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Related Concept Videos

Toxic Reactions: Overview01:26

Toxic Reactions: Overview

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When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
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Types of Toxins01:36

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Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
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Toxicokinetics for organ-on-chip devices.

Nathaniel G Hermann1, Richard A Ficek1, Dmitry A Markov2

  • 1Department of Physics and Astronomy, Vanderbilt University, PMB 401807, Nashville, TN 37240, USA. shane.hutson@vanderbilt.edu.

Lab on a Chip
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This summary is machine-generated.

A new model accurately predicts chemical doses in organ-on-chip (OOC) devices by accounting for interactions with polydimethylsiloxane (PDMS). This advances the reliability of OOCs for toxicology and pharmacology testing.

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Area of Science:

  • Pharmacology and Toxicology
  • Biomedical Engineering
  • Materials Science

Background:

  • Organ-on-chip (OOC) devices are advanced New Approach Methods (NAMs) for predicting human responses.
  • Polydimethylsiloxane (PDMS) is a common material in OOCs but interacts with hydrophobic chemicals.
  • This interaction alters the actual chemical dose cells experience, impacting OOC reliability.

Purpose of the Study:

  • To develop and validate a comprehensive model for chemical-PDMS interactions.
  • To quantify dose inaccuracies in OOC devices due to PDMS material properties.
  • To improve the predictive accuracy of OOCs in toxicological and pharmacological applications.

Main Methods:

  • Developed a model to characterize chemical partitioning into and diffusion through PDMS.
  • Applied the model to 24 diverse chemicals, including dyes, pollutants, and pesticides.
  • Validated the model's ability to predict time-dependent doses under continuous-flow conditions.

Main Results:

  • Characterized PDMS interactions for 24 chemicals.
  • Obtained physical interaction parameters for each chemical.
  • Demonstrated accurate prediction of time-dependent doses in simulated OOC conditions.

Conclusions:

  • The developed model accurately characterizes chemical-PDMS interactions.
  • This model enables precise dose prediction in OOC devices.
  • The findings enhance the utility of OOCs as reliable NAMs in drug development and toxicology.