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Related Experiment Videos

Predicting skin permeability from complex chemical mixtures.

Jim E Riviere1, James D Brooks

  • 1Center for Chemical Toxicology Research and Pharmacokinetics, 4700 Hillsborough Street, North Carolina State University, Raleigh, NC 27606, USA. Jim_Riviere@ncsu.edu

Toxicology and Applied Pharmacology
|September 27, 2005
PubMed
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This study introduces a hybrid quantitative structure permeation relationships (QSPeR) model that improves predictions of chemical absorption through skin by incorporating mixture properties. The new model enhances risk assessment for occupational and environmental chemical exposures.

Area of Science:

  • Dermal Absorption and Toxicology
  • Quantitative Structure-Property Relationships (QSPR)
  • Environmental Health Risk Assessment

Background:

  • Current risk assessments for topical chemical exposure often use simplified models that do not account for complex chemical mixtures.
  • Existing methods rely on individual chemical data or physicochemical properties, potentially underestimating risks from real-world exposures.

Purpose of the Study:

  • To develop and validate a hybrid quantitative structure permeation relationships (QSPeR) model that accurately predicts dermal absorption of chemicals from complex mixtures.
  • To incorporate a mixture factor (MF) into QSPeR models to account for the physicochemical properties of vehicles and additives in chemical formulations.

Main Methods:

  • Utilized porcine skin in flow-through diffusion cells to measure chemical absorption.

Related Experiment Videos

  • Developed hybrid QSPeR models combining individual penetrant descriptors with a mixture factor (MF) based on vehicle/mixture properties.
  • Tested the model with 16 compounds in 344 treatment combinations, including various vehicles (water, ethanol, propylene glycol) and additives (sodium lauryl sulfate, methyl nicotinate).

    • Main Results:

    • The QSPeR model without the mixture factor (MF) showed a correlation (R²) of 0.62 for absorption prediction.
    • Incorporating the MF significantly improved prediction accuracy, increasing the correlation (R²) to 0.78.
    • Key parameters correlated with the MF included refractive index, polarizability, and log (1/Henry's Law Constant) of mixture components.

    Conclusions:

    • The developed hybrid QSPeR model provides a robust approach for quantitating the effects of chemical mixtures on dermal absorption.
    • Incorporating physicochemical properties of mixtures into QSPeR models substantially enhances prediction accuracy for risk assessment.
    • This methodology offers a more realistic approach to evaluating occupational and environmental chemical exposures.