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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
102
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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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...
84
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

161
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
161
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

107
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
107
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

70
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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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

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Published on: May 27, 2014

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Bayesian kinetic modeling for tracer-based metabolomic data.

Xu Zhang1, Ya Su2, Andrew N Lane3,4,5

  • 1Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA. xu.zhang0131@gmail.com.

BMC Bioinformatics
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian framework for analyzing Stable Isotope Resolved Metabolomics (SIRM) data. The new method improves kinetic model parameter estimation and enables robust statistical comparisons between experimental groups, aiding disease research.

Keywords:
Bayesian methodKinetic modelingSIRM

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Computational Biology

Background:

  • Stable Isotope Resolved Metabolomics (SIRM) traces metabolic pathways at the atomic level using stable isotope tracers like enriched glucose.
  • Non-steady-state kinetic modeling of SIRM data uses ordinary differential equations (ODEs) to understand metabolic dynamics in health and disease.
  • Current kinetic modeling faces challenges in parameter fitting and lacks rigorous methods for comparing parameters between groups.

Purpose of the Study:

  • To develop a new Bayesian statistical framework for enhanced parameter estimation and hypothesis testing in non-steady-state kinetic modeling of SIRM data.
  • To provide robust methods for comparing kinetic model parameters across different experimental conditions.

Main Methods:

  • Leveraging prior distributions for expert knowledge integration and robust parameter estimation.
  • Implementing a shrinkage approach for stable variance estimation across metabolites.
  • Utilizing an adaptive Metropolis algorithm for efficient posterior distribution sampling.
  • Employing a reparameterization method for tractable hypothesis testing and inference based on credible intervals.

Main Results:

  • The proposed Bayesian framework offers robust estimation of kinetic model parameters.
  • The method enables statistically rigorous comparisons of model parameters between experimental groups.
  • The framework demonstrated strong performance in simulation studies and a lung cancer case study.

Conclusions:

  • The developed Bayesian framework enhances the analysis of SIRM data through improved parameter estimation and hypothesis testing.
  • This approach facilitates a deeper understanding of metabolic regulation and dysregulation in disease contexts.
  • The freely available software (https://github.com/xuzhang0131/MCMCFlux) supports the application of this framework in biological research.