The European Commission is committed to phasing out animal testing for chemical safety assessments, as highlighted in its response to the European Citizens’ Initiative ‘Save cruelty-free cosmetics – Commit to a Europe without animal testing’. To achieve this goal, the Commission has been developing a roadmap, in collaboration with EU agencies and stakeholders. The roadmap aims to accelerate many activities contributing to the replacement or reduction of animal use in regulatory testing across a range of sectors. In this paper, we explain how these activities can be conceptualised as Transitional Initiatives, each of which produces outputs leading to outcomes that potentially contribute to the final impact of phasing out animal testing. The Commission’s Joint Research Centre (JRC) has established a dynamic online catalogue of these initiatives which will help to inform strategy and avoid duplicative efforts during the implementation phase of the roadmap. All stakeholders are invited to inspect the catalogue and notify the JRC of any relevant initiatives of their own. This paper explains the rationale behind Transitional Initiatives along with practical details of the notification process.

Computational medicine promises significant advancements in healthcare, using physics-based simulations and artificial intelligence to optimise disease diagnosis, personalise treatment strategies and accelerate medical innovation. Biomedical research efforts are generating a growing number of computational models of human pathophysiology and medical treatments, with advanced applications in areas such as cardiovascular diseases, orthopaedics and cancer diagnosis. However, the widespread adoption of these models is hindered by technological and regulatory barriers. This article provides an overview of the potential impact, needs and challenges of the adoption of in silico medicine in the healthcare ecosystem, with a focus on initiatives to sustain this technology within the European Union's regulatory environment. The article introduces the concept of the 'computational model lifecycle' as a framework to describe the stages from academic research to pre-clinical and clinical applications, analysing key opportunities and challenges in translating these technologies at each stage. These challenges are associated with data management, standards for model credibility assessment, transparency of regulatory frameworks, and clinical integration. The article highlights European initiatives such as the European Health Data Space and the Virtual Human Twins Initiative, aimed at fostering the development and application of computational medicine in healthcare.

The use of adverse effect data from animals as the gold standard in regulatory toxicology has a long tradition dating back to the 1960s. It has also been increasingly criticised, based on both scientific and animal welfare concerns, and yet, animal studies remain the gold standard in most areas of toxicology to this very day. In the 1980s, when the first generation of non-animal methods were evaluated as alternatives to animal testing, it was logical to compare the ‘new’ data obtained with historical animal data. This worked reasonably well for simple endpoints, such as skin and eye irritation, but became problematic for the more complex systemic endpoints, since in these cases, the in vivo effects are not directly comparable to those observed in in vitro systems. While the need to redefine the gold standard is not new, there is still no consensus on how to do so. We propose a consistent principle that avoids the need for animal reference data, while also ensuring an equivalent or better level of protection. We argue that the gold standard can be redefined, or rather bypassed, by focusing on risk management outcomes rather than the outputs of animal methods. This allows us to more efficiently protect human health and the environment, ensuring the safe use of chemicals while also identifying less hazardous chemicals for use as substitutes. We describe how this might work out for two main contexts of use: classification and labelling, and risk assessment. This has implications for the implementation of the EU Commission Roadmap toward the phasing out of animal testing in chemical safety assessments.

The European regulatory framework on chemicals is at a crossroads. There are calls for the framework to be more effective, by better protecting people and the environment. There is also room for it to be more efficient and cost-effective, by harmonizing assessment practices across sectors and avoiding the need for unnecessary testing. At the same time, there is a political commitment to phase out animal testing in chemical safety assessments. In this commentary, we argue that these needs are not at odds with each other. On the contrary, the European Commission's roadmap to phase out animal testing could also be the transition pathway to a more efficient, effective, and sustainable regulatory ecosystem. Central to our proposal is a framework based on biological reasoning in which biological questions can be answered by a choice of methods, with non-animal methods progressively becoming the only choice. Within this framework, a tiered approach to testing and assessment allows for greater efficiency and effectiveness, while also introducing considerations of proportionality and cost-effectiveness. Testing strategies, and their component methods, should be developed in tandem and judged in terms of their outcomes, and the protection levels they inform, rather than their ability to predict the outputs of animal tests.

The aim of this study was to explore the sensitizing potency of several known skin allergens, focusing on their capacity to induce allergic contact dermatitis (ACD). Our approach involves different bioinformatic tools, including UISS-TOX, a simulation platform designed to predict the immune response following allergen exposure. Using eight well-characterized skin sensitizers, including pyridine and hexyl salicylate, we evaluated docking interactions with keratin and Toll-like receptors (TLRs), and we predicted B-cell epitopes, providing insights into potential antigenic sites. The results were integrated into UISS-TOX simulations to observe T helper cell and cytokine dynamics over time. The simulations revealed distinct Th1-mediated responses consistent with ACD, enabling not only the prediction of skin sensitizer potency but also the differentiation of response intensities among the sensitizers. Pyridine, for instance, demonstrated a higher Th1 activation and associated cytokine release than hexyl salicylate, aligning with its stronger sensitizing profile in literature. This study underscores UISS-TOX’s potential as a reliable in silico method for allergenicity prediction, aligning with New Approach Methodologies and reducing the need for animal testing.

