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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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.
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations01:15

Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations

Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...

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Related Experiment Video

Updated: Jul 9, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

Estimation of optimal modeling weights for a Bayesian-based closed-loop system for propofol administration using the

Tom De Smet1, Michel M R F Struys, Scott Greenwald

  • 1Demed Engineering, Temse, Belgium.

Anesthesia and Analgesia
|November 29, 2007
PubMed
Summary

This study optimized a Bayesian-based closed-loop system for propofol administration using simulations. The developed controller demonstrated accurate prediction errors for maintaining the Bispectral Index (BIS) at various targets.

Related Experiment Videos

Last Updated: Jul 9, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

Area of Science:

  • Anesthesia
  • Pharmacology
  • Control Systems Engineering

Background:

  • Bayesian methods require integrating standard and patient-specific models using Bayesian variances.
  • Bayesian variances control model deviation in closed-loop systems.
  • Optimizing these variances is crucial for effective closed-loop control.

Purpose of the Study:

  • To select optimal Bayesian variances for a model-based closed-loop propofol administration system.
  • To utilize the Bispectral Index (BIS) as the controlled variable.
  • To achieve patient-individualized anesthesia control.

Main Methods:

  • Identified relevant Bayesian variances defining the modeling process.
  • Simulated 625 candidate controller sets on 416 virtual patients.
  • Evaluated controller performance using a BIS offset trajectory simulating a surgical case.

Main Results:

  • Developed and optimized a patient-individualized model-based closed-loop controller using Bayesian optimization.
  • The optimal controller achieved median absolute prediction errors of 12.9 +/- 2.87 at BIS 30, 7.59 +/- 0.74 at BIS 50, and 5.76 +/- 1.03 at BIS 70.
  • Demonstrated successful parameter setting for precise BIS control.

Conclusions:

  • The optimized Bayesian-based closed-loop system shows promise for safe clinical testing.
  • The system is suitable for both induction and maintenance of anesthesia.
  • Clinical introduction should occur under direct anesthesiologist supervision.