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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.
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Multicompartment Models: Overview

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Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Model Approaches for Pharmacokinetic Data: Physiological Models

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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Related Experiment Video

Updated: Jul 15, 2026

A Method for Targeted 16S Sequencing of Human Milk Samples

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

Modelling the temporal trajectories of human milk components.

József Baranyi¹, Tünde Pacza², Mayara L Martins²

¹Institute of Nutrition Science, Faculty of Agriculture, Food Sciences and Environmental Management, University of Debrecen, 138 Böszörményi Str, Debrecen, Hungary. baranyi.jozsef@med.unideb.hu.

BMC Pregnancy and Childbirth

|November 11, 2024

Summary

Human milk composition changes over time, with individual mother

Keywords:

Error estimation Food composition Human Milk Longitudinal data Predictive modelling Saturation model

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Last Updated: Jul 15, 2026

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Area of Science:

Human Milk Composition Analysis
Postpartum Nutritional Science
Infant Nutrition Research

Background:

Available data can reveal patterns in human milk (HM) composition over time.
Understanding temporal changes in HM is crucial for infant health and nutrition.

Purpose of the Study:

To model and analyze the temporal trajectories of selected human milk components (HMC-s) in the first four months postpartum.
To investigate the influence of geographical location on HMC-s stationary levels.

Main Methods:

Developed a two-phase primary model to describe HMC-s temporal changes from colostrum to steady state.
Fitted the model to a database of individual HMC trajectories (molecules and molecule-groups).
Conducted secondary modeling to assess the impact of geographical location on HMC stationary levels.

Main Results:

Identified optimal non-equidistant sampling times for experimental designs, with longer intervals post-first week postpartum.
The final stationary level of HMC-s was analyzed in relation to geographical location.

Conclusions:

Individual biological differences between mothers are the primary driver of HMC-s concentration variation.
Geographical location has a lesser impact on HMC-s concentration compared to individual maternal factors.