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Related Concept Videos

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

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Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
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Modeling with Differential Equations01:25

Modeling with Differential Equations

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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

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Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

310
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...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
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Predicting the Effects of Interventions: A Tutorial on the Disequilibrium Model.

Kenneth W Jacobs1, Zachary H Morford2, James E King1,3

  • 1Department of Psychology/296, University of Nevada, Reno, NV 89557 USA.

Behavior Analysis in Practice
|June 21, 2017
PubMed
Summary

The disequilibrium approach offers mathematical models for behavior analysis and intervention. This tutorial details these models and provides an Excel tool to predict behavior change, aiding clinical practice.

Keywords:
Contingency managementContingent activityDisequilibrium modelInstrumental activityPremack principleResponse deprivation

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Area of Science:

  • Behavioral Science
  • Applied Behavior Analysis (ABA)

Background:

  • The disequilibrium approach, rooted in the probability-differential and response deprivation hypotheses, offers valuable mathematical models for behavior management.
  • Despite proven effectiveness in behavior prediction and control, these models are underutilized in clinical settings.

Purpose of the Study:

  • To elucidate the disequilibrium approach and its mathematical underpinnings.
  • To adapt these models into a practical tool for applied behavior analysis settings.
  • To facilitate the integration of disequilibrium models into clinical practice.

Main Methods:

  • Detailed explanation of the disequilibrium approach and its theoretical foundations.
  • Adaptation of mathematical models for practical application in behavior modification.
  • Development of a Microsoft Excel® spreadsheet tool for calculating behavior change predictions.

Main Results:

  • The developed Excel tool integrates disequilibrium models to predict the direction and magnitude of behavior change.
  • The tool requires practitioners to input baseline measures and select intervention parameters.
  • Guidance is provided on how practitioners can effectively utilize baseline measures and intervention parameters within the disequilibrium framework.

Conclusions:

  • The disequilibrium approach provides a robust framework for understanding and manipulating behavior.
  • The provided Excel tool serves as a practical resource for clinicians to apply disequilibrium models.
  • Enhanced integration of these models can improve the prediction and control of behavior in applied settings.