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Related Concept Videos

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...
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...
Determination of Michaelis Constant and Maximum Elimination Rate01:20

Determination of Michaelis Constant and Maximum Elimination Rate

The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...

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Related Experiment Video

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System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis
09:57

System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis

Published on: April 5, 2017

Estimation of the wild-type minimum inhibitory concentration value distribution.

Stijn Jaspers1, Marc Aerts, Geert Verbeke

  • 1Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.

Statistics in Medicine
|August 16, 2013
PubMed
Summary

This study introduces a new method to estimate the wild-type (normal) bacterial population

Keywords:
antimicrobial resistancecensoringmodel averagingmultinomial distributionwild-type distribution

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High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity
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Last Updated: May 8, 2026

System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis
09:57

System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis

Published on: April 5, 2017

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity
09:09

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity

Published on: November 16, 2016

Area of Science:

  • Microbiology
  • Statistical Modeling
  • Public Health

Background:

  • Antimicrobial resistance (AMR) is a growing global health threat.
  • Monitoring non-wild-type isolates is crucial for understanding AMR.
  • Current methods rely on minimum inhibitory concentration (MIC) values from dilution experiments.

Purpose of the Study:

  • To develop a novel statistical method for estimating the wild-type component in isolate populations.
  • To accurately characterize the wild-type minimum inhibitory concentration (MIC) distribution.
  • To provide a robust approach for monitoring antimicrobial resistance.

Main Methods:

  • Development of a new method based on the multinomial distribution.
  • Utilizing a likelihood framework to compare different distributional assumptions.
  • Employing the Akaike Information Criterion (AIC) and model averaging for parameter selection.

Main Results:

  • The new method effectively estimates the wild-type component of isolate populations.
  • Identified optimal parameters and suitable distributions for wild-type MIC ranges.
  • Demonstrated the utility of model averaging for characterizing wild-type distributions.

Conclusions:

  • The developed method offers an elegant approach to quantify wild-type MIC distributions.
  • Facilitates comparison of distributional assumptions for accurate AMR monitoring.
  • Aids in defining the normal range of MIC values for specific antibiotic-bacterium combinations.