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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

Thomas V Wiecki1, Imri Sofer, Michael J Frank

  • 1Department of Cognitive, Linguistic and Psychological Sciences, Brown University Providence, RI, USA.

Frontiers in Neuroinformatics
|August 13, 2013
PubMed
Summary
This summary is machine-generated.

HDDM is a new Python toolbox for analyzing decision-making data. It uses hierarchical Bayesian methods to estimate parameters from less data, handles outliers, and integrates with fMRI, outperforming other methods.

Keywords:
Bayesian modelingPythondecision-makingdrift diffusion modelsoftware

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychology
  • Neuroimaging

Background:

  • Diffusion models are key for understanding decision-making and its neural basis using response times.
  • Current estimation methods need extensive data and provide only point estimates, limiting statistical power and uncertainty assessment.

Purpose of the Study:

  • Introduce HDDM, a novel Python toolbox for hierarchical Bayesian estimation of diffusion and linear ballistic accumulator models.
  • To provide a flexible, efficient tool for analyzing decision-making data, requiring less data per subject and handling outliers.

Main Methods:

  • Developed a Python-based toolbox (HDDM) for hierarchical Bayesian parameter estimation.
  • Applied HDDM to a real-world decision-making dataset.
  • Conducted parameter recovery studies comparing HDDM with traditional methods.

Main Results:

  • HDDM requires less data per subject/condition compared to non-hierarchical methods.
  • The toolbox supports full Bayesian analysis, outlier handling, and integration with trial-by-trial measurements like fMRI.
  • Parameter recovery studies demonstrated HDDM's superiority over chi-squared quantile and maximum likelihood estimation methods.

Conclusions:

  • HDDM offers a powerful and flexible approach for analyzing decision-making data using hierarchical Bayesian inference.
  • The toolbox enhances statistical power, reduces data requirements, and accommodates complex analyses, including neuroimaging integration.