readacross

Read-across is a scientific approach used in chemical risk assessment to predict the properties of a target substance by using data from one or more similar substances, known as source substances. The approach relies on the assumption that chemicals with similar structures, physicochemical properties, and toxicological mechanisms will exhibit related hazard and risk profiles.

Process steps typically include defining the regulatory endpoint, identifying relevant source substances, and establishing a justification for similarity across three aspects: structure, mechanism of action, and exposure context. Data from source substances are used to predict the target's missing information, with uncertainty explicitly characterized. Read-across draws on adjacent chemicals, established categories, and supporting information from quantitative structure–activity relationships (QSAR) or in vitro data to strengthen the prediction.

Regulatory guidance for read-across is provided by international and regional bodies such as the OECD and the European Chemicals Agency (ECHA). The OECD has core principles for read-across, and ECHA provides a Read-Across Assessment Framework and guidance to promote transparent justification, data quality, and uncertainty characterization. Read-across is applied within REACH and CLP processes to support decision making while potentially reducing animal testing.

Strengths include reducing animal testing and increasing data efficiency, while enhancing consistency among related substances. Limitations involve uncertainty in similarity judgments, endpoint specificity, and varying regulatory acceptance. A robust and transparent justification, quality data, and explicit uncertainty assessment are required. Case-by-case evaluation, analog mapping, and, where available, supporting in silico or in vitro data help improve reliability.

In practice, read-across is used across human health and environmental hazard endpoints and across industries including chemicals, cosmetics, and consumer goods. It remains an active area of methodological development, with ongoing case studies illustrating successful and unsuccessful applications.