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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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...
Pharmacokinetic Models: Overview01:20

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 assumptions,...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

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.
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

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Updated: May 25, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling.

Michaela Meyer1, Sebastian Schneckener, Bernd Ludewig

  • 1Systems Biology and Computational Solutions, Bayer Technology Services GmbH, Building 9115, 51368 Leverkusen, Germany.

Drug Metabolism and Disposition: the Biological Fate of Chemicals
|February 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to estimate drug-metabolizing enzyme and transporter activity in vivo using gene expression data within physiologically based pharmacokinetic (PBPK) models for better drug development.

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Systems Biology
  • Computational Biology

Background:

  • Drug metabolization and distribution involve complex active processes across multiple organs.
  • Quantifying tissue-specific protein activity in vivo is challenging due to experimental limitations.

Purpose of the Study:

  • To develop a novel approach for estimating in vivo activity of drug-metabolizing enzymes and transporters.
  • To integrate tissue-specific mRNA expression data into physiologically based pharmacokinetic (PBPK) models.

Main Methods:

  • Utilized publicly available databases (ArrayExpress, literature, UniGene) for gene expression data.
  • Developed a customized database to store and preprocess expression data.
  • Constructed PBPK models for pravastatin in humans, incorporating data on specific transporters and metabolizing enzymes in liver, kidney, and intestine.

Main Results:

  • PBPK models integrating gene expression data demonstrated superior performance compared to simpler or randomly assigned models.
  • Accurate prediction of drug pharmacokinetics was achieved.
  • The approach enables simultaneous investigation of drug-drug interactions across all relevant organs.

Conclusions:

  • Integrating relative gene expression data into PBPK models provides a robust method for estimating in vivo enzyme and transporter activity.
  • This approach enhances the accuracy of pharmacokinetic predictions and facilitates the study of drug-drug interactions.
  • The developed methodology offers a valuable tool for drug development and personalized medicine.