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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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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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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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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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A dynamic Cassie-Baxter model.

Tingyi Leo Liu1, Zhiyu Chen, Chang-Jin Kim

  • 1Mechanical and Aerospace Engineering Department, University of California, Los Angeles, California 90095, USA. leolty@ucla.edu.

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Summary
This summary is machine-generated.

This study introduces a new 2-D model for contact-angle hysteresis in the Cassie-Baxter state, improving predictions by incorporating contact-line friction. The generalized 3-D model accurately forecasts diverse experimental data, advancing understanding of liquid behavior on microstructured surfaces.

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Area of Science:

  • Surface Science
  • Fluid Dynamics
  • Materials Science

Background:

  • Contact-angle hysteresis in the Cassie-Baxter state is crucial for liquid behavior on microstructured surfaces.
  • Existing models often fail to accurately predict experimental data due to limitations in dimensionality and understanding of receding contact lines.

Purpose of the Study:

  • To develop a more accurate model for contact-angle hysteresis by addressing limitations in existing theories.
  • To investigate the role of contact-line friction in two-dimensional (2-D) and three-dimensional (3-D) systems.

Main Methods:

  • Conducted a 2-D experiment to eliminate spatial and temporal averaging of receding contact lines.
  • Developed a new 2-D model incorporating contact-line friction.
  • Generalized the 2-D model to a 3-D model by introducing a line solid fraction term.

Main Results:

  • The new 2-D model showed excellent agreement with 2-D experimental results, outperforming existing models.
  • The generalized 3-D model successfully predicted a wide range of 3-D experimental data across different microstructures and receding modes.

Conclusions:

  • Contact-line friction is a key factor in accurately modeling contact-angle hysteresis.
  • The developed 2-D and generalized 3-D models provide a more robust framework for predicting liquid behavior on complex surfaces.