Inconsistencies and Misleading Information in Officially Approved Prescribing Information From Three Major Drug Markets

Pfistermeister, Barbara; Sass, Anke; Criegee-Rieck, Manfred; Bürkle, Thomas; Fromm, Martin F.; Maas, Renke

doi:10.1038/clpt.2014.156

Cited by 23 publications

(31 citation statements)

References 20 publications

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“…Studies have shown product labeling to be incomplete [19], [20] and one motivation for this activity is to extend previous pilot work on methods to address known limitations of the information source [21]. To do this, we examined cases where there existed an overlap between the PDDI datasets designed for NLP research with product labeling and other data sources.…”

Section: Methodsmentioning

confidence: 99%

Toward a complete dataset of drug–drug interaction information from publicly available sources

Ayvaz

Horn

Hassanzadeh

et al. 2015

Journal of Biomedical Informatics

105

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Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

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Section: Methodsmentioning

confidence: 99%

Toward a complete dataset of drug–drug interaction information from publicly available sources

Ayvaz

Horn

Hassanzadeh

et al. 2015

Journal of Biomedical Informatics

105

View full text Add to dashboard Cite

show abstract

“…This is because inconsistent information regarding medicines containing the same active ingredient contributes to confusion and poor prescription decisions. Although similar regulatory requirements are applied to generic medicines in the USA, inconsistency has been reported among bioequivalent medicines, both there and in other countries . To the best of our knowledge, only two original studies have reported labeling inconsistencies regarding the same drug, authorized by the same regulatory agency.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown, however, that drug labeling and SmPCs have omitted core clinical pharmacology information in the United States (USA) and the EU . Information about drug interactions in SmPCs has often been found to be incomplete and outdated or even absent …”

Section: Introductionmentioning

confidence: 99%

“…Storflor et al investigated 71 generic labels of the 17 top‐selling medicines in Norway, and Duke et al reported that 77.9% of generic manufacturers produced labels that differed from those of the corresponding brand‐name medicines in the USA. Evaluating the SmPCs of medicines marketed in the USA, the United Kingdom (UK), and Germany, Pfistermeister et al found inconsistencies in labels for the same medicines among different regulatory bodies. Doogue and Thynne reported inconsistencies in generic drug labels in Australia.…”

Section: Introductionmentioning

confidence: 99%

“…5 Information about drug interactions in SmPCs has often been found to be incomplete and outdated 6 or even absent. 7 According to the Quality Review of Documents guidance, 8 all relevant aspects of the content of generic SmPCs should be consistent with the SmPCs of reference medicinal products-the so-called "brand-name" medicines. This is because inconsistent information regarding medicines containing the same active ingredient contributes to confusion and poor prescription decisions.…”

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confidence: 99%

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Natural language processing‐based assessment of consistency in summaries of product characteristics of generic antimicrobials

Shimazawa

Kano

Ikeda

2018

Pharmacology Res & Perspec

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To investigate consistency in summaries of product characteristics (SmPCs) of generic antimicrobials, we used natural language processing (NLP) to analyze and compare large amounts of text quantifying consistency between original and generic SmPCs. We manually compared each section of generic and original SmPCs for antimicrobials listed in the electronic Medicines Compendium in the United Kingdom, focusing on omissions and additions of clinically significant information (CSI). Independently, we quantified differences between the original and generic SmPCs using Kachako, a fully automatic NLP platform. Among the 137 antimicrobials listed in the electronic Medicines Compendium, we identified 193 pairs of original and generic antimicrobial SmPCs for the 48 antimicrobials for which generic SmPCs existed. Of these 193 pairs, 157 (81%) were consistent and 36 were inconsistent with the original SmPC. When the cut‐off value of RATE (the index of similarity between two SmPCs) was set at 0.860, our NLP system effectively discriminated consistent generic SmPCs with a specificity of 100% and a sensitivity of 61%. We observed CSI omissions but not additions in the SmPC subsection related to pharmacokinetic properties. CSI additions but not omissions were found in the subsections dealing with therapeutic indications and fertility, pregnancy and lactation. Despite regulatory guidance, we observed substantial inconsistencies in the information in the United Kingdom SmPCs for antimicrobials. NLP technology proved to be a useful tool for checking large numbers of SmPCs for consistency.

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Assessment of Consistency of Drug Interaction Information in Drug Labels Among the United States, the United Kingdom, China, Japan, and Korea

Jeong

Kam

et al. 2018

Clin Pharma and Therapeutics

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Information gap in drug labeling among countries create challenges in therapeutic use of drugs. We aimed to evaluate the consistency of drug interaction information in drug labels among five countries. The study drugs were chosen from the commonly approved drug list in the US, UK, China, Japan, and Korea. The degree of agreement of drug interaction data was evaluated by kappa coefficient. Thirty-eight drugs were evaluated, and moderate degree of agreement was observed among all countries' labeling (κ = 0.43, 95% confidence interval (CI) = 0.41-0.46). The degree of agreement was the highest for the UK and Korea (κ = 0.71, 95% CI = 0.67-0.75) and the lowest for the UK and Japan (κ = 0.02, 95% CI = 0.00-0.04). Information regarding drug interactions listed in the studied drug labels was not in high agreement. International standardization of drug labeling is required to ensure safe drug therapy.

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Inconsistencies and Misleading Information in Officially Approved Prescribing Information From Three Major Drug Markets

Cited by 23 publications

References 20 publications

Toward a complete dataset of drug–drug interaction information from publicly available sources

Toward a complete dataset of drug–drug interaction information from publicly available sources

Natural language processing‐based assessment of consistency in summaries of product characteristics of generic antimicrobials

Assessment of Consistency of Drug Interaction Information in Drug Labels Among the United States, the United Kingdom, China, Japan, and Korea

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