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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

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.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume of...

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Related Experiment Videos

Coarse-grained models: getting more with less.

A J Rader1

  • 1Department of Physics, 402N. Blackford St., LD154D, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA. ajrader@iupui.edu

Current Opinion in Pharmacology
|October 5, 2010
PubMed
Summary
This summary is machine-generated.

Simplified coarse-grained models aid biomolecular studies. This review highlights commonalities, differences, and guides model selection for specific research questions in molecular dynamics and folding.

Related Experiment Videos

Area of Science:

  • Computational Biology
  • Biophysics
  • Molecular Modeling

Background:

  • Coarse-grained models are increasingly used to simplify complex biomolecular systems.
  • These models aim to capture essential molecular features with fewer variables.
  • Diverse coarse-grained approaches exist for studying dynamics, binding, assembly, and folding.

Purpose of the Study:

  • To review recent advancements in coarse-grained modeling for biomolecules.
  • To highlight commonalities and distinctions among various coarse-grained models.
  • To provide guidance for selecting appropriate coarse-grained models for specific research.

Main Methods:

  • Review of recent literature on coarse-grained biomolecular models.
  • Analysis of common principles and distinguishing features of different models.
  • Synthesis of information to aid in model selection.

Main Results:

  • Coarse-grained models share a goal of identifying minimal variables for realistic molecular description.
  • Recent findings reveal both shared and unique characteristics across different model types.
  • Guidance is offered for researchers navigating the selection of suitable models.

Conclusions:

  • Choosing a coarse-grained model depends on the specific research questions being addressed.
  • Understanding model characteristics is crucial for effective application in biomolecular studies.
  • The proliferation of models necessitates clear criteria for selection.