A strategy for structuring and reporting a read-across prediction of toxicity.

Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested.

A framework to facilitate consistent characterization of read across uncertainty.

A process for evaluating analogues for use in structure activity relationship (SAR) assessments was previously published (Wu et al., 2010) and tested using a series of case studies (Blackburn et al., 2011). SAR-based "read across" approaches continue to be broadly used to address toxicological data gaps. The potential additional uncertainty introduced into risk assessments as a result of application of read across approaches to fill data gaps has been widely discussed (OECD, 2007; ECETOC, 2012; Patlewicz et al., 2013), but to date a systematic framework to guide the characterization of uncertainty in read across assessments has not been proposed. The current manuscript presents both a systematic framework to describe potential areas of additional uncertainty that may arise in read across (evaluated based on the number and suitability of analogues contributing data, severity of the critical effect, and effects and potency concordance), as well as a questionnaire for evaluating and documenting consideration of these potential additional sources of uncertainty by risk assessors. Application of this framework represents a next step in standardizing the read across process, both by providing a means to transparently assign a level of uncertainty to a SAR-based read across assessment and by facilitating consistency in read across conclusions drawn by different risk assessors.