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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

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Related Experiment Video

Updated: Sep 13, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis.

Yongqi Zheng¹, Meng Zhang¹, Conghui Wang¹

¹Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China, 86 13811155891.

JMIR Medical Informatics

|July 28, 2025

Summary

Common data models (CDMs) in pharmacoepidemiology enable large-scale studies. High-impact research often involves multicenter collaboration and vaccine studies, highlighting the need for global inclusivity.

Keywords:

bibliometric analysis common data model pharmacoepidemiology systematic review

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Published on: June 13, 2025

Area of Science:

Pharmacoepidemiology
Real-world evidence
Data standardization

Background:

Common Data Models (CDMs) standardize data for pharmacoepidemiologic research.
CDMs facilitate large-scale, multicenter studies and real-world evidence generation.
A comprehensive global evaluation of CDM applications in this field was lacking.

Purpose of the Study:

To systematically review and bibliometrically analyze the landscape of CDM usage in pharmacoepidemiology.
To map publication trends, institutional collaborations, and citation impacts of CDM research.
To identify factors associated with high-impact studies in this domain.

Main Methods:

Systematic review and bibliometric analysis of 308 studies (1997-2024) from 9 databases.
Studies were categorized by Total Citations per Year (TCpY) to analyze impact.
Comparative analysis of high-TCpY versus low-TCpY studies.

Main Results:

The United States and South Korea are leading contributors in CDM-based pharmacoepidemiology.
High-TCpY studies are significantly associated with multicenter collaboration, US institutions, and vaccine research.
International collaborations are concentrated in North America, Europe, and East Asia, with limited LMIC involvement.

Conclusions:

This study provides the first bibliometric overview of CDM-based pharmacoepidemiologic research.
Multicenter, collaborative, and vaccine-focused studies demonstrate higher impact.
Increased global inclusivity and collaboration are crucial for advancing pharmacoepidemiology.