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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...
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's proficiency in drug...
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.
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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,...
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...

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Updated: Jun 12, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Physiological modeling for technical, clinical and research applications.

Dusan Fiala1, Agnes Psikuta, Gerd Jendritzky

  • 1ErgonSim, Comfort Energy Efficiency, Holderbuschweg 47, D-7056 Stuttgart, Germany. dfiala@ergonsim.de

Frontiers in Bioscience (Scholar Edition)
|June 3, 2010
PubMed
Summary
This summary is machine-generated.

A new dynamic simulation model accurately predicts human thermal responses to various environmental conditions. This tool aids in understanding thermoregulation and thermal comfort across diverse applications, from clothing research to clinical settings.

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Area of Science:

  • Human physiology
  • Environmental science
  • Computational modeling

Background:

  • Diverse technical fields require tools to predict human thermal and thermoregulatory responses.
  • Challenges include environmental heating/cooling, anesthesia, and non-ionizing radiation.

Purpose of the Study:

  • To present a dynamic simulation model for predicting human thermophysiological and perceptual responses.
  • To assess the model's utility in various applications and conditions.

Main Methods:

  • Developed a multi-segmental, multi-layered mathematical model.
  • Incorporated a comfort model using physiological states to predict thermal sensation.
  • Validated the model against climate-chamber experiments and real-world conditions.

Main Results:

  • The model accurately predicts body temperatures, thermoregulatory responses, and heat exchange.
  • It forecasts thermal sensation for steady-state and transient conditions.
  • Validation studies showed predictions within the standard deviation of experimental observations.

Conclusions:

  • The dynamic simulation model effectively predicts human physiological and perceptual responses to thermal challenges.
  • The model demonstrates broad applicability in biometeorology, clothing research, automotive, clinical, and safety fields.