Antibiotic pharmacokinetic modelling versus antibiotic therapy optimisation and resistance reduction: Bibliometric analysis
Arkadiusz Adamiszak (1,2)
(1) Department of Clinical Pharmacy and Biopharmacy, Poznan University of Medical Sciences, Poland, (2) Doctoral School, Poznan University of Medical Sciences, Poland
Objectives: Modern statistics and pharmacokinetic (PK) modelling are increasingly applied to calculate personalised dosage regimens or estimate unique drug dosage equations. In an era of problems with resistant bacteria strains, this is one of the ways to optimise antibiotic therapy and minimise the risk of resistance development. This bibliometric study aims to investigate trends in PK modelling studies, especially in the case of developing individualised dosage schemes and decreasing bacterial resistance.
Methods: We used Bibliometrix© (R version 4.1.2 library, K-Synth Srl, University of Naples Federico II, Naples, Italy) [1], VOSviewer© (version 1.6.18, Nees Jan van Eck and Ludo Waltman, Leiden University, Leiden, Netherlands) [2], CiteSpace© (version 6.1.R6, Chaomei Chen) [3], and Microsoft® Excel 365 (Microsoft, Redmond, Washington, USA) to analyse and summarise Web of Science articles from 1983 to March 2023. In order to search for publications, we used the following query: "TS=(("PK modeling" OR "PK modelling" OR "pharmacokinetic* modeling" OR "pharmacokinetic* modelling" OR "pharmacokinetic* model*" OR "PK model*" OR "population* pharmacokinetic*" OR "population* PK") AND antibiotic*)". We assessed the articles found in terms of various authors, countries, affiliations, journals, keywords, Web of Science categories, co-citations and citation bursts.
Results: We analysed 968 articles following the inclusion criteria and built keywords co-occurrence map and timelines for interest in PK modelling of antibiotics, co-citations, and keyword trends. The average annual growth rate of subject-related publications was 35.56% between 1983 and 2022, maintaining a continuous upward trend. In contrast, analysing the growth in interest in antibiotic resistance and Monte Carlo simulations, looking at keywords, the average annual growth rate over 1991 and 2022 was 89.06% and 65.76%, respectively. The three most productive and impacted authors are Roberts, JA., Lipman, J., and Wallis, SC. (82, 57, 34 articles, and 30, 25, 15 H-index, respectively). The United States leads in this field of research (29.13% of papers). The most relevant affiliations are the University of Queensland, Royal Brisbane and Women's Hospital, and Monash University. The top three most productive and impacted journals are Antimicrob. Agents Chemother., J. Antimicrob. Chemother., Int. J. Antimicrob. Agents (181, 83, 47 articles and 42, 30, 18 H-index, respectively). The majority of articles by keyword clustered on meropenem, vancomycin, piperacillin, cefepime and amikacin. Moreover, therapeutic drug monitoring, resistance, antibiotic dosing, target attainment, intensive care unit, and paediatrics are the most trending aspects.
Conclusions: Given the results of this study, the constantly increasing bacterial resistance and the limited perspectives for marketing new antibiotics, we expect to see a steady increase in interest in exploiting the potential of PK modelling for optimising antibiotic therapy. Considering the observed trends, PK modelling, combined with simulations and the probability of target attainment assessments, provides an opportunity to improve the efficacy and safety of ongoing antibiotic therapy.
References:
[1] Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 2017;11:959–75. https://doi.org/10.1016/J.JOI.2017.08.007.
[2] van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010;84:523–38. https://doi.org/10.1007/S11192-009-0146-3.
[3] Chen C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology 2006;57:359–77. https://doi.org/10.1002/ASI.20317.