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The Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation was created to protect human health and the environment through the better and earlier identification of harmful intrinsic properties of chemical substances on the European market. One of its central aims was the promotion of alternatives to animal testing, yet it has instead become a long tick-box list of in vivo experiments questionable relevance to human health outcomes despite a global trend towards new approach methods (NAMs) in chemical safety assessment. The Chemicals Strategy for Sustainability (CSS), proposed by the European Commission in 2020, is a golden opportunity to revise REACH in a significant and impactful way, yet proposals presented so far have significant negative animal welfare consequences. There is still time to correct the course of the ongoing REACH revision - proposals made herein offer a path towards the promising future intended by the CSS. These proposals are anchored in three vectors of action, varying in level of complexity - from changes that ECHA can implement to improve existing processes, through technical changes aimed at minimizing animal testing and increasing NAM acceptance, to deeper structural changes to establish non-animal testing strategies as the basis for risk assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

In June 2021 the Organisation for Economic Co-operation and Development published Guideline No. 497 on Defined Approaches for Skin Sensitisation (DASS GL). There are two DAs published, known as the 2o3 and the ITS. The 2o3 uses two concordant results from either the DPRA, KeratinoSens™, or the h-CLAT assays to predict hazard (sensitiser/non-sensitiser). The ITS applies a score to results from the DPRA, the h-CLAT and an in silico model to predict United Nations Globally Harmonized System (GHS) sub-categories (1A/1B/Not Classified). The ITS can use Derek Nexus as the in silico model (known as ITSv1) or use OECD QSAR Toolbox (known as ITSv2). As limitations of the individual in chemico/in vitro assays and in silico predictions are carried through to the DAs, inconclusive predictions are possible for chemicals with results in the borderline range, and chemicals with out of domain results. However, these inconclusive predictions can be resolved by applying a weight of evidence approach. Herein, four case studies are presented, each 'inconclusive' for skin sensitisation potential according to both DAs. A weight of evidence approach was applied to each using a robust scientific approach to provide a conclusive prediction, where possible, based on several additional, non-animal lines of evidence., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martyn Chilton reports a relationship with Lhasa Limited that includes: employment. Corresponding author (Donna Macmillan) was previously employed by, and co-author Martyn Chilton is currently employed by, Lhasa Limited. Lhasa Limited produce Derek Nexus, an in silico tool used in one of the defined approaches published by the OECD and described within the manuscript., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Allergic contact dermatitis, or the clinical manifestation of skin sensitization, is a leading occupational hazard. Several testing approaches exist to assess skin sensitization, but in silico models are perhaps the most advantageous due to their high speed and low-cost results. Many in silico skin sensitization models exist, though many have only been tested against results from animal studies (e.g., LLNA); this creates uncertainty in human skin sensitization assessments in both a screening and regulatory context. This project’s aim was to evaluate the accuracy of eight in silico skin sensitization models against two human data sets: one highly curated (Basketter et al., 2014) and one screening level (HSDB). The binary skin sen­sitization status of each chemical in each of the two data sets was compared to the prediction from eight in silico skin sensitization tools (Toxtree, PredSkin, OECD’s QSAR Toolbox, UL’s REACHAcross™, Danish QSAR Database, TIMES-SS, and Lhasa Limited’s Derek Nexus). Models were assessed for coverage, accuracy, sensitivity, and specificity, as well as optimization features (e.g., probability of accuracy, applicability domain, etc.), if available. While there was a wide range of sensitivity and specificity, the models generally performed comparably to the LLNA in predicting human skin sensitization status (i.e., approximately 70-80% accuracy). Additionally, the models did not mispredict the same com­pounds, suggesting there might be an advantage in combining models. In silico skin sensitization models offer accurate and useful insights in a screening context; however, further improvements are necessary so these models may be con­sidered fully reliable for regulatory applications.

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays., (Copyright © 2016 Elsevier Inc. All rights reserved.)