The last resort requirement under REACH: From principle to practice.

REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) is a European Union regulation that aims to protect human health and the environment from the risks posed by chemicals. Article 25 clearly states that: "[i]n order to avoid animal testing, testing on vertebrate animals for the purposes of this Regulation shall be undertaken only as a last resort." In practice, however, the standard information requirements under REACH are still primarily filled using animal studies. This paper presents examples illustrating that animal testing is not always undertaken only as a last resort. Six over-arching issues have been identified which contribute to this: (1) non-acceptance of existing animal or non-animal data, (2) non-acceptance of read-across, (3) inflexible administrative processes, (4) redundancy of testing, (5) testing despite animal welfare concerns and (6) testing for cosmetic-only ingredients. We, members of the Animal-Free Safety Assessment (AFSA) Collaboration, who work together to accelerate the global adoption of non-animal approaches for chemical safety assessment, herein propose several recommendations intended to aid the European Commission, the European Chemicals Agency and registrants to protect human health and the environment while avoiding unnecessary animal tests - truly upholding the last resort requirement in REACH.

Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing.

In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.

REACHing for solutions: Essential revisions to the EU chemicals regulation to modernise safety assessment.

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.

How to resolve inconclusive predictions from defined approaches for skin sensitisation in OECD Guideline No. 497.

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.

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system.

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 µg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 µg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 µg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.

Selective Anti-Leishmanial Strathclyde Minor Groove Binders Using an N-Oxide Tail-Group Modification.

The neglected tropical disease leishmaniasis, caused by Leishmania spp., is becoming more problematic due to the emergence of drug-resistant strains. Therefore, new drugs to treat leishmaniasis, with novel mechanisms of action, are urgently required. Strathclyde minor groove binders (S-MGBs) are an emerging class of anti-infective agent that have been shown to have potent activity against various bacteria, viruses, fungi and parasites. Herein, it is shown that S-MGBs have potent activity against L. donovani, and that an N-oxide derivation of the tertiary amine tail of typical S-MGBs leads to selective anti-leishmanial activity. Additionally, using S-MGB-219, the N-oxide derivation is shown to retain strong binding to DNA as a 2:1 dimer. These findings support the further study of anti-leishmanial S-MGBs as novel therapeutics.

A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria.

A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1-3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a '2 out of 3' approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.

Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach.

A previously published fragmentation method for making reliable negative in silico predictions has been applied to the problem of predicting skin sensitisation in humans, making use of a dataset of over 2750 chemicals with publicly available skin sensitisation data from 18 in vivo assays. An assay hierarchy was designed to enable the classification of chemicals within this dataset as either sensitisers or non-sensitisers where data from more than one in vivo test was available. The negative prediction approach was validated internally, using a 5-fold cross-validation, and externally, against a proprietary dataset of approximately 1000 chemicals with in vivo reference data shared by members of the pharmaceutical, nutritional, and personal care industries. The negative predictivity for this proprietary dataset was high in all cases (>75%), and the model was also able to identify structural features that resulted in a lower accuracy or a higher uncertainty in the negative prediction, termed misclassified and unclassified features respectively. These features could serve as an aid for further expert assessment of the negative in silico prediction.

A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces.

Dermal contact with chemicals may lead to an inflammatory reaction known as allergic contact dermatitis. Consequently, it is important to assess new and existing chemicals for their skin sensitizing potential and to mitigate exposure accordingly. There is an urgent need to develop quantitative non-animal methods to better predict the potency of potential sensitizers, driven largely by European Union (EU) Regulation 1223/2009, which forbids the use of animal tests for cosmetic ingredients sold in the EU. A Nearest Neighbours in silico model was developed using an in-house dataset of 1096 murine local lymph node (LLNA) studies. The EC3 value (the effective concentration of the test substance producing a threefold increase in the stimulation index compared to controls) of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n = 45) and external (n = 103) data and accuracy of predictions assessed using a threefold error, fivefold error, European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) and Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classifications. In particular, the model predicts the GHS skin sensitization category of compounds well, predicting 64% of chemicals in an external test set within the correct category. Of the remaining chemicals in the previously unseen dataset, 25% were over-predicted (GHS 1A predicted: GHS 1B experimentally) and 11% were under-predicted (GHS 1B predicted: GHS 1A experimentally). Copyright © 2017 John Wiley & Sons, Ltd.

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.

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.

A systematic scoping review of the neurological effects of COVID-19.

