Altex-Alternatives To Animal Experimentation最新文献

Prediction of hepatotoxicity in humans remains an unresolved challenge. Recently, an in-vitro/in-silico-method was established to predict blood concentrations of test compounds with an increased risk of causing human hepatotoxicity. In the present study, we addressed the question whether gene expression data can improve the quality of hepatotoxicity prediction compared to cytotoxicity analysis alone. A particular challenge is that high-dimensional gene expression data must be summarized into variables that allow for the determination of the lowest test compound concentration that causes altered gene expression. To address this challenge, we analyzed 60 hepatotoxic and non-hepatotoxic substances in a concentration dependent manner for cytotoxicity and expression of 3,524 probes, whose expression were previously reported to be influenced by hepatotoxicants. The toxicity separation index (TSI) was applied to quantify how well specific summary variables of gene expression are able to differentiate between the set of hepatotoxic and non-hepatotoxic substances. The best TSI was obtained when the lowest concentration of a test compound was considered positive that led to differential expression of two genes when compared to vehicle controls. Furthermore, the best gene expression-based summary variable was superior to cytotoxicity-based variables alone, and the combination of the best summary variables of gene expression and cytotoxicity data further improved the TSI compared to each category alone. In conclusion, the method used to derive summary variables of gene expression is critical and the best summary variables improve the prediction of hepatotoxic substances in relation to oral doses and blood concentrations in humans.

Potency and quantitative risk assessment are essential for determining safe concentrations for the formulation of potential skin sensitizers into consumer products. Several new approach methodologies (NAMs) for skin sensitization hazard assessment have been developed, validated, and adopted in OECD test guidelines. However, work is ongoing to develop NAMs for predicting skin sensitization potency on a quantitative scale for use as a point of departure (POD) in next-generation risk assessment (NGRA). GARDskin Dose-Response (DR) is an adaptation of the validated GARDskin assay (OECD TG 442E), and the readout of the assay is a quantitative potency prediction similar to the No Expected Sensitization Induction Level (NESIL) value (µg/cm2). The goal of this study was to evaluate the performance of the GARDskin DR assay for potency prediction of fragrance ingredients. One hundred (100) fragrance ingredients from a reference database covering varied structural reactivity domains and potency were tested in GARDskin DR. Materials tested had varied protein-binding reactivity alerts, including Schiff base, Michael addition, SN2, and acylation. Potency categories were predicted with a total accuracy of 37% and an approximate accuracy (exact match or off by 1 category) of 81%. Combining predicted weak and very weak categories increased total accuracy to 53% and approximate accuracy to 98%. The mean prediction error for the NESIL and local lymph node assay (LLNA) EC3 was 3.15- and 3.36-fold, respectively. Based on the results of this study, GARDskin DR is a promising predictor of skin sensitization potency with an applicability domain covering a wide range of fragrance ingredient reaction mechanisms, increasing the confidence in using the assay to conduct NGRA, ultimately reducing the need for animal testing.

Chemical safety assessment still heavily relies on animal testing, presenting ethical dilemmas and limited human predictive value. New approach methodologies (NAMs), including in vitro and in silico techniques, offer alternative solutions. In silico toxicology has made progress in predicting chemical effects but frequently lacks biological mechanistic foundations. Recent developments focus on mechanistic understanding of adverse effects inflicted by chemicals, as embedded in (quantitative) adverse outcome pathways (AOPs). However, there is a demand for more detailed mechanistic insights at the gene and cell levels, encompassing both pathology and physiology. Drawing inspiration from the Disease Maps Project, this paper introduces Physiological Maps (PMs) as comprehensive graphical representations of biochemical processes related to specific organ functions. PMs are standardized using Systems Biology Graphical Notation and controlled vocabularies and annotations. Curation guidelines have been developed to ensure reproducibility and usability. This paper presents the methodology used to build PMs, emphasizing the essential collaboration between domain experts and curators. PMs offer user-friendly, standardized visualization for data analysis and educational purposes. Enabling a better understanding of (patho)physiology, they also complement and support the development of AOPs by providing detailed mechanistic information at the gene and cell level. Furthermore, PMs contribute to developing in vitro test batteries and to building (dynamic) in silico models aiming to predict the toxicity of chemicals. Collaborative efforts between the toxicology and systems biology communities are crucial for creating standardized and comprehensive PMs, supporting and accelerating the development of human-relevant NAMs for next-generation risk assessment.

The regular workshops held by the Center for Alternatives to Animal Testing (CAAT) on biology-inspired microphysiological systems (MPS) taking place every four years, have become a reliable measure to assess fundamental scientific, industrial and regulatory trends for translational science in the MPS-field from a bird's eye view. The 2023 workshop participants at that time concluded that the technology as used within academia has matured significantly, underlined by the broad use of MPS and the steadily increasing number of high quality research publications - yet, broad industry adoption of MPS has been slow, despite strong interest. Academic research using MPS primarily aims to accurately recapitulate human biology in MPS-based organ models in areas where traditional models have been lacking key elements of human physiology, thereby enabling breakthrough discoveries for life sciences. Examples of these developments are summarized in the report presented here. In addition, we focus on key challenges identified during the previous workshop around progress made in bridging gaps between stakeholders between academia, regulatory agencies and industry on one hand, as well as overcoming hurdles to gain confidence in, and acceptance of MPS-derived data - the latter being of particular importance in a regulatory environment. The status of implementation of the recommendations detailed in the 2019 report have been reviewed. We conclude that communication between stakeholders has improved significantly, while recommendations related to regulatory acceptance still need to be implemented. Participants noted that the remaining challenges for increased translation of these technologies to industrial use and regulatory decision-making will not be fully solvable by basic academic research alone. Rather, more efforts into well-defined context-of-use qualifications are needed, together with increased standardization making MPS data more reliable and ultimately these novel tools economically more sustainable. The long-term roadmap from the 2015 workshop has been critically reviewed and updated. Recommendations for the next period and an outlook conclude the report.

