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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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: 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.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Multicompartment Models: Overview01:14

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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.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Related Experiment Video

Updated: Jul 24, 2025

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
09:00

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Multi-dimensional population balance model development using a breakage mode probability kernel for prediction of

Ashley Dan1, Haresh Vaswani1, Alice Šimonová2

  • 1Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.

Pharmaceutical Development and Technology
|July 6, 2023
PubMed
Summary

This study developed a predictive model for milling processes, accurately forecasting granule quality attributes like API content and porosity. The model enhances understanding of how milling conditions impact final drug product quality.

Keywords:
Millingbreakage modegranule quality attributespopulation balance model

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Last Updated: Jul 24, 2025

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Area of Science:

  • Pharmaceutical Engineering
  • Chemical Engineering
  • Materials Science

Background:

  • Milling significantly influences critical granule attributes beyond particle size, including API content and porosity.
  • These attributes directly impact the quality and performance of the final drug product.
  • Predicting milling effects on granule quality is essential for robust pharmaceutical manufacturing.

Purpose of the Study:

  • To develop and validate a predictive model for the Comil milling process.
  • To incorporate granule quality attributes (API content, porosity) into the modeling framework.
  • To dynamically predict breakage modes during milling.

Main Methods:

  • Development of a hybrid population balance model (PBM) for Comil simulation.
  • Validation of the PBM using experimental data, achieving R² > 0.9.
  • Increased PBM dimensionality to include API content and porosity.
  • Implementation of a breakage mode probability kernel for attrition and impact prediction.

Main Results:

  • The developed PBM accurately predicts particle size distribution and granule quality attributes.
  • Model predictions for API content and porosity were successfully generated.
  • The breakage mode kernel dynamically predicted attrition and impact probabilities based on process conditions.

Conclusions:

  • The hybrid PBM provides a robust tool for understanding and predicting milling impacts on granule quality.
  • The model's ability to account for API content and porosity enhances its applicability in drug formulation.
  • Dynamic prediction of breakage modes improves the mechanistic understanding of the milling process.