Assessment of acute kidney injury related to small-molecule protein kinase inhibitors using the FDA adverse event reporting system

2020 ◽  
Vol 86 (5) ◽  
pp. 655-662
Author(s):  
Qianqian Fan ◽  
Jie Ma ◽  
Bo Zhang ◽  
Qiuyue Li ◽  
Fang Liu ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Adverse Event ◽  
Protein Kinase ◽  
Kinase Inhibitors ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Protein Kinase Inhibitors ◽  
Event Reporting

scholarly journals 1977. Comparing Acute Kidney Injury Risk among Antibiotic Classes: A Study of the FDA Adverse Event Reporting System (FAERS)

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S662-S662
Author(s):  
Taylor M Patek ◽  
Chengwen Teng ◽  
Kaitlin E Kennedy ◽  
Christopher R Frei
Keyword(s):  
Acute Kidney Injury ◽  
Adverse Event ◽  
Injury Risk ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Event Reporting ◽  
Increased Risk

Abstract Background A recent article published in 2018 studied the FDA Adverse Event Reporting System (FAERS) and listed the most common medications associated with acute kidney injury (AKI) based on number of AKI reports. In regards to antibiotics, the study only ranked vancomycin, fluoroquinolones, penicillin combinations, and trimethoprim–sulfamethoxazole as having a significant association with AKI. The objective of this study was to evaluate those and additional antibiotic classes using FAERS, and to compare their risk associated with this adverse drug event. Methods FAERS reports from January 1, 2015 to December 31, 2017 were included in the study. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify AKI cases. Reporting Odds Ratios (RORs) and corresponding 95% confidence intervals (95% CI) for the association between antibiotics and AKI were calculated. An association was considered statistically significant when the lower limit of the 95% CI was greater than 1.0. Results A total of 2,042,801 reports (including 20,138 acute kidney injury reports) were considered, after inclusion criteria were applied. Colistin had the greatest proportion of AKI reports, representing 25% of all colistin reports. Acute kidney injury RORs (95% CI) for antibiotics were (in descending order): colistin 33.10 (21.24–51.56), aminoglycosides 17.41 (14.49–20.90), vancomycin 15.28 (13.82–16.90), trimethoprim-sulfamethoxazole 13.72 (11.94–15.76), penicillin combinations 7.95 (7.09–8.91), clindamycin 6.46 (5.18–8.04), cephalosporins 6.07 (5.23–7.05), daptomycin 6.07 (4.61–7.99), macrolides 3.60 (3.04–4.26), linezolid 3.48 (2.54–4.77), carbapenems 3.31 (2.58–4.25), metronidazole 2.55 (1.94–3.36), tetracyclines 1.73 (1.26–2.36), and fluoroquinolones 1.71 (1.49–1.97). Conclusion This study found 17 classes of antibiotics and combinations that were significantly associated with AKI compared with four antibiotics that were mentioned in a recently published article looking at drug-associated AKI. While this study confirmed previous literature of certain antibiotics associated with increased risk of AKI, it also compared antibiotics within classes and provided additional insight regarding which antibiotics had the highest associated risk of an AKI. Disclosures All authors: No reported disclosures.

scholarly journals Proton pump inhibitors associated acute kidney injury and chronic kidney disease: data mining of US FDA adverse event reporting system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bin Wu ◽  
Dan Li ◽  
Ting Xu ◽  
Min Luo ◽  
Zhiyao He ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Adverse Event ◽  
Kidney Disease ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
Us Fda

AbstractProton pump inhibitors (PPIs) were widely used. Observational studies suggested increasing risk of kidney injury in patients with PPIs treatment. We gathered six PPI regimens and adverse reports of acute kidney injury (AKI) and chronic kidney disease (CKD) based on US FDA Adverse Event Reporting System (FAERS) database from 2004 to 2019. We employed reporting odds ratio (ROR) to detect signals. Finally, we identified 3187 PPIs-associated AKI cases and 3457 PPIs-associated CKD cases. We detected significant signals between PPIs and AKI as well as CKD. The signal strength was stronger for CKD (ROR = 8.80, 95% CI 8.49–9.13) than AKI (ROR = 3.95, 95% CI 3.81–4.10), while dexlansoprazole performed stronger association for CKD (ROR = 34.94, 95% CI 30.89–39.53) and AKI (ROR = 8.18, 95% CI 7.04–9.51) than the other five PPIs. The median time from PPIs use to event occurrence was 23 days for AKI and 177 days for CKD. PPIs-associated AKI resulted larger proportion of death, life-threatening, hospitalization and disability events than PPIs-associated CKD. By mining the FAERS big data, we provided more information between PPIs use and the AKI and CKD events. PPIs rational use should be repeatedly stressed.

scholarly journals Correction to: Comparing Acute Kidney Injury Reports Among Antibiotics: A Pharmacovigilance Study of the FDA Adverse Event Reporting System (FAERS)

Drug Safety ◽  
2020 ◽  
Vol 43 (8) ◽  
pp. 825-825
Author(s):  
Taylor M. Patek ◽  
Chengwen Teng ◽  
Kaitlin E. Kennedy ◽  
Carlos A. Alvarez ◽  
Christopher R. Frei
Keyword(s):  
Acute Kidney Injury ◽  
Adverse Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Event Reporting