The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Chemicals such as trimellitic anhydride and toluene diisocyanate can induce allergic reactions, leading to conditions like occupational asthma and rhinitis. Although they manifest differently, these sensitizers possess shared characteristics, notably their low molecular weight and the capacity to trigger immune responses upon protein binding. This study utilizes the Universal Immune System Simulator to simulate immune reactions to these chemicals. Results indicate that the Universal Immune System Simulator can closely replicate trimellitic anhydride induced immune reactions, primarily showing Th2-type responses with cytokine patterns typical of allergies. These findings underscore the Universal Immune System Simulator's potential in predicting immune reactions to certain chemicals, offering insights into the distinctions between skin and respiratory sensitizers. Upcoming research seeks to further elucidate the immunotoxic pathways of these agents, enabling differentiation based on unique immune reactions.

The body of EU chemicals legislation has evolved since the 1960s, producing the largest knowledge base on chemicals worldwide. Like any evolving system, however, it has become increasingly diverse and complex, resulting in inefficiencies and potential inconsistencies. In the light of the EU Chemicals Strategy for Sustainability, it is therefore timely and reasonable to consider how aspects of the system could be simplified and streamlined, without losing the hard-earned benefits to human health and the environment. In this commentary, we propose a conceptual framework that could be the basis of Chemicals 2.0 - a future safety assessment and management approach that is based on the application of New Approach Methodologies (NAMs), mechanistic reasoning and cost-benefit considerations. Chemicals 2.0 is designed to be a more efficient and more effective approach for assessing chemicals, and to comply with the EU goal to completely replace animal testing, in line with Directive 2010/63/EU. We propose five design criteria for Chemicals 2.0 to define what the future system should achieve. The approach is centered on a classification matrix in which NAMs for toxicodynamics and toxicokinetics are used to classify chemicals according to their level of concern. An important principle is the need to ensure an equivalent, or higher, protection level.

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

In this editorial we reflect on the past decade of developments in predictive toxicology, and in particular on the evolution of the Adverse Outcome Pathway (AOP) paradigm. Starting out as a concept, AOPs have become the focal point of a community of scientists, regulators and decision-makers. AOPs provide the mechanistic knowledge underpinning the development of Integrated Approaches to Testing and Assessment (IATA), including computational models now referred to as quantitative AOPs (qAOPs). With reference to recent and related works on qAOPs, we take a brief historical perspective and ask what is the next stage in modernising chemical toxicology beyond animal testing.

Toxicology in the 21st Century has seen a shift from chemical risk assessment based on traditional animal tests, identifying apical endpoints and doses that are "safe", to the prospect of Next Generation Risk Assessment based on non-animal methods. Increasingly, large and high throughput in vitro datasets are being generated and exploited to develop computational models. This is accompanied by an increased use of machine learning approaches in the model building process. A potential problem, however, is that such models, while robust and predictive, may still lack credibility from the perspective of the end-user. In this commentary, we argue that the science of causal inference and reasoning, as proposed by Judea Pearl, will facilitate the development, use and acceptance of quantitative AOP models. Our hope is that by importing established concepts of causality from outside the field of toxicology, we can be "constructively disruptive" to the current toxicological paradigm, using the "Causal Revolution" to bring about a "Toxicological Revolution" more rapidly.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This inves-tigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharma-ceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. Emphasis is placed on quantitative structure-activity relationship (QSAR) models implemented by means of a range of software tools. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered.

