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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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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.
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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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Nonlinear Pharmacokinetics: Overview01:19

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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.
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Drug Classes and Categories01:25

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
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Term Selection for a Class of Separable Nonlinear Models.

Min Gan, Guang-Yong Chen, Long Chen

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    |April 17, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a two-step method for selecting critical terms in nonlinear models, optimizing parameters and using the least absolute shrinkage and selection operator (LASSO) for sparsity. The approach effectively identifies key terms, leading to more parsimonious and accurate models.

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    Area of Science:

    • Statistics
    • Machine Learning
    • Computational Science

    Background:

    • Term selection is crucial for building parsimonious and interpretable nonlinear models.
    • Overparameterized models can lead to poor generalization and estimation difficulties.

    Purpose of the Study:

    • To develop an efficient algorithm for automatic term selection in separable nonlinear models.
    • To improve model performance and interpretability through parsimony.

    Main Methods:

    • A two-step optimization strategy combining variable projection for nonlinear parameter estimation.
    • Application of the least absolute shrinkage and selection operator (LASSO) for automatic term selection and sparsity.
    • Iterative refinement of the selection and optimization process.

    Main Results:

    • The proposed algorithm successfully identifies critical terms in overparameterized nonlinear models.
    • Demonstrated effectiveness on exponential and neural network-based models.
    • The resulting parsimonious models exhibit superior performance compared to existing methods.

    Conclusions:

    • The developed method provides an effective approach for term selection in complex nonlinear modeling.
    • Achieving model parsimony through automatic term selection enhances predictive accuracy and understanding.