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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...
119
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
132
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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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

96
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

144
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...
144

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High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification.

Max R Mowbray1, Chufan Wu1, Alexander W Rogers1

  • 1Department of Chemical Engineering, Centre for Process Integration, University of Manchester, Manchester, UK.

Biotechnology and Bioengineering
|October 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-step framework using Reinforcement Learning (RL) to build accurate bioprocess models. It effectively captures time-varying and history-dependent behaviors, overcoming key modeling challenges.

Keywords:
historical-dependent kineticshybrid modelingmodel structure identificationreinforcement learningtime-varying parameter estimation

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Area of Science:

  • Bioprocess Engineering
  • Computational Biology
  • Systems Biology

Background:

  • Simulating complex bioprocess dynamics, especially time-varying and history-dependent behaviors, presents a significant challenge.
  • Existing hybrid modeling approaches integrate kinetic and data-driven methods to address these complexities.

Purpose of the Study:

  • To propose a novel two-step framework for constructing high-fidelity bioprocess models.
  • To simultaneously identify kinetic model structures and time-varying parameters using Reinforcement Learning (RL).
  • To quantify time-varying and history-dependent kinetic behaviors in bioprocesses.

Main Methods:

  • A two-step framework combining kinetic model structure speculation with model-free Reinforcement Learning (RL).
  • Step 1: Speculating and combining feasible kinetic model structures from process knowledge.
  • Step 2: Utilizing RL for simultaneous model structure identification and time-varying parameter estimation.

Main Results:

  • The proposed framework efficiently constructs high-fidelity models for bioprocesses.
  • Demonstrated capability in quantifying both time-varying and history-dependent kinetic behaviors.
  • Successfully minimized risks of over-parametrization and over-fitting in model development.

Conclusions:

  • The novel framework offers a powerful approach for general bioprocess modeling.
  • Highlights advantages over existing hybrid modeling and model structure identification techniques.
  • Potential for enhanced predictive modeling in complex biological systems.