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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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.
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...
70
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

145
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.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
145
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

64
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A

Daniel R Bergman1, Kerri-Ann Norton2, Harsh Vardhan Jain3

  • 1Department of Mathematics, University of Michigan, 530 Church Street, Ann Arbor, MI, 48109, USA.

Bulletin of Mathematical Biology
|December 30, 2023
PubMed
Summary

We developed a new computational method, SMoRe ParS, to efficiently calibrate agent-based models (ABMs) using complex experimental data. This approach improves the accuracy of ABM simulations for biological and biomedical research.

Keywords:
Agent-based modelCancerModel parameterizationParameter identifiabilitySurrogate model

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biomedical Informatics

Background:

  • Agent-based models (ABMs) are crucial for understanding complex biological systems.
  • Calibrating ABMs with high-dimensional parameter spaces and multiscale data remains a significant computational challenge.
  • Existing methods struggle to efficiently connect ABM parameter spaces with multidimensional experimental data.

Purpose of the Study:

  • To extend and validate a novel methodology, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), for computationally efficient ABM calibration.
  • To develop a framework connecting high-dimensional ABM parameter spaces with multidimensional data.
  • To demonstrate the effectiveness of SMoRe ParS in calibrating ABMs using both unidimensional and multidimensional experimental data.

Main Methods:

  • Modified SMoRe ParS to initially use unidimensional data (in vitro cancer cell growth assays) to confine high-dimensional ABM parameter spaces.
  • Extended the approach to constrain parameter spaces using multidimensional data from in vitro cancer cell inhibition assays with oxaliplatin.
  • Validated the approach by comparing ABM simulation accuracy using SMoRe ParS-inferred parameters against a commonly used direct method.

Main Results:

  • The extended SMoRe ParS framework effectively calibrates ABM parameter spaces to multidimensional data.
  • Using SMoRe ParS-inferred parameters resulted in ABM simulations that closely matched experimental data.
  • The surrogate model acts as an effective intermediary between ABMs and experimental data for parameter calibration.

Conclusions:

  • SMoRe ParS provides a robust and scalable strategy for leveraging multidimensional data to inform multiscale ABMs.
  • The method enables efficient exploration of ABM parameter spaces and associated uncertainties.
  • This approach enhances the predictive power and reliability of agent-based modeling in biological and biomedical research.