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Hormesis in mixtures -- can it be predicted?

Regina G Belz1, Nina Cedergreen, Helle Sørensen

  • 1University of Hohenheim, Institute of Phytomedicine, Department of Weed Science, Otto-Sander-Strasse 5, 70593 Stuttgart, Germany. belz@uni-hohenheim.de

The Science of the Total Environment
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Predicting chemical mixture effects, especially hormesis (beneficial low-dose responses), is possible. Statistical models can forecast mixture impacts and the concentration range of hormesis when compounds exhibit stimulatory effects.

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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Ecotoxicology

Background:

  • Binary mixture studies traditionally focus on inhibitory effects.
  • Hormesis, or stimulatory responses at low chemical concentrations, is increasingly recognized.
  • Predicting mixture effects when hormesis is involved presents a challenge.

Purpose of the Study:

  • To investigate the predictability of mixture effects when one or both components induce hormesis.
  • To evaluate the applicability of existing statistical models (e.g., concentration addition) for hormetic mixtures.
  • To determine if the concentration range and magnitude of hormesis in mixtures can be predicted.

Main Methods:

  • Evaluation of six datasets for Lactuca sativa L. (root length) and Lemna minor L. (areal growth).
  • Application of concentration addition (CA) and curved isobole models.
  • Comparison of predictions from monotonic and biphasic concentration-response models.
  • Testing for linear interpolation of maximum stimulatory responses.

Main Results:

  • Concentration addition (CA) successfully predicted maximal stimulatory (M) and no-observed-effect (LDS) concentrations when EC50 followed CA.
  • Deviations from CA at EC50 were mirrored in M and LDS predictions, described by curved isoboles.
  • Ignoring hormesis in predictions led to slight overestimations of deviation from CA.
  • Four of six datasets showed reasonable linear interpolation for maximum hormetic stimulation.

Conclusions:

  • Mixture effects and the hormesis concentration range are predictable using statistical models when both components induce hormesis.
  • Available models can forecast the extent of growth stimulation in mixtures based on individual chemical responses.
  • The study validates predictive models for complex mixture interactions involving hormesis.