Warfarin–A natural anticoagulant: A review of research trends for precision medication
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Date
2024-02-23Author
Xue, Ling
Singla, Rajeev K.
He, Shan
Arrasate Gil, Sonia
González Díaz, Humberto
Miao, Liyan
Shen, Bairong
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Phytomedicine 128 : (2024) // Article ID 155479
Abstract
Background: Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual
variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of
these factors on individual variability.
Purpose: The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics
and machine learning models of personalized warfarin dosage.
Methods: A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using
VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from
the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review
that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology,
mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine
learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added
as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were
added to retrieve the literature.
Results: Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United
States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies
have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms.
There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models,
and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains
biological or pharmacological mechanisms in structure. The process of pharmacometric model development is
very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more
mathematical and have less biological interpretation. However, it is faster, more efficient, and less timeconsuming.
Most published models of machine learning algorithms were established based on cross-sectional
data sourced from the database.