards

Objectives: This study was aimed to assess the type, prevalence, characteristics of drug interaction and factors associated from admitted patients in medical wards at primary, district and referral hospitals in East Gojjam Zone, Amhara Regional State, Ethiopia. Methods: A facility-based retrospective cross-sectional study design was conducted among admitted patients in medical wards at different hospitals of East Gojjam Zone from September 2019 to February 2020. Patient-specific data were extracted from patient medical prescription papers using a structured data collection tool. Potential drug–drug interaction was identified using www.drugs.com as drug–drug interaction checker. Data were analyzed using SPSS version 23.0. To identify the explanatory predictors of potential drug–drug interaction, logistic regression analysis was done at a statistical significance level of p-value < 0.05. Results: Of the total 554 prescriptions, 51.1% were prescribed for females with a mean (±standard deviation) age of 40.85 ± 23.09 years. About 46.4% prescriptions of patients had one or more comorbid conditions, and the most frequent identified comorbid conditions were infectious (18.6%) and cardiac problems (6.3%) with 0.46 ± 0.499 average number of comorbid conditions per patient. Totally, 1516 drugs were prescribed with 2.74 ± 0.848 mean number per patient and range of 2–6. Two hundred and forty-two (43.7%) prescriptions had at least one potential drug–drug interaction, and it was found that 292 drug interactions were presented. Almost half of the drug–drug interaction identified was moderate (50%). Overall, the prevalence rate of drug–drug interaction was 43.7%. Older age (adjusted odds ratio = 8.301; 95% confidence interval (5.51–12.4), p = 0.000), presence of comorbidities (adjusted odds ratio = 1.72; 95% confidence interval (1.10–2.68), p = 0.000) and number of medications greater or equal to 3 (adjusted odds ratio = 2.69; 95% confidence interval (1.42–5.11), p = 0.000) were independent predictors for the occurrence of potential drug–drug interaction. Conclusion: The prevalence of potential drug–drug interaction among admitted patients was relatively high. Pharmacodynamic drug–drug interaction was the common mechanism of drug–drug interaction with moderate degree. Therefore, close follow-up of hospitalized patients is highly recommended.

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