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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.