Comparing Acute Kidney Injury Reports Among Antibiotics: A Pharmacovigilance Study of the FDA Adverse Event Reporting System (FAERS)

Drug Safety ◽  
2019 ◽  
Vol 43 (1) ◽  
pp. 17-22 ◽  
Author(s):  
Taylor M. Patek ◽  
Chengwen Teng ◽  
Kaitlin E. Kennedy ◽  
Carlos A. Alvarez ◽  
Christopher R. Frei
Keyword(s):  
Acute Kidney Injury ◽  
Adverse Event ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Kidney Injury ◽  
Adverse Event Reporting ◽  
Event Reporting

Comparison of Adverse Drug Reactions for Patients Treated with Tyrosine Kinase Inhibitors: Data Mining of the Public Version of the FDA Adverse Event Reporting System (AERS)

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1698-1698
Author(s):  
Tetsuya Tanimoto ◽  
Yasuo Oshima ◽  
Koichiro Yuji ◽  
Masahiro Kami
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Odds Ratio ◽  
Kinase Inhibitors ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Reactions ◽  
Suspected Drug ◽  
Event Reporting

Abstract Abstract 1698 Backgrounds: The consecutive approvals of tyrosine kinase inhibitors (TKIs) have been changing the landscape of treatment strategy for patients with chronic myeloid leukemia (CML). Currently, three TKIs are available worldwide, including imatinib (Glivec/Gleevec; Novartis Pharmaceuticals, East hanover, NJ), nilotinib (Tasigna; Novartis Pharmaceuticals) and dasatinib (Sprycel; Bristol-Myers Squibb, Princeton, NJ). Although second generation TKIs (nilotinib and dasatinib) have shown their efficacy and safety in recent clinical trials, additional data are needed for better understanding and differences in their safety profiles may be helpful when choosing a TKI. We compared the adverse drug reactions (ADRs) for patients treated with three TKIs using spontaneous adverse event reporting after approval to investigate the characteristics of safety profiles. Method: To compare adverse events characteristics among three TKIs, the case/noncase adverse events reports associated with TKIs use were retrieved from the U.S. Food and Drug Administration Adverse Event Reporting System (AERS) between 2004 and 2010. We calculated the reporting odds ratio (ROR), which is known as one of data mining algorithms for signal detection techniques of ADRs, characterized by providing a fast and cost-efficient way of detecting possible ADR signals. All events in the AERS have been coded for data entry in accordance with the standardized terminology, known as Preferred Terms, in the Medical Dictionary for Regulatory Activities. The ROR is similar to the idea of odds ratio, calculating the odds of exposure of the suspected drug in patients who had events divided by the odds of exposure of the suspected drug in those without events. The ROR -1.96 standard error greater than 1 with at least 4 ADR reports was used as a signal criterion in this study. Results: We identified 18,979 ADRs for imatinib, 5,388 ADRs for nilotinib, and 2,482 ADRs for dasatinib. The number of ADRs flagged by our signal criterion was 91 for imatinib, 82 for nilotinib, and 109 for dasatinib. Top 10 lists of ADRs with higher ROR are shown in Table for each TKI. The safety profiles were almost different among TKIs. ADRs related to skin and hepatic function were noted for imatinib, whereas ADRs related to cardiac events were prominent for nilotinib, and ADRs related to lymphocytosis, edema and effusion were noticeable for dasatinib. The different dosing requirements of dasatinib and nilotinib may be an additional factor of ADRs. Conclusions: ADRs reported in the AERS for each TKI were relatively consistent with known characteristics of ADRs reported in previous clinical trials. Our information would be supportive data for choosing a TKI for CML patients based on comorbidities and drug safety profiles. The choice of therapy in a given patient with CML may depend on age, past history and comorbidities as well as disease risk score and mutational analysis. Disclosures: Oshima: Sanofi Aventis: Employment.

scholarly journals Clinically-weighted transcriptomic signatures for protein kinase inhibitor associated cardiotoxicity

2016 ◽  
Author(s):  
JGC van Hasselt ◽  
J Hansen ◽  
Y Xiong ◽  
J Shim ◽  
A Pickard ◽  
...  
Keyword(s):  
Adverse Event ◽  
Protein Kinase ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Adverse Event Reporting System ◽  
Cardiac Contractility ◽  
Digital Gene Expression ◽  
Adverse Event Reporting ◽  
Protein Kinase Inhibitors ◽  
Risk Scores

ABSTRACTCardiotoxicity (CT) involving diminished cardiac contractility and heart failure is a major adverse event associated with otherwise efficacious protein kinase inhibitors (KIs). Here, we sought to develop clinically-weighted transcriptomic signatures to predict risk of CT and to better understand the biological processes associated with CT risk. We obtained transcriptome-wide response profiles in four human primary cardiomyocyte cell lines that were treated with 22 different KIs using mRNA sequencing with 3’ digital gene expression. The FDA Adverse Event Reporting System was used to derive relative risk scores for four types of CT for different KIs. We used elastic net regression to associate these transcriptomic profiles with KI-associated risk scores for CT subtypes to obtain clinically-weighted transcriptomic signatures, which showed good predictive properties (cross-validation R2>0.87). Our clinically-weighted transcriptomic signatures for KI-associated CT may be of relevance in early drug development for the prediction of KI-associated CT.

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