Uncertainties in the Extrapolation of In Vitro Data in Human Risk Assessment: A Case Study of qIVIVE for Imazalil...

Area of Science:

• Toxicology and Risk Assessment
• In Vitro and In Silico Methods
• Regulatory Science

Background:

• New Approach Methodologies (NAMs) offer alternatives to animal testing for hazard characterization.
• Quantitative in vitro-in vivo extrapolation (qIVIVE) is crucial for applying in vitro data to human health risk assessment.
• Current qIVIVE practices often lack robust quantification of uncertainties, hindering regulatory acceptance.

Purpose of the Study:

• To describe and quantify uncertainties associated with qIVIVE when using NAMs for risk assessment.
• To demonstrate the application of qIVIVE with uncertainty quantification using a case study.
• To support the regulatory implementation of NAMs by addressing extrapolation uncertainties.

Main Methods:

• A case study involving a human cell line in vitro test method for imazalil-induced liver steatosis.
• Quantitative in vitro-in vivo extrapolation (qIVIVE) incorporating various extrapolation steps.
• Uncertainty analysis using the Monte Carlo Risk Assessment platform.

Main Results:

• Identified and addressed uncertainties in extrapolations from in vitro to in vivo effects, concentrations, dosimetry, duration, and population variability.
• A primarily quantitative approach was used to assess uncertainties in the imazalil liver steatosis case study.
• The study provides a framework for quantifying qIVIVE uncertainties in NAM-based risk assessments.

Conclusions:

• Quantifying uncertainties in qIVIVE is essential for the reliable regulatory application of NAMs.
• The case study demonstrates the feasibility of a quantitative qIVIVE approach for risk assessment.
• Addressing these uncertainties will facilitate the broader adoption of NAMs in regulatory toxicology.