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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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Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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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.
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Biopharmaceutics and Pharmacokinetics: Overview01:28

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Understanding drugs, drug products, and their performance in pharmaceutical science is pivotal. Drugs, whether simple molecules or complex compounds, are designed to interact with the body's biological systems to diagnose, treat, or prevent diseases. Drug products include various delivery systems such as tablets, capsules, injections, and inhalers. The performance of these drug products is gauged by their ability to deliver the active ingredient to the desired site of action at the...
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Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
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Particle Property Characterization and Data Curation for Effective Powder Property Modeling in the Pharmaceutical

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Computational modeling and machine learning help reduce manufacturing failures by analyzing particle properties. Robust data curation and harmonized strategies are essential for accurate particle size and shape analysis in drug development.

Keywords:
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Area of Science:

  • Pharmaceutical Manufacturing
  • Computational Science
  • Materials Science

Background:

  • Computational modeling, machine learning, and statistical analysis are vital for addressing chemistry, manufacturing, and control failures in solid dosage forms.
  • Particle properties significantly impact drug product manufacturability, necessitating advanced characterization and computational methods.
  • Current challenges include generating and curating reliable particle size and shape data for robust analysis.

Purpose of the Study:

  • To review common errors in particle characterization and data compression.
  • To propose a harmonized strategy for generating robust particle morphology data (size and shape).
  • To discuss data curation approaches and the future outlook for modeling particle properties.

Main Methods:

  • Review of common sources of error in particle characterization techniques.
  • Analysis of data compression methods relevant to particle data.
  • Proposal for a harmonized strategy encompassing sampling, characterization, and data curation.

Main Results:

  • Identified common sources of error in particle characterization and data compression.
  • Outlined a strategy for producing informative particle morphology datasets.
  • Discussed approaches for data curation to support modeling.

Conclusions:

  • Harmonized strategies are crucial for generating reliable particle morphology data.
  • Robust data is essential for successful computational modeling in pharmaceutical manufacturing.
  • Future efforts should focus on improving data quality and curation for advanced modeling of particle properties.