Grouping of substances and read-across

Grouping of substances and read-across is one of the most commonly used alternative approaches for filling data gaps in registrations submitted under REACH. This approach uses relevant information from analogous (‘source') substances to predict the properties of ‘target' substances. If the grouping and read-across approach is applied correctly, experimental testing can be reduced as there is no need to test every target substance.

For each standard information requirement that applies, registrants must indicate whether they are making an adaptation using read-across, and they must justify its use.

Registrants need to make sure that their approach falls within the conditions for using grouping and read-across approaches set out in Annex XI, section 1.5 of the REACH Regulation. A read-across approach can also support a conclusion for a property using a weight-of-evidence approach.

ECHA's guidance and other support material indicate how to build and report read-across cases. The grouping of substances and read-across approach needs to be adequately and appropriately documented. This should cover, among other things, the assumptions made and the conclusions drawn. Key data should be easily identifiable with appropriate references to the substance dataset (e.g. IUCLID substance dataset).

The justification provided by registrants is assessed by ECHA to see if it fulfils the legal requirements.


Illustrative example

ECHA has developed an illustrative example of a grouping of substances and read-across approach to support companies in complying with their obligations under REACH. The illustrative example includes several elements:

  • Part 1: An Introductory Note which provides background information on read-across including general considerations and addresses shortcomings commonly identified by ECHA when evaluating registration dossiers.
  • Part 2: An illustrative example for a hypothetical substance outlining the level of information expected to be provided. It includes explanatory comments expanding on the reasoning and approach taken.


Read-across Assessment Framework

The ECHA Read-Across Assessment Framework (RAAF) structures the scientific evaluation of grouping and read-across approaches under REACH.

The RAAF was needed as, given the variety of different read-across cases that might be made, the assessment of read-across can be technically challenging. ECHA is now using the RAAF to make sure that assessing grouping and read-across used for human health endpoints under dossier evaluation is consistent and transparent.

This publication also provides insights for registrants making use of expertise in read-across on how to assess, and improve where they can, their explanations of why and how read-across can be used.

Using the RAAF

The RAAF aims to ensure that crucial scientific aspects of the grouping and read-across are evaluated consistently. When experts apply the RAAF in read-across, it will result in a structured assessment of the strengths and weaknesses of the read-across and identify possible shortcomings in the documentation, scientific reasoning and/or supporting evidence. The outcome of an assessment using the RAAF is a conclusion on whether or not the read-across may be scientifically acceptable.

Structure of the RAAF

Read-across approaches are assessed through the use of different scenarios and their respective assessment elements and assessment options. The scenarios describe different grouping and read-across approaches.

Each scenario has several assessment elements which are crucial when judging how valid and reliable a read-across is. A read-across case is appraised against each of the respective assessment elements. The assessment elements contain detailed explanations and examples. For each assessment element, an assessor is guided through a series of questions to select the most appropriate assessment option (conclusion) for that element. 


  • Make use of ECHA's Read–Across Assessment Framework to check the robustness of your read-across adaptation.
  • Give a hypothesis-driven justification why the data from one substance can be used to fill the data gap for another substance. Do that for each proprty.
  • Analyse experimental data for contradictions against the proposed hypothesis. Justify read-across adequately and provide supporting and credible information.
  • Specify the identity of all substances used. Consider also impurities and potentially different substance compositions when developing a read across argument.
  • Show how structural similarity and dissimilarity justify the prediction.
  • Create a data matrix, highlighting trends within the category.