BACKGROUND: The global coronavirus 2019 (COVID-19) pandemic began in early 2020, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In mid-2020 the CIAO (Modelling the Pathogenesis of COVID-19 Using the Adverse Outcome Pathway Framework) project was established, bringing together over 75 interdisciplinary scientists worldwide to collaboratively investigate the underlying biological mechanisms of COVID-19 and consolidate the data using the Adverse Outcome Pathway (AOP) Framework. Neurological symptoms such as anosmia and encephalitis have been frequently reported to be associated with infection with SARS-CoV-2. OBJECTIVE: Within CIAO, a working group was formed to conduct a systematic scoping review of COVID-19 and its related neurological symptoms to determine which key events and modulating factors are most commonly reported and to identify knowledge gaps. DESIGN: LitCOVID was used to retrieve 86,075 papers of which 10,244 contained relevant keywords. After title and abstract screening, 2,328 remained and their full texts were reviewed based on predefined inclusion and exclusion criteria. 991 studies fulfilled the inclusion criteria and were retrieved to conduct knowledge synthesis. RESULTS: The majority of publications reported human observational studies. Early key events were less likely to be reported compared to middle and late key events/adverse outcomes. The majority of modulating factors described related to age or sex. Less recognised COVID-19 associated AO or neurological effects of COVID-19 were also identified including multiple sclerosis/demyelination, neurodegeneration/cognitive effects and peripheral neuronal effects. CONCLUSION: There were many methodological and reporting issues noted in the reviewed studies. In particular, publication abstracts would benefit from clearer reporting of the methods and endpoints used and the key findings, to ensure relevant papers are included when systematic reviews are conducted. The information extracted from the scoping review may be useful in understanding the mechanisms of neurological effects of COVID-19 and to further develop or support existing AOPs linking COVID-19 and its neurological key events and adverse outcomes. Further evaluation of the less recognised COVID-19 effects is needed.

Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity.

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30 years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

COVID-19 through Adverse Outcome Pathways: Building networks to better understand the disease - 3rd CIAO AOP Design Workshop.

On April 28-29, 2021, 50 scientists from different fields of expertise met for the 3rd online CIAO workshop. The CIAO project "Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway (AOP) framework" aims at building a holistic assembly of the available scientific knowledge on COVID-19 using the AOP framework. An individual AOP depicts the disease progression from the initial contact with the SARS-CoV-2 virus through biological key events (KE) toward an adverse outcome such as respiratory distress, anosmia or multiorgan failure. Assembling the individual AOPs into a network highlights shared KEs as central biological nodes involved in multiple outcomes observed in COVID-19 patients. During the workshop, the KEs and AOPs established so far by the CIAO members were presented and posi­tioned on a timeline of the disease course. Modulating factors influencing the progression and severity of the disease were also addressed as well as factors beyond purely biological phenomena. CIAO relies on an interdisciplinary crowd­sourcing effort, therefore, approaches to expand the CIAO network by widening the crowd and reaching stakeholders were also discussed. To conclude the workshop, it was decided that the AOPs/KEs will be further consolidated, inte­grating virus variants and long COVID when relevant, while an outreach campaign will be launched to broaden the CIAO scientific crowd.

Evaluation of the global performance of eight in silico skin sensitization models using human data.

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.

Understanding COVID-19 through adverse outcome pathways - 2nd CIAO AOP Design Workshop.

The CIAO project (Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway framework) aims at a holistic assembly of knowledge to deliver a truly transdisciplinary description of the entire COVID-19 physiopathology starting with the initial contact with the SARS-CoV-2 virus and ending with one or several adverse outcomes, e.g., respiratory failure. On 27-28 January 2021, a group of 50+ scientists from numerous organizations around the world met in the 2nd CIAO AOP Design Workshop to discuss the depiction of the COVID-19 disease process as a series of key events (KEs) in a network of AOPs. During the workshop, 74 such KEs forming 13 AOPs were identified, covering COVID-19 manifestations that affect the respiratory, neurological, liver, cardiovascular, kidney and gastrointestinal systems. Modulating factors influencing the course and severity of the disease were also addressed, as was a possible extension of the investigations beyond purely biological phenomena. The workshop ended with the creation of seven working groups, which will further elaborate on the AOPs to be presented and discussed in the 3rd CIAO workshop on 28-29 April 2021.

Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge.

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways-namely key events (KEs) and key event relationships-to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.