Dataset Information

Systematic analysis of the varied designs of 819 therapeutic antibodies and Fc fusion proteins assigned international nonproprietary names.

ABSTRACT: By combining and cross-checking data from the World Health Organization's lists of international nonproprietary names with three other databases, we assembled a dataset of amino acid sequences of 819 antibody-related therapeutics. This enabled the systematic tabulation of 57 different molecular formats and about 90 different constant-region variants (47 for silencing, 14 for enhancement, 8 for modifying binding to FcRn, 13 for heterodimerization, 4 for site-specific modification and 4 for stabilization), as well as the frequencies of different targets and immunoglobulin allotypes. The curated dataset provides a resource for researchers, giving insights into trends in antibody engineering and a guide to the most frequently tested designs.

SUBMITTER: Wilkinson I

PROVIDER: S-EPMC9481088 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature

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Systematic analysis of the varied designs of 819 therapeutic antibodies and Fc fusion proteins assigned international nonproprietary names.

Wilkinson Ian I Hale Geoff G

mAbs 20220101 1

By combining and cross-checking data from the World Health Organization's lists of international nonproprietary names with three other databases, we assembled a dataset of amino acid sequences of 819 antibody-related therapeutics. This enabled the systematic tabulation of 57 different molecular formats and about 90 different constant-region variants (47 for silencing, 14 for enhancement, 8 for modifying binding to FcRn, 13 for heterodimerization, 4 for site-specific modification and 4 for stabil ...[more]

PMID: 36109838

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Project description:BackgroundConfusion between look-alike and sound-alike (LASA) medication names (such as mercaptamine and mercaptopurine) accounts for up to one in four medication errors, threatening patient safety. Error reduction strategies include computerized physician order entry interventions, and 'Tall Man' lettering. The purpose of this study is to explore the medication name designation process, to elucidate properties that may prime the risk of confusion.Methods and findingsWe analysed the formal and semantic properties of 7,987 International Non-proprietary Names (INNs), in relation to naming guidelines of the World Health Organization (WHO) INN programme, and have identified potential for errors. We explored: their linguistic properties, the underlying taxonomy of stems to indicate pharmacological interrelationships, and similarities between INNs. We used Microsoft Excel for analysis, including calculation of Levenshtein edit distance (LED). Compliance with WHO naming guidelines was inconsistent. Since the 1970s there has been a trend towards compliance in formal properties, such as word length, but longer names published in the 1950s and 1960s are still in use. The stems used to show pharmacological interrelationships are not spelled consistently and the guidelines do not impose an unequivocal order on them, making the meanings of INNs difficult to understand. Pairs of INNs sharing a stem (appropriately or not) often have high levels of similarity (<5 LED), and thus have greater potential for confusion.ConclusionsWe have revealed a tension between WHO guidelines stipulating use of stems to denote meaning, and the aim of reducing similarities in nomenclature. To mitigate this tension and reduce the risk of confusion, the stem system should be made clear and well ordered, so as to avoid compounding the risk of confusion at the clinical level. The interplay between the different WHO INN naming principles should be further examined, to better understand their implications for the problem of LASA errors.

Dataset Information

Systematic analysis of the varied designs of 819 therapeutic antibodies and Fc fusion proteins assigned international nonproprietary names.

Publications

Systematic analysis of the varied designs of 819 therapeutic antibodies and Fc fusion proteins assigned international nonproprietary names.

Similar Datasets

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