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Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
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Related Experiment Video

Updated: Jun 22, 2026

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers
13:14

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Published on: August 22, 2014

Addressing temporal variability when modeling bioaccumulation in plants.

Emma Undeman1, Gertje Czub, Michael S McLachlan

  • 1Department of Applied Environmental Science, Stockholm University, S-106 91 Stockholm, Sweden. emma.undeman@itm.su.se

Environmental Science & Technology
|June 24, 2009
PubMed
Summary

Steady state models may oversimplify bioaccumulation of organic contaminants. A new non-steady state model and chemical partitioning plots help predict contaminant levels more accurately by considering dynamic processes.

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Last Updated: Jun 22, 2026

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers
13:14

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Published on: August 22, 2014

Area of Science:

  • Environmental Chemistry
  • Ecotoxicology
  • Plant Physiology

Background:

  • Steady state models are widely used for predicting organic contaminant bioaccumulation in organisms.
  • However, dynamic processes like growth and environmental fluctuations can lead to inaccuracies in steady state predictions.
  • Temporal variability in chemical uptake and elimination is often overlooked.

Purpose of the Study:

  • To propose a strategy for addressing temporal variability in bioaccumulation modeling.
  • To develop and evaluate a novel non-steady state bioaccumulation model for plants.
  • To utilize chemical partitioning space plots for understanding contaminant dynamics.

Main Methods:

  • Development of a novel non-steady state bioaccumulation model for plants.
  • Parameterization and evaluation of the non-steady state model.
  • Generation of chemical partitioning space plots to visualize time to steady state and dominant fluxes.

Main Results:

  • Identified the time for organic contaminants to reach steady state in plant tissues.
  • Characterized dominant chemical uptake and elimination fluxes based on contaminant properties.
  • Established criteria for the applicability of steady state models versus non-steady state models.

Conclusions:

  • Steady state models are suitable only when exposure duration exceeds the time to reach steady state.
  • Significant parameter variations on the time scale of steady state attainment necessitate non-steady state modeling.
  • Chemical partitioning plots offer valuable insights into contaminant behavior and modeling requirements.