Research ArticleJournal of Scientometric ResearchVol. 14 | Issue 2 | 2026 | pp. 706–736Open access
Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling
- 1,2,
- 2*,
- 3
- 1 Department of Pharmacy, Hospital Kulim, Kedah, MALAYSIA.
- 2 School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MALAYSIA.
- 3 Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH and Co. KG, Ingelheim, GERMANY.
Published in Journal of Scientometric Research
Correspondence: Hadzliana Zainal
School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MALAYSIA.
Email: hadz@usm.my
Copyright: © 2026 Manuscript Technomedia. This is an open access article.
- Published:
- Jan 3, 2026
- Received:
- May 6, 2025
- Accepted:
- Jul 19, 2025
How to cite
Darnalis, N. M., Zainal, H., & Germovsek, E. (2026). Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling. Journal of Scientometric Research, 14(2), 706–736. https://doi.org/10.5530/jscires.20250983
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.
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Article metadata
| Title | Bibliometric Analysis to Explore Trends of the 100 Most Cited Articles in Population Pharmacokinetic and/or Pharmacodynamic Modelling |
|---|---|
| Authors | Nur Mardhiya Darnalis; Hadzliana Zainal; Eva Germovsek |
| Affiliations | Department of Pharmacy, Hospital Kulim, Kedah, MALAYSIA.; School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, MALAYSIA.; Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH and Co. KG, Ingelheim, GERMANY. |
| Corresponding author | hadz@usm.my |
| Journal | Journal of Scientometric Research |
| Volume / Issue | Vol. 14, Issue 2 (2026) |
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