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Related Concept Videos

Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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)...
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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.
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Mechanistic Models: Overview of Compartment Models01:21

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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...
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.
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The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...

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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Published on: February 13, 2021

Piecewise nonlinear mixed-effects models for modeling cardiac function and assessing treatment effects.

Hyejeong Jang1, Daniel J Conklin, Maiying Kong

  • 1Department of Bioinformatics and Biostatistics, SPHIS, University of Louisville, Louisville, KY, USA.

Computer Methods and Programs in Biomedicine
|December 21, 2012
PubMed
Summary

Mixed-effects models effectively analyze cardiac function in isolated mouse hearts during ischemia/reperfusion. These models reveal how glutathione S-transferase P1/P2 gene deletion impacts heart performance, offering new physiological study applications.

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Area of Science:

  • Physiology
  • Pharmacology
  • Biostatistics

Background:

  • Longitudinal data analysis requires methods that account for within-subject correlations.
  • Mixed-effects models are well-suited for longitudinal data, capturing individual and population response profiles.
  • Glutathione S-transferase P1/P2 (GSTP) plays a role in cellular protection, but its impact on cardiac function during injury is not fully understood.

Purpose of the Study:

  • To apply mixed-effects models to analyze cardiac function in isolated Langendorff-perfused mouse hearts.
  • To investigate the effects of ischemia/reperfusion injury on cardiac function variables.
  • To evaluate the impact of GSTP gene deletion on cardiac function compared to wild-type mice.

Main Methods:

  • Utilized piecewise nonlinear mixed-effects models and a change point nonlinear mixed-effects model.
  • Measured cardiac function variables: heart rate, coronary flow, and left ventricle developed pressure (LVDP).
  • Analyzed data from isolated, Langendorff-perfused hearts of GSTP gene knockout and wild-type mice before and during ischemia/reperfusion.

Main Results:

  • Developed models to describe the dynamics of cardiac function variables during the experiment.
  • Quantified alterations in cardiac function due to ischemia/reperfusion injury.
  • Demonstrated significant differences in cardiac function variables between GSTP gene knockout and wild-type mice.

Conclusions:

  • Mixed-effects models provide a robust framework for analyzing complex physiological data from isolated perfused hearts.
  • The study highlights the utility of these models in understanding the role of specific genes, like GSTP, in cardiac response to injury.
  • Findings support a novel application of mixed-effects models in physiological and pharmacological research.