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Related Concept Videos

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: 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...
166
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...
342
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
182
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

215
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.
215
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs
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Modeling Complex Pharmacokinetics of Long-Acting Injectable Products Using Convolution-Based Models With

Roberto Gomeni1, Françoise Bressolle-Gomeni1

  • 1R&D Department, Pharmacometrica, Longcol, La Fouillade, France.

Journal of Clinical Pharmacology
|February 19, 2021
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Summary

Convolution-based models enhance pharmacokinetic (PK) modeling for long-acting injectable (LAI) products. This approach accurately characterizes complex drug release and supports optimal LAI product development through PK simulations.

Keywords:
NONMEMconvolution-based modellong-acting injectable PKnonparametric input

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Area of Science:

  • Pharmacokinetics
  • Drug Delivery Systems
  • Computational Modeling

Background:

  • Long-acting injectable (LAI) products are increasingly used for chronic conditions.
  • Modeling the complex, multiphase drug release from LAI formulations presents significant challenges.
  • Accurate pharmacokinetic (PK) modeling is crucial for effective LAI product development and therapeutic use.

Purpose of the Study:

  • To demonstrate the utility of convolution-based models with piecewise-linear approximation for LAI PK modeling.
  • To characterize complex PK profiles of diverse LAI formulations.
  • To support optimal LAI product development by simulating PK time courses under various dosing strategies.

Main Methods:

  • Convolution-based modeling and simulation implemented in NONMEM.
  • Piecewise-linear approximation of nonlinear drug release functions.
  • Evaluation using PK data from three LAI products (microsphere, nanosuspension, polymer) and a theophylline reference dataset.

Main Results:

  • The convolution-based approach accurately characterized complex PK profiles of different LAI formulations.
  • Simulations effectively addressed key questions for optimal LAI product development.
  • Nonparametric input functions provided superior data description compared to conventional parametric models, evidenced by AIC, BIC, Cmax, and AUC0-t.

Conclusions:

  • Convolution-based modeling offers an enhanced tool for characterizing LAI PK properties.
  • This modeling approach facilitates the simulation of PK time courses for optimizing dosing strategies.
  • The nonparametric input function within convolution models demonstrates improved accuracy for LAI PK analysis.