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Updated: Jul 9, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules
Published on: June 16, 2023
From pharmacometric foundations to emerging artificial intelligence applications: A bibliometric analysis of
Xuejing Li1, Wenfeng Chen2, Xuan Wang1
1Department of Pharmacy, Zaozhuang Municipal Hospital, Zaozhuang, Shandong, China.
Objective:
This study aims to provide a comprehensive bibliometric analysis of model-informed precision dosing (MIPD) research in anti-infective therapy from 2005 to 2025, systematically mapping its evolution from pharmacometric foundations to emerging artificial intelligence applications, and identifying key research trends, institutional contributions, and future research priorities in this rapidly evolving field.
Methods:
The terminology related to MIPD and anti-infective drugs was used as a search strategy to retrieved literature from the core collection of Web of Science, and the data obtained were analyzed by VOSviewer, CiteSpace and Bibliometrix.
Results:
A total of 4,601 articles were included, the USA published the largest number of documents, Australia had the highest citations per publication. The University of Queensland was the most productive institution. Roberts JA and Lipman J were the most published authors. The field underwent a foundational phase of developing population pharmacokinetic models, a consolidation phase aimed at promoting Bayesian-guided TDM and extending it to special populations, and a frontier phase of parallel advancement involving traditional MIPD and emerging machine learning frameworks. Critically, the divergence between pharmacokinetic metrics used in traditional MIPD and predictive accuracy metrics favored by machine learning approaches represents a principal obstacle to clinical integration.
Conclusion:
This study provides the first comprehensive bibliometric map of two decades of MIPD research in anti-infective therapy, documenting its evolution from pharmacometric foundations to emerging AI-assisted applications, and identifies prospective clinical validation, EHR integration, and harmonized ML-MIPD evaluation frameworks as key future priorities.
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