Open AccessNatural language processing‐based assessment of consistency in summaries of product characteristics of generic antimicrobials
Author(s) -
Shimazawa Rumiko,
Kano Yoshinobu,
Ikeda Masayuki
Publication year - 2018
Publication title -
pharmacology research and perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.975
H-Index - 27
ISSN - 2052-1707
DOI - 10.1002/prp2.435
Subject(s) - consistency (knowledge bases) , computer science , natural product , natural language processing , product (mathematics) , antimicrobial , artificial intelligence , mathematics , chemistry , microbiology and biotechnology , biology , geometry , stereochemistry
To investigate consistency in summaries of product characteristics (Sm PC s) of generic antimicrobials, we used natural language processing ( NLP ) to analyze and compare large amounts of text quantifying consistency between original and generic Sm PC s. We manually compared each section of generic and original Sm PC s 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 Sm PC s 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 Sm PC s for the 48 antimicrobials for which generic Sm PC s existed. Of these 193 pairs, 157 (81%) were consistent and 36 were inconsistent with the original Sm PC . When the cut‐off value of RATE (the index of similarity between two Sm PC s) was set at 0.860, our NLP system effectively discriminated consistent generic Sm PC s with a specificity of 100% and a sensitivity of 61%. We observed CSI omissions but not additions in the Sm PC 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 Sm PC s for antimicrobials. NLP technology proved to be a useful tool for checking large numbers of Sm PC s for consistency.