Literature DB >> 23042584

Consistency in the safety labeling of bioequivalent medications.

Jon Duke1, Jeff Friedlin, Xiaochun Li.   

Abstract

PURPOSE: Bioequivalent medications are required by the Food and Drug Administration to have identical warnings on their labels. This requirement has both clinical and legal importance, yet has never been validated. We sought to determine the real-world consistency of electronic labeling for bioequivalent drugs from different manufacturers.
METHODS: Using natural language processing, we indexed the adverse drug reactions (ADRs) found in the Adverse Reactions and Post-Marketing sections of 9105 structured product labels. We calculated the standard deviation in ADR labeling for each bioequivalent drug and the percent deviation of each generic label from its corresponding brand. We also analyzed the performance of individual generic manufacturers. For the 25 drugs with the greatest discrepancy in labeled ADRs, we performed manual review to identify causes of inconsistency.
RESULTS: 68% of multi-manufacturer drugs had discrepancies in ADR labeling. For a given drug, the mean deviation in number of labeled ADRs was 4.4, and the median was 0.8 (IQR 0 to 3.2). The mean range in number of labeled ADRs was 12 +/- 0.9, and the median was 2 (IQR 0 to 9). Overall, 77.9% of generic manufacturers produced labels differing from brand. Causes of inconsistency included missing tables, outdated post-marketing reports, and formatting issues.
CONCLUSIONS: Despite FDA mandate, bioequivalent drugs often differ in their safety labeling. Physicians should be aware of such differences and regulators should consider new strategies for harmonizing bioequivalent labels.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 23042584     DOI: 10.1002/pds.3351

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


  14 in total

1.  Text mining for adverse drug events: the promise, challenges, and state of the art.

Authors:  Rave Harpaz; Alison Callahan; Suzanne Tamang; Yen Low; David Odgers; Sam Finlayson; Kenneth Jung; Paea LePendu; Nigam H Shah
Journal:  Drug Saf       Date:  2014-10       Impact factor: 5.606

2.  Clinical relevance of information in the Summaries of Product Characteristics for dose adjustment in renal impairment.

Authors:  Teresa M Salgado; Blanca Arguello; Fernando Martinez-Martinez; Shalom I Benrimoj; Fernando Fernandez-Llimos
Journal:  Eur J Clin Pharmacol       Date:  2013-07-25       Impact factor: 2.953

3.  Accuracy of an automated knowledge base for identifying drug adverse reactions.

Authors:  E A Voss; R D Boyce; P B Ryan; J van der Lei; P R Rijnbeek; M J Schuemie
Journal:  J Biomed Inform       Date:  2016-12-16       Impact factor: 6.317

4.  Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.

Authors:  Laritza M Rodriguez; Dina Demner Fushman
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

Review 5.  Defining a reference set to support methodological research in drug safety.

Authors:  Patrick B Ryan; Martijn J Schuemie; Emily Welebob; Jon Duke; Sarah Valentine; Abraham G Hartzema
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

6.  Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.

Authors:  Richard D Boyce; Patrick B Ryan; G Niklas Norén; Martijn J Schuemie; Christian Reich; Jon Duke; Nicholas P Tatonetti; Gianluca Trifirò; Rave Harpaz; J Marc Overhage; Abraham G Hartzema; Mark Khayter; Erica A Voss; Christophe G Lambert; Vojtech Huser; Michel Dumontier
Journal:  Drug Saf       Date:  2014-08       Impact factor: 5.606

7.  Concordance and predictive value of two adverse drug event data sets.

Authors:  Aurel Cami; Ben Y Reis
Journal:  BMC Med Inform Decis Mak       Date:  2014-08-22       Impact factor: 2.796

8.  Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data.

Authors: 
Journal:  J Biomed Semantics       Date:  2017-03-07

9.  Discrepancies Between the Labels of Originator and Generic Pharmaceutical Products: Implications for Patient Safety.

Authors:  Alexandra Thoenes; Luca Cariolato; Julian Spierings; Alexis Pinçon
Journal:  Drugs Real World Outcomes       Date:  2020-06

10.  Natural language processing-based assessment of consistency in summaries of product characteristics of generic antimicrobials.

Authors:  Rumiko Shimazawa; Yoshinobu Kano; Masayuki Ikeda
Journal:  Pharmacol Res Perspect       Date:  2018-11-11
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