The Threshold of Toxicological Concern (TTC) is an exposure threshold below which there is no appreciable risk to human health. There are two main approaches: TTC values based on cancer potency data from which a one in a million excess lifetime risk is estimated and TTC values based on non-cancer effects. For the latter approach, a distribution is typically fitted to the No Observed Adverse Effect Levels (NOAELs) from repeat dose toxicity studies from which a 5th percentile value is taken and adjusted using an uncertainty factor (usually this is 100). Established TTC values are those based on oral chronic studies that were first developed by Munro et al. (1996) who subcategorised chemicals into one of three Cramer structural classes (Cramer et al., 1978). Kroes et al. (2004) presented a tiered TTC approach that established several human exposure thresholds spanning four orders of magnitude where the lowest TTC was for substances presenting structural alerts for genotoxicity (0.15 μg/d), to the next tier for organophosphates/carbamates (18 μg/d) and the remaining higher TTC values representing the same three Cramer classes originally derived by Munro et al. (1996). The World Health Organization (WHO) and the European Food Safety Authority (EFSA) (European Food Safety Authority and World Health Organization, 2016; EFSA et al., 2019) have determined that the TTC approach is a sound and fit-for-purpose risk assessment tool, with a number of caveats, in cases where chemical-specific repeat dose toxicity data are not available. More recent work has explored extending the dataset underpinning the original Cramer TTC values to demonstrate its protection and robustness (Yang et al., 2017). The work by Yang et al. (2017) was undertaken under the auspices of the EU SEURAT-1 project COSMOS whereupon a COSMOS TTC dataset was developed. This comprised 552 substances for which the toxicity data and chemical structural identifiers were carefully curated. This federated dataset was then used to derive new TTC values for two of the Cramer classes (I and III). The derived values (46 and 2.3 μg/kg bw/d respectively) have since been adopted as the current recommendations by the SCCS in relation to cosmetic substances (SCCS, 2021). In addition, a total of 476 chemicals from the RIFM (Research Institute for Fragrance Materials) dataset were added to the COSMOS/Munro TTC database for analysis, in particular bolstering Cramer class II chemicals from 40 chemicals to 111 chemicals. The derived TTC values of 49, 12.7, and 2.9 μg/kg bw/day for Cramer classes I, II or III confirm the adequacy ofMunro TTC values and further support the use of TTC for safety assessment of fragrance materials (Patel et al., 2020). Thus, these above results show that cosmetic ingredients are sufficiently represented structural classes in the current TTC dataset that had been originally developed for food. The TTC concept is scientific and convincing for it to be applied in cosmetic safety assessment. In Edited and reviewed by: Andrea Richarz, European Chemicals Agency, Finland

Immunotoxicity hazard identification of chemicals aims to evaluate the potential for unintended effects of chemical exposure on the immune system. Perfluorinated alkylate substances (PFAS), such as perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persistent, globally disseminated environmental contaminants known to be immunotoxic. Elevated PFAS exposure is associated with lower antibody responses to vaccinations in children and in adults. In addition, some studies have reported a correlation between PFAS levels in the body and lower resistance to disease, in other words an increased risk of infections or cancers. In this context, modelling and simulation platforms could be used to simulate the human immune system with the aim to evaluate the adverse effects that immunotoxicants may have. Here, we show the conditions under which a mathematical model developed for one purpose and application (e.g., in the pharmaceutical domain) can be successfully translated and transferred to another (e.g., in the chemicals domain) without undergoing significant adaptation. In particular, we demonstrate that the Universal Immune System Simulator was able to simulate the effects of PFAS on the immune system, introducing entities and new interactions that are biologically involved in the phenomenon. This also revealed a potentially exploitable pathway for assessing immunotoxicity through a computational model.

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

In many domains regulating chemicals and chemical products, there is a legal requirement to determine skin sensitivity to allergens. While many in vitro assays to detect contact hypersensitivity have been developed as alternatives to animal testing over the past ten years and significant progress has been made in this area, there is still a need for continued investment in the creation of techniques and strategies that will allow accurate identification of potential contact allergens and their potency in vitro. In silico models are promising tools in this regard. However, none of the state-of-the-art systems seems to function well enough to serve as a stand-alone hazard identification tool, especially in evaluating the possible allergenicity effects in humans.The Universal Immune System Simulator, a mechanistic computational platform that simulates the human immune system response to a specific insult, provides a means of predicting the immunotoxicity induced by skin sensitisers, enriching the collection of computational models for the assessment of skin sensitization.Here, we present a specific disease layer implementation of the Universal Immune System Simulator for the prediction of allergic contact dermatitis induced by specific skin sensitizers.

New Approach Methodologies (NAMs) are considered to include any in vitro, in silico or chemistry-based method, as well as the strategies to implement them, that may provide information that could inform chemical safety assessment. Current chemical legislation in the European Union is limited in its acceptance of the widespread use of NAMs. The European Partnership for Alternative Approaches to Animal Testing (EPAA) therefore convened a 'Deep Dive Workshop' to explore the use of NAMs in chemical safety assessment, the aim of which was to support regulatory decisions, whilst intending to protect human health. The workshop recognised that NAMs are currently used in many industrial sectors, with some considered as fit for regulatory purpose. Moreover, the workshop identified key discussion points that can be addressed to increase the use and regulatory acceptance of NAMs. These are based on the changes needed in frameworks for regulatory requirements and the essential needs in education, training and greater stakeholder engagement as well the gaps in the scientific basis of NAMs.