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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...
338
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

450
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.
450
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...
661
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
738
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
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...
311
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

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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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Related Experiment Video

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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Quantitative Systems Pharmacology Approaches Applied to Microphysiological Systems (MPS): Data Interpretation and

J Yu1, N A Cilfone1, E M Large2

  • 1Department of Biological Engineering, Massachusetts Institute of Technology Cambridge, Massachusetts, USA.

CPT: Pharmacometrics & Systems Pharmacology
|November 5, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a systems pharmacology approach using Microphysiological Systems (MPS) for more predictive preclinical drug discovery. The integrated experimental and computational methods enhance mechanistic understanding for better in vitro to in vivo translation.

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Area of Science:

  • Pharmacology
  • Biotechnology
  • Computational Biology

Background:

  • Traditional preclinical drug discovery models often lack predictive accuracy.
  • Microphysiological Systems (MPS) offer a promising in vitro platform for drug testing.
  • Integrating experimental and computational approaches is key to improving drug discovery paradigms.

Purpose of the Study:

  • To develop and demonstrate a systems pharmacology approach for Microphysiological Systems (MPS) technology.
  • To enhance the mechanistic detail in preclinical drug discovery models.
  • To improve the translation of in vitro findings to in vivo outcomes.

Main Methods:

  • Utilized a systems pharmacology framework for MPS development and application.
  • Incorporated detailed mechanistic analysis beyond traditional pharmacokinetic/pharmacodynamic (PK/PD) models.
  • Conducted case studies on a liver/immune MPS, including PK data analysis and inflammation response modeling.
  • Performed theoretical investigation of a four-MPS interactome to explore drug absorption, distribution, metabolism, and excretion (ADME) principles.

Main Results:

  • Demonstrated the application of the systems pharmacology approach through case studies on a single MPS.
  • Showcased the utility of a multi-MPS interactome model for understanding complex pharmacological processes.
  • Provided a quantitative framework for designing multi-MPS experiments and operations.

Conclusions:

  • The described systems pharmacology approach significantly enhances the predictive power of MPS for drug discovery.
  • Mechanistic insights derived from MPS can be effectively translated from in vitro to in vivo.
  • This integrated paradigm represents a substantial advancement in preclinical drug development methodologies.