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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

898
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...
898
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

589
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...
589
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

716
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
716
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

335
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...
335
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

428
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...
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Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

Determination of Multiple Dosing Parameters: Loading and Maintenance Doses

401
A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
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Related Experiment Video

Updated: May 7, 2026

High Throughput Microfluidic Rapid and Low Cost Prototyping Packaging Methods
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Model-Based Active Food Packaging: Materials-Transport Coupling for State-Dependent Mobility Maps, Right-Sized

Zhou Qin1, Yuqing Qi1, Yuhong Xue1

  • 1Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, China.

Comprehensive Reviews in Food Science and Food Safety
|May 6, 2026
PubMed
Summary
This summary is machine-generated.

Active food packaging now offers programmable mass transfer, controlling active ingredient release for enhanced safety and performance. This review presents a design framework for optimizing active packaging systems, focusing on material science and package engineering.

Keywords:
food‐contact materialsheadspace engineeringmigration controlpackagingpartition coefficient

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Area of Science:

  • Food Science and Technology
  • Materials Science
  • Chemical Engineering

Background:

  • Active food packaging has evolved from static barriers to dynamic systems controlling active compound release.
  • Existing reviews often categorize by material or function, lacking an integrated design perspective.

Purpose of the Study:

  • To introduce an integrated design framework for programmable active food packaging.
  • To provide a comprehensive overview of current advancements and future directions in active packaging technology.

Main Methods:

  • The review synthesizes information on programmable mass transfer principles and design levers.
  • It analyzes various carrier types, architectures, and active functions (e.g., oxygen scavenging, antimicrobial).
  • Emphasis is placed on developing operating-window parameter maps and safety-by-design strategies.

Main Results:

  • Programmable mass transfer relies on controlling active loading, carrier affinity, layer properties, and package geometry.
  • The framework incorporates state-dependent mobility maps, dosing strategies, and transferability criteria.
  • Key considerations include retortability, safety, and circularity within a scale-up roadmap.

Conclusions:

  • An integrated design framework enables precise control over active packaging performance.
  • Future advancements require mechanistic models, data-driven surrogates, and in-pack sensing for robust, scalable solutions.
  • Safety-by-design and recyclability are crucial for next-generation active food packaging.