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Related Concept Videos

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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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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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Mechanistic Models: Overview of Compartment Models01:21

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

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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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Cardinality optimization in constraint-based modelling: application to human metabolism.

Ronan M T Fleming1,2,3, Hulda S Haraldsdottir2, Le Hoai Minh2

  • 1Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands.

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|September 12, 2023
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Summary
This summary is machine-generated.

We developed new algorithms to solve complex cardinality optimization problems in constraint-based modeling. Our methods efficiently find approximate solutions for biochemical network analysis, improving upon existing approaches.

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Area of Science:

  • Computational Biology
  • Optimization
  • Systems Biology

Background:

  • Cardinality optimization problems are crucial in constraint-based modeling for tasks like consistency testing and sparse solution computation.
  • Existing methods for these computationally complex problems often lack exact and globally optimal solutions within polynomial time.

Purpose of the Study:

  • To reformulate cardinality optimization problems in constraint-based modeling into a difference of convex functions.
  • To develop and test novel algorithms for approximately solving these reformulated problems.

Main Methods:

  • Approximating the zero-norm with nonconvex continuous functions to transform cardinality optimization problems.
  • Employing a sequence of convex programs to iteratively solve the reformulated problems.
  • Applying the algorithms to biochemical networks, including human metabolic reconstructions.

Main Results:

  • Novel algorithms were implemented and numerically tested, demonstrating efficiency and practical utility.
  • The developed algorithms match or outperform existing related approaches for cardinality optimization in constraint-based modeling.
  • Successful application to extract models for thermodynamic flux balance analysis from human metabolic reconstructions.

Conclusions:

  • The proposed approach provides an effective method for solving challenging cardinality optimization problems in constraint-based modeling.
  • The algorithms offer a practical and efficient solution for analyzing biochemical networks and extracting relevant models.
  • Open-source implementations are available for reproducibility and integration into existing modeling workflows.