Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling

Population pharmacokinetic and/or pharmacodynamic [PK(/PD)] modelling has become more popular in drug development and academic research. However, there are no reports exploring the research trends in this area. To explore the (research) trends of most cited articles on PK(/PD) modelling, we bibliometrically analysed the most cited articles (n=100) extracted from the Scopus online database from inception (1964-2021) and again in the recent years (2015-2021) using VOSviewer v1.6.15 and Publish or Perish v8 software. Information such as ATC/drug class, model type, software used, studied population, authors’ institutions, journals, collaborations between countries, and funding sources was extracted and compared. Majority of the studies (65%) described in the 100 most cited articles were population PK modelling studies, with the proportion of the population PKPD modelling studies increasing over time (from 30 to 43%). A large percentage of the impactful articles (43%) were published by top five journals, analysed adult data (84%) and used NONMEM® (80%), which has not changed much over time. Most of the impactful articles studied chemotherapeutic and immunomodulating (33%), anti-infective (29%), and central nervous system (22%) ATC class of drugs, with articles analysing immunosuppressant drug class increasing the most over time (from 10% to 18%). In conclusion, we used a bibliometric approach and investigated research trends in top 100 most cited articles involving PK (/PD) modelling. Apart from the changes mentioned above most other metrics that we compared remained relatively unchanged over time.

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Publication Details

Journal
Journal of Scientometric Research
Published
2025-09-01
DOI
https://doi.org/10.5530/jscires.20250983
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling

Hadzliana Zainal, Eva Germovsek, Nur Mardhiya Darnalis
Journal of Scientometric Research
Computational Drug Discovery Methods
article

Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling

Hadzliana Zainal, Eva Germovsek, Nur Mardhiya Darnalis
article en

Abstract

Population pharmacokinetic and/or pharmacodynamic [PK(/PD)] modelling has become more popular in drug development and academic research. However, there are no reports exploring the research trends in this area. To explore the (research) trends of most cited articles on PK(/PD) modelling, we bibliometrically analysed the most cited articles (n=100) extracted from the Scopus online database from inception (1964-2021) and again in the recent years (2015-2021) using VOSviewer v1.6.15 and Publish or Perish v8 software. Information such as ATC/drug class, model type, software used, studied population, authors’ institutions, journals, collaborations between countries, and funding sources was extracted and compared. Majority of the studies (65%) described in the 100 most cited articles were population PK modelling studies, with the proportion of the population PKPD modelling studies increasing over time (from 30 to 43%). A large percentage of the impactful articles (43%) were published by top five journals, analysed adult data (84%) and used NONMEM® (80%), which has not changed much over time. Most of the impactful articles studied chemotherapeutic and immunomodulating (33%), anti-infective (29%), and central nervous system (22%) ATC class of drugs, with articles analysing immunosuppressant drug class increasing the most over time (from 10% to 18%). In conclusion, we used a bibliometric approach and investigated research trends in top 100 most cited articles involving PK (/PD) modelling. Apart from the changes mentioned above most other metrics that we compared remained relatively unchanged over time.

Journal of Scientometric ResearchVol. 14(2)
Boehringer Ingelheim (Germany) (DE), Universiti Sains Malaysia (MY), Hanlim Pharm (South Korea) (KR), Boehringer Ingelheim (Australia) (AU)
Openalex Percentile: Top 76%
Computational Drug Discovery Methods
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