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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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Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

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The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
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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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The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
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Pharmacodynamic Models: Overview01:27

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Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
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Pharmacodynamics is a scientific field that delves into drugs' intricate biochemical, cellular, and physiological effects on the human body. The study of pharmacodynamics helps us understand how drugs interact with the body and elicit various responses.
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Network-based Approaches in Pharmacology.

Baptiste Boezio1, Karine Audouze1, Pierre Ducrot2

  • 1Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France.

Molecular Informatics
|July 11, 2017
PubMed
Summary

Network pharmacology and chemical networks enhance drug discovery by analyzing cellular and chemical spaces. This approach aids rational drug design and understanding drug mechanisms.

Keywords:
Systems Pharmacologybiological networkchemical space networkdrug-targetdrug-therapy

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Area of Science:

  • Computational biology and cheminformatics
  • Pharmacology and drug discovery

Background:

  • Network-based approaches are crucial for understanding complex drug actions.
  • Network pharmacology analyzes drug responses within cellular or phenotypic networks.
  • Chemical-based networks offer a complementary method for characterizing chemical space.

Purpose of the Study:

  • To discuss recent advancements in network pharmacology and chemical-based networks.
  • To highlight the complementary roles of these network approaches in drug discovery.
  • To introduce a novel network-based approach using drug-target-therapy data.

Main Methods:

  • Review of current literature on network pharmacology and chemical-based networks.
  • Integration of drug-target-therapy data for network construction.
  • Analysis of network properties to understand drug mechanisms.

Main Results:

  • Network pharmacology provides insights into cellular and phenotypic drug responses.
  • Chemical-based networks are effective for exploring chemical space.
  • The combined network approach facilitates rational drug design.
  • The introduced drug-target-therapy network offers a practical example.

Conclusions:

  • Network-based strategies, integrating cellular and chemical information, are vital for modern drug discovery.
  • These approaches enhance the understanding of drug mechanisms and support rational drug design.
  • The presented drug-target-therapy network exemplifies the utility of integrated network analysis.