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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...
109
Parametric Survival Analysis: Weibull and Exponential Methods01:14

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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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: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

164
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.
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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...
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Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl.

Ahmed Elmokadem1, Yi Zhang2, Timothy Knab1

  • 1Metrum Research Group, Tariffville, Connecticut, USA.

CPT: Pharmacometrics & Systems Pharmacology
|January 20, 2023
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Summary
This summary is machine-generated.

This tutorial presents a Bayesian framework for physiologically-based pharmacokinetic (PBPK) model analysis. It leverages Bayesian inference to estimate PBPK model parameters and quantify their uncertainty using R and Julia.

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Area of Science:

  • Pharmacokinetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Physiologically-based pharmacokinetic (PBPK) models are mechanistic, incorporating prior biological knowledge.
  • Bayesian inference integrates prior knowledge with data to update parameter estimates and quantify uncertainty.
  • PBPK models benefit from Bayesian methods due to available strong prior information.

Purpose of the Study:

  • To demonstrate a comprehensive population Bayesian PBPK analysis framework.
  • To provide a practical guide for implementing Bayesian PBPK analyses.
  • To showcase the utility of R/Stan/Torsten and Julia/SciML/Turing.jl for PBPK modeling.

Main Methods:

  • Utilized a Bayesian inference approach for parameter estimation in PBPK models.
  • Implemented a full population analysis framework.
  • Employed R/Stan/Torsten and Julia/SciML/Turing.jl for computational implementation.

Main Results:

  • Successfully demonstrated the application of Bayesian tools for PBPK parameter inference.
  • Quantified parameter uncertainty within the PBPK models.
  • Provided a reproducible framework for population Bayesian PBPK analysis.

Conclusions:

  • Bayesian inference is well-suited for PBPK model parameter estimation and uncertainty quantification.
  • The presented framework using R and Julia facilitates robust population Bayesian PBPK analyses.
  • This approach enhances the reliability and interpretability of PBPK models in biological systems.