Literature DB >> 15255086

What is prescription labeling communicating to doctors about hepatotoxic drugs? A study of FDA approved product labeling.

Mary E Willy1, Zili Li.   

Abstract

PURPOSE: The objective of this study was to evaluate the informativeness and consistency of product labeling of hepatotoxic drugs marketed in the United States.
METHODS: We searched the Physicians' Desk Reference-2000 for prescription drugs with hepatic failure and/or hepatic necrosis listed in the labeling. We used a six-item checklist to evaluate the 'informativeness' and consistency of the labeling content. An informativeness score equaled the proportion of checklist items present in each drug's labeling.
RESULTS: Ninety-five prescription drugs were included in the study. Eleven (12%) of the drugs had information related to hepatic failure in a Black Boxed Warning, 52 (54%) in the Warnings section and 32 (34%) in the Adverse Reactions section of the label. The mean informativeness score was 35%; the score was significantly higher, 61%, when the risk was perceived to be high. The informativeness of labeling was not affected by the time of the labeling, but differed across the Center for Drug Evaluation and Research (CDER) Review Division responsible for the labeling.
CONCLUSIONS: The information provided in labeling is variable and affected by many factors, including the perceived level of risk and review division strategy. Product labeling may benefit from current FDA initiatives to improve the consistency of risk-related labeling.

Entities:  

Mesh:

Year:  2004        PMID: 15255086     DOI: 10.1002/pds.856

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  3 in total

1.  Mining FDA drug labels using an unsupervised learning technique--topic modeling.

Authors:  Halil Bisgin; Zhichao Liu; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2011-10-18       Impact factor: 3.169

2.  CSH guidelines for the diagnosis and treatment of drug-induced liver injury.

Authors:  Yue-Cheng Yu; Yi-Min Mao; Cheng-Wei Chen; Jin-Jun Chen; Jun Chen; Wen-Ming Cong; Yang Ding; Zhong-Ping Duan; Qing-Chun Fu; Xiao-Yan Guo; Peng Hu; Xi-Qi Hu; Ji-Dong Jia; Rong-Tao Lai; Dong-Liang Li; Ying-Xia Liu; Lun-Gen Lu; Shi-Wu Ma; Xiong Ma; Yue-Min Nan; Hong Ren; Tao Shen; Hao Wang; Ji-Yao Wang; Tai-Ling Wang; Xiao-Jin Wang; Lai Wei; Qing Xie; Wen Xie; Chang-Qing Yang; Dong-Liang Yang; Yan-Yan Yu; Min-de Zeng; Li Zhang; Xin-Yan Zhao; Hui Zhuang
Journal:  Hepatol Int       Date:  2017-04-12       Impact factor: 6.047

3.  Investigating drug repositioning opportunities in FDA drug labels through topic modeling.

Authors:  Halil Bisgin; Zhichao Liu; Reagan Kelly; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2012-09-11       Impact factor: 3.169

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.