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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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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 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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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...
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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...
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Reduced Animal Models Fitting Only Equations for Phenotyped Animals.

Mohammad Ali Nilforooshan1, Dorian Garrick2

  • 1Livestock Improvement Corporation, Hamilton, New Zealand.

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Summary
This summary is machine-generated.

Reduced animal models simplify genetic evaluations by focusing on phenotyped animals. This approach yields identical breeding values while significantly cutting computational costs.

Keywords:
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Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Statistical genetics

Background:

  • Mixed model equations are computationally intensive for estimating breeding values.
  • Traditional animal models include non-phenotyped ancestors, complicating analyses.
  • Reduced models offer computational efficiency in genetic evaluations.

Purpose of the Study:

  • To develop and validate reduced animal models that exclude non-phenotyped animals.
  • To investigate methods for obtaining identical breeding value solutions using reduced models.
  • To explore the combination of different reduced model strategies.

Main Methods:

  • Formulating reduced animal models by excluding non-phenotyped ancestral animals.
  • Developing back-solving techniques for non-phenotyped animals' breeding values.
  • Extending reduced model concepts to various animal model formulations.
  • Combining reduced models for phenotyped parents and progeny.

Main Results:

  • Reduced models produced identical breeding value solutions compared to full models.
  • Back-solving provided accurate solutions for non-phenotyped animals.
  • Combined reduced models efficiently targeted phenotyped parents and progeny.

Conclusions:

  • Reduced animal models significantly decrease computational demand without sacrificing accuracy.
  • Excluding non-phenotyped animals and utilizing back-solving is a viable strategy for genetic evaluations.
  • The developed methods offer practical improvements for large-scale genetic analyses.