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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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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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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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Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
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Mechanistic Image-Based Modelling: Concepts and Applications.

Denis Menshykau1, Simon Tanaka2

  • 1, Düsseldorf, Germany. denis.menshykau@gmail.com.

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PubMed
Summary
This summary is machine-generated.

Image-based modeling integrates imaging data with computational models to understand complex biological systems. This approach enhances drug discovery and development by improving pharmacokinetic and pharmacodynamic insights.

Keywords:
Biomedical engineeringDevelopmental biologyDrug discovery and developmentImage-based modellingMechanistic modellingModel informed drug developmentModelling and simulation

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Area of Science:

  • Computational biology
  • Biomedical imaging
  • Systems biology

Background:

  • Rapid advancements in imaging generate vast datasets, posing challenges for data interpretation and information extraction.
  • Mechanistic computational modeling offers insights into complex systems but often lacks sufficient data for model development and validation.

Purpose of the Study:

  • To introduce the principles of mechanistic image-based modeling.
  • To review its applications in developmental biology, biomedical research, medical device development, and drug discovery.
  • To highlight the potential of image-based modeling in the pharmaceutical industry.

Main Methods:

  • Image-based modeling leverages imaging data for computational model construction, parameterization, and validation.
  • The approach integrates quantitative imaging with mechanistic computational frameworks.
  • Review of existing literature and case studies demonstrating the application of image-based modeling.

Main Results:

  • Image-based modeling bridges the gap between imaging data and mechanistic understanding.
  • Demonstrated utility across diverse fields including developmental biology and drug development.
  • Successful application in medical device development and pharmaceutical research.

Conclusions:

  • Mechanistic image-based modeling is a powerful approach for extracting biological insights from imaging data.
  • Its implementation in the pharmaceutical industry promises to advance model-informed drug discovery and development.
  • This approach can significantly de-risk drug development by enhancing understanding of pharmacokinetic and pharmacodynamic properties.