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BioMed Research International
Volume 2014, Article ID 585687, 9 pages
Research Article

Exploring Population Pharmacokinetic Modeling with Resampling Visualization

1College of Information Engineering, Taishan Medical University, Taian, Shandong 271016, China
2Faculté de Pharmacie, Université de Montréal, CP 6128, Succursale Centre-Ville, Montréal , QC, Canada H3C 3J7
3Centre de Recherche Mathématiques, Université de Montréal, CP 6128, Succursale Centre-Ville, Montréal , QC, Canada H3C 3J7
4Agricultural Big Data Research Center, College of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China

Received 6 February 2014; Revised 12 April 2014; Accepted 13 April 2014; Published 4 May 2014

Academic Editor: Jareen Meinzen-Derr

Copyright © 2014 Fenghua Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. In the last decade, population pharmacokinetic (PopPK) modeling has spread its influence in the whole process of drug research and development. While targeting the construction of the dose-concentration of a drug based on a population of patients, it shows great flexibility in dealing with sparse samplings and unbalanced designs. The resampling approach has been considered an important statistical tool to assist in PopPK model validation by measuring the uncertainty of parameter estimates and evaluating the influence of individuals. Methods. The current work describes a graphical diagnostic approach for PopPK models by visualizing resampling statistics, such as case deletion and bootstrap. To examine resampling statistics, we adapted visual methods from multivariate analysis, parallel coordinate plots, and multidimensional scaling. Results. Multiple models were fitted, the information of parameter estimates and diagnostics were extracted, and the results were visualized. With careful scaling, the dependencies between different statistics are revealed. Using typical examples, the approach proved to have great capacity to identify influential outliers from the statistical perspective, which deserves special attention in a dosing regimen. Discussion. By combining static graphics with interactive graphics, we are able to explore the multidimensional data from an integrated and systematic perspective. Complementary to current approaches, our proposed method provides a new way for PopPK modeling analysis.