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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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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: 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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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: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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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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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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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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Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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Systems Biology in ELIXIR: modelling in the spotlight.

Vitor Martins Dos Santos1, Mihail Anton2, Barbara Szomolay3

  • 1Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands.

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Summary

A new ELIXIR Systems Biology Community is established to advance European systems biology infrastructure. It focuses on data integration, interoperability, and training to support personalized medicine and industrial applications.

Keywords:
Biological dataBiomolecular ModelsBiotechnologyELIXIR CommunitiesFAIRNetwork BiologySystems BiologySystems Medicine

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Area of Science:

  • Systems Biology
  • Bioinformatics Infrastructure
  • Computational Biology

Background:

  • ELIXIR is establishing new communities to enhance biological research infrastructure.
  • Systems biology requires robust infrastructure for data, tools, and training.

Purpose of the Study:

  • To outline the establishment of the ELIXIR Systems Biology Community.
  • To define the community's proposed contributions to ELIXIR and the wider systems biology field.
  • To identify key areas and objectives for the community's future activities.

Main Methods:

  • Founding of the ELIXIR Systems Biology Community.
  • Series of meetings to identify key activity areas.
  • Identification of objectives grouped into headline areas and timelines.

Main Results:

  • Seven key areas for future activities were identified: overcoming barriers, data linking, resource interoperability, systems medicine development, modeling as a service, capacity building, and industrial embedding.
  • Objectives were defined under Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding.
  • Short-term (3-year), mid-term (6-year), and long-term (10-year) objectives were established.

Conclusions:

  • The ELIXIR Systems Biology Community is crucial for advancing systems biology infrastructure in Europe.
  • The community's focus on data, tools, standards, training, and cloud access will support advanced biological applications and personalized medicine.
  • A clear set of objectives and timelines will guide the community's contributions to ELIXIR and the global systems biology landscape.