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Related Concept Videos

Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

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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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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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Pharmacokinetic Models: Overview01:20

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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.
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Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

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Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...

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Process modeling in the pharmaceutical industry using the discrete element method.

William R Ketterhagen1, Mary T am Ende, Bruno C Hancock

  • 1Pharmaceutical Research and Development, Pfizer Inc, Groton, Connecticut 06340, USA. william.ketterhagen@pfizer.com

Journal of Pharmaceutical Sciences
|June 20, 2008
PubMed
Summary
This summary is machine-generated.

The discrete element method (DEM) models pharmaceutical processes, offering insights into material properties and operations. Further research is needed to fully understand complex DEM models for bulk solids processing.

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Area of Science:

  • Pharmaceutical Engineering
  • Computational Mechanics
  • Materials Science

Background:

  • The discrete element method (DEM) is a versatile computational tool applied across various industries.
  • DEM simulations are increasingly utilized to understand and optimize pharmaceutical manufacturing processes.

Purpose of the Study:

  • To review current discrete element method (DEM) models for key pharmaceutical processes.
  • To provide insights into the impact of material properties and operating conditions on these processes.
  • To discuss advanced DEM model extensions and identify areas for future research.

Main Methods:

  • Literature review of DEM applications in pharmaceutical processes.
  • Overview of fundamental DEM modeling components.
  • Discussion of advanced DEM model extensions (e.g., particle shape, noncontact forces, interstitial fluids).

Main Results:

  • DEM models offer valuable insights into material transport, storage, blending, granulation, milling, compression, and film coating.
  • Material properties and operating conditions significantly influence pharmaceutical process outcomes.
  • Advanced DEM features like nonspherical particles and interstitial fluids are crucial for complex simulations.

Conclusions:

  • DEM is a powerful tool for analyzing pharmaceutical processes, enhancing understanding of bulk solids behavior.
  • Further investigation into complex DEM models is required to fully elucidate their impact on processing.
  • Optimizing pharmaceutical manufacturing through DEM requires continued development and validation.