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Related Concept Videos

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Related Experiment Video

Updated: Jun 14, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

Probabilistic Concentration-Response Modeling and Risk Prioritization of Defined Mixtures of PFAS Using Human In

Lucie C Ford1, Hsing-Chieh Lin1, Weihsueh A Chiu1

  • 1Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, 77843 Texas, United States.

Environmental Science & Technology
|June 12, 2026
PubMed
Summary

Directly testing per- and polyfluoroalkyl substances (PFAS) mixtures in human cells is more health-protective than component-based models. This approach reveals potent PFAS mixtures and identifies sensitive liver cell models for tiered testing strategies.

Keywords:
PFAS mixturesconcentration additionconcentration−response modelinghuman in vitro assaysmixture risk assessment

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Evaluating Toxicity of Chemicals using a Zebrafish Vibration Startle Response Screening System
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Evaluating Toxicity of Chemicals using a Zebrafish Vibration Startle Response Screening System

Published on: January 12, 2024

Area of Science:

  • Environmental Health
  • Toxicology
  • Cell Biology

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with known adverse health effects for some individual compounds.
  • Limited data exists on the health impacts of complex PFAS mixtures, hindering accurate risk assessment.
  • Realistic exposure scenarios are crucial for evaluating the biological effects of PFAS mixtures.

Purpose of the Study:

  • To assess the biological effects of 20 defined PFAS mixtures across eight human cell types.
  • To compare experimental mixture toxicity with predictions from concentration addition (CA) models.
  • To identify sensitive human cell models for evaluating PFAS mixture toxicity.

Main Methods:

  • Exposure of eight human cell types (including iPSC-derived cells and primary hepatocytes) to five 10-fold dilutions of PFAS mixtures.
  • Measurement of cell-specific viability and functional endpoints.
  • Modeling concentration-response data to derive mixture-specific points of departure (PODs) and comparing them with CA model predictions.

Main Results:

  • PFAS mixtures derived from prior in vitro bioactivity and highly contaminated water samples exhibited the highest potency.
  • HepG2 cells, primary hepatocytes, and iPSC-derived neurons were identified as the most sensitive cell types.
  • Concentration addition (CA) models demonstrated low accuracy and significantly underestimated the potency of PFAS mixtures (approximately 300-fold).

Conclusions:

  • Direct experimental testing of PFAS mixtures provides more health-protective toxicity estimates compared to component-based models like CA.
  • A tiered testing strategy prioritizing sensitive human liver cell models is supported by these findings.
  • Understanding the complex interactions within PFAS mixtures is essential for accurate environmental and health risk assessments.