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Related Concept Videos

Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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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.
In the case of subcutaneously administered drugs,...
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Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

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A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
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Introduction to Nonlinear Inequalities01:25

Introduction to Nonlinear Inequalities

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Linear and nonlinear inequalities are fundamental for analyzing variable relationships and identifying ranges satisfying specific conditions. A linear inequality involves variables raised only to the first power, resulting in a straight-line graph. This line partitions the coordinate plane into two distinct regions: one that satisfies the inequality and one that does not. Each region represents a set of solutions where the linear relationship holds true under the specified constraint.Nonlinear...
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Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

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Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding or...
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Drug Distribution as One-Compartment Model and Elimination by Nonlinear Pharmacokinetics: Overview01:25

Drug Distribution as One-Compartment Model and Elimination by Nonlinear Pharmacokinetics: Overview

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Drug administration can occur through various routes, each of which may result in a different process of elimination. This process is often mixed with nonlinear and linear processes. It's important to understand that a single drug can be metabolized into different metabolites through parallel processes.
For instance, consider the metabolism of sodium salicylate. This compound is metabolized into two distinct substances: a glucuronide and a glycine conjugate. The rate of conjugation depends...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Nonlinear Mixed-Effects Models for PET Data.

Yakuan Chen, Jeff Goldsmith, R Todd Ogden

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    |August 4, 2018
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    Summary
    This summary is machine-generated.

    Nonlinear mixed-effects (NLME) models offer a more accurate and powerful approach for analyzing dynamic positron emission tomography (PET) data compared to traditional two-stage methods.

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    Area of Science:

    • Nuclear medicine
    • Pharmacokinetics
    • Statistical modeling

    Background:

    • Dynamic PET data analysis traditionally uses a two-stage approach.
    • Stage 1 involves individual subject kinetic parameter estimation.
    • Stage 2 analyzes population effects using statistical methods on individual estimates.

    Purpose of the Study:

    • To explore the application of nonlinear mixed-effects (NLME) models for dynamic PET data.
    • To improve efficiency, precision, and model complexity in PET analysis.
    • To enable simultaneous fitting of data across subjects.

    Main Methods:

    • Simultaneous modeling of subjects using NLME.
    • Inclusion of random effects for kinetic parameters.
    • Direct estimation of population parameters in a joint model.

    Main Results:

    • NLME simulations show improved estimation of group-level effects.
    • NLME demonstrated enhanced power to detect inter-group differences.
    • Application to clinical PET data revealed effects missed by the two-stage method.

    Conclusions:

    • The NLME approach is more accurate and powerful for PET compartment modeling.
    • NLME offers broader methodological scope due to efficiency and stability.
    • This method enhances the analysis of population-level effects in PET studies.