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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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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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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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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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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Using physiologically based models for clinical translation: predictive modelling, data interpretation or something

Steven A Niederer1, Nic P Smith1,2

  • 1Department of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College London, The Rayne Institute, 4th Floor Lambeth Wing, London, SE1 7EH, UK.

The Journal of Physiology
|April 29, 2016
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Summary
This summary is machine-generated.

Predictive models integrating physiology and patient data offer significant potential for understanding and treating heart disease. Overcoming current challenges is key to achieving personalized cardiac care.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Heart disease remains a major clinical challenge in Western societies.
  • Predictive models integrating physiological understanding and clinical data hold promise for advancing cardiovascular research and treatment.
  • Current realization of this potential is limited.

Purpose of the Study:

  • To discuss the opportunities and challenges in translating predictive models for clinical use in cardiology.
  • To propose distinct modes for clinical translation of biophysically-based models.
  • To outline critical challenges for achieving personalized clinical cardiac care.

Main Methods:

  • Review of successful model translation strategies for biophysically-based models.
  • Analysis of emerging supporting technologies for clinical translation.
  • Proposal of three distinct modes for clinical translation.

Main Results:

  • Identified successful elements in translating biophysically-based models.
  • Highlighted emerging technologies that can support clinical translation.
  • Proposed three distinct modes for clinical translation of cardiac models.

Conclusions:

  • Translating predictive models into clinical practice for heart disease offers significant opportunities.
  • Overcoming identified challenges is crucial for realizing the full potential of these models.
  • Achieving fully personalized clinical cardiac care requires addressing these challenges.