Lessons learned from read-across case studies for repeated-dose toxicity.

Area of Science:

• Toxicology and chemical risk assessment
• Pharmacokinetics and pharmacodynamics
• New Approach Methodologies (NAMs)

Background:

• Read-across is a method used to predict the toxicity of chemicals based on data from similar substances.
• Uncertainty in read-across predictions can arise from data quality, quantity, and the justification of similarity.
• Chronic health endpoints present particular challenges for read-across due to complex biological mechanisms.

Purpose of the Study:

• To evaluate case studies on read-across predictions for chronic health endpoints.
• To identify and analyze sources of uncertainty in the read-across process.
• To determine the key factors influencing the reliability of read-across predictions.

Main Methods:

• Analysis of existing case studies focusing on read-across for chronic health endpoints.
• Assessment of uncertainty factors, including data quality, quantity, and similarity justifications.
• Evaluation of the role of chemical, toxicokinetic, and toxicodynamic similarity.

Main Results:

• Chemical similarity alone is often insufficient for justifying read-across, particularly for chronic health endpoints.
• Toxicokinetic (e.g., ADME) and toxicodynamic similarity are crucial for reliable read-across predictions.
• New Approach Methodologies (NAMs) like in vitro and in silico tools can strengthen toxicodynamic similarity arguments.
• Metabolism-related toxicokinetic data frequently drives overall uncertainty in read-across assessments.

Conclusions:

• Read-across predictions for chronic health endpoints necessitate strong evidence of toxicokinetic and toxicodynamic similarity.
• Addressing uncertainty requires comprehensive evaluation of scientific arguments and supporting data.
• NAMs offer valuable data for improving the reliability of read-across, especially for toxicodynamic aspects.