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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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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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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...
303
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

271
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...
271
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

412
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...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

602
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...
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Model-Based Methods in the Biopharmaceutical Process Lifecycle.

Paul Kroll1,2, Alexandra Hofer1, Sophia Ulonska1

  • 1Research Area Biochemical Engineering, Institute of Chemical Environmental and Biological Engineering, Vienna University of Technology, Gumpendorfer Straße 1a - 166/4, A-1060, Vienna, Austria.

Pharmaceutical Research
|November 24, 2017
PubMed
Summary
This summary is machine-generated.

Model-based methods enhance biopharmaceutical process technology by improving experimental design, characterization, and control. Overcoming challenges in user acceptance and tool integration will unlock their full potential for process optimization.

Keywords:
bioprocessdata miningmodellingmonitoringoptimization

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Area of Science:

  • Biopharmaceutical Process Technology
  • Chemical and Pharmaceutical Engineering
  • Biotechnology

Background:

  • Model-based methods are gaining prominence in biopharmaceutical process technology.
  • Applications span experimental design, process characterization, design, monitoring, and control.
  • These methods offer benefits such as reduced experimental effort, enhanced process transparency, and improved robustness.

Purpose of the Study:

  • To provide a comprehensive review of the state-of-the-art in model-based methods within the biopharmaceutical process lifecycle.
  • To identify current challenges and propose potential solutions for wider adoption.
  • To highlight the role of model-based methods in supporting Quality by Design (QbD) and validation initiatives.

Main Methods:

  • Review of existing literature and applications of model-based methods in biopharmaceutical processes.
  • Analysis of benefits, challenges, and potential solutions.
  • Proposal for integrated application throughout the biopharmaceutical process lifecycle.

Main Results:

  • Model-based methods offer significant advantages but are underutilized in bioprocess technology.
  • Key barriers include user acceptance, lack of user-friendly tools, integration issues with control systems, and undefined workflows.
  • Successful implementation requires a holistic approach across the entire process lifecycle.

Conclusions:

  • Model-based methods are crucial for advancing biopharmaceutical process technology, enabling data-driven decision-making and process optimization.
  • Addressing current limitations is essential to fully leverage their potential for robust and efficient bioprocessing.
  • Future directions include integrating model-based approaches from process development through continuous improvement using data mining.