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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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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.
On...
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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: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

94
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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Global Optimization Approach for Parameter Estimation in Stochastic Dynamic Models of Biosystems.

Carlos Sequeiros, Irene Otero-Muras, Carlos Vazquez

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    Accurately estimating parameters in stochastic dynamic models is crucial for understanding biological systems. This study introduces a novel hybrid approach combining global optimization and advanced simulation techniques for efficient and accurate parameter estimation, even in highly stochastic regimes.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Biophysics

    Background:

    • Mechanistic dynamic models are vital for understanding biomolecular networks.
    • Biochemical stochasticity is significant at the single-cell level with low molecule counts.
    • Deterministic models are inadequate for highly stochastic biological systems.

    Purpose of the Study:

    • To address the challenge of parameter estimation in stochastic dynamic models.
    • To develop a novel strategy for accurate parameter estimation in systems with high molecular noise.
    • To enable parameter estimation in highly stochastic regimes far from the thermodynamic limit.

    Main Methods:

    • A hybrid approach combining global optimization (stochastic-deterministic) and tailored stochastic simulation techniques.
    • Utilized a Partial Integro-Differential Equation (PIDE) model solved via a semilagrangian method for dense population data.
    • Developed efficient parallel implementations for multi-core CPUs and GPUs to accelerate simulations.

    Main Results:

    • The novel strategy successfully estimated parameters in four challenging systems: Lotka-Volterra, S. cerevisiae polarization, genetic toggle switch, and circadian oscillator.
    • Achieved results in very reasonable computation times, often minutes on standard hardware.
    • Demonstrated significant speedups compared to recent alternative methods.

    Conclusions:

    • The proposed method enables parameter estimation for stochastic dynamic models, particularly in systems with low molecule numbers and high intrinsic noise.
    • The PIDE approximation to the Chemical Master Equation (CME) is valid for very low copy numbers, extending applicability beyond SDE and Fokker-Planck approximations.
    • The developed computational tools offer efficient and accurate solutions for parameter estimation in complex biological systems.