Gut microbiota play a central role in human health, notably through the production of metabolites, including short-chain fatty acids, secondary bile acids, vitamins or neurotransmitters. Beyond contributing to gut health, these microbial metabolites significantly impact multiple organ systems by activating key signaling pathways along the gut-organ axes, including the gut-liver, gut-brain, and gut-bone axes. Chemicals ingested through food such as food additives, extensively used to enhance the texture, preservation and appearance of foods, may interact with our gut microbiota, altering metabolite production, and this can have consequences for our health. However, gut microbial metabolism is currently overlooked in toxicology. While efforts are underway to develop standardized human-based new approach methodologies to assess compound-microbiome interactions, anchoring those assays within the adverse outcome pathway (AOP) framework would offer a structured way to connect changes in gut microbial metabolism to adverse health outcomes. Using human-based models enhances the relevance of the results while supporting the reduction of animal-based testing in toxicology research.

On occasion of the DNT5 meeting in Konstanz, Germany (April-2024), participants brainstormed on future challenges concerning a regulatory implementation of the developmental neurotoxicity (DNT) in vitro test battery (DNT-IVB). The five discussion topics below outline some of the key issues, opportunities and research directions for the next several years: (1) How to contextualize DNT hazard with information on potential maternal toxicity or other toxicity domains (non-DNT)? Several approaches on how to use cytotoxicity data from NAMs were discussed. (2) What opportunities exist for an immediate or near-future application of the DNT-IVB, e.g. as a prioritisation step or add-on to other information? Initial examples are already emerging; the data can be used even if the battery is not converted to a defined approach. (3) How to establish data interpretation procedures for multi-dimensional endpoints that reduce dimensionality and are suitable for classification? A decision framework is required on how to use the DNT-IVB in a regulatory context. Machine-learning (AI-approaches) may provide novel classification models. (4) How can a battery of molecular initiating events (MIEs) be smartly linked to the DNT-IVB? At what tier of an overall strategy would MIEs be evaluated, and how would one optimally balance cost vs information yield. (5) What is the way forward to scientific validation of DNT NAMs and the DNT-IVB? A large set of animal data would be required for conventional approaches, while mechanistic information may establish relevance in other ways

The integration of artificial intelligence (AI) into new approach methods (NAMs) for toxicology rep-resents a paradigm shift in chemical safety assessment. Harnessing AI appropriately has enormous potential to streamline validation efforts. This review explores the challenges, opportunities, and future directions for validating AI-based NAMs, highlighting their transformative potential while acknowledging the complexities involved in their implementation and acceptance. We discuss key hurdles such as data quality, model interpretability, and regulatory acceptance, alongside opportunities including enhanced predictive power and efficient data integration. The concept of e-validation, an AI-powered framework for streamlining NAM validation, is presented as a comprehensive strategy to overcome limitations of traditional validation approaches, leveraging AI-powered modules for reference chemical selection, study simulation, mechanistic validation, and model training and evaluation. We propose robust validation strategies, including tiered approaches, performance benchmarking, uncertainty quantification, and cross-validation across diverse datasets. The importance of ongoing monitoring and refinement post-implementation is emphasized, addressing the dynamic nature of AI models. We consider ethical implications and the need for human oversight in AI-driven toxicology and outline the impact of trends in AI devel-opment, research priorities, and a vision for the integration of AI-based NAMs in toxicological practice, calling for collaboration among researchers, regulators, and industry stakeholders. We describe the vision of companion AI post-validation agents to keep methods and their validity status current. By addressing these challenges and opportunities, the scientific community can harness the potential of AI to enhance predictive toxicology while reducing reliance on traditional animal testing and increasing human relevance and translational capabilities.

This corrects the article DOI: 10.14573/altex.2403151.

The workshop titled State of the Science on Assessing Developmental Neurotoxicity Using New Approach Methods was co-organized by University of Maryland’s Joint Institute for Food Safety and Applied Nutrition (JIFSAN) and the U.S. Food and Drug Administration’s (FDA) Center for Food Safety and Applied Nutrition (CFSAN; now called the Human Foods Program), and was hosted by FDA in College Park, MD on November 14-15, 2023. This event convened experts from inter­national organizations, governmental agencies, industry, and academia to explore the transition from traditional in vivo tests to innovative new approach methods (NAMs) in developmental neurotoxicity (DNT) testing. The discussions emphasized the heightened vulnerability of the developing human brain to toxic exposures and the potential of NAMs to provide more ethical, economical, and scientifically robust alternatives to traditional testing. Various NAMs for DNT were discussed, including in silico, in chemico, in vitro, non-mammalian whole organisms, and novel mammalian approaches. In addition to progress in the field, the workshop discussed ongoing chal­lenges such as expectations to perfectly replicate the complex biology of human neurodevelopment and integration of DNT NAMs into regulatory frameworks. Presentations and panel discussions pro­vided a comprehensive overview of the state of the science, assessed the capabilities and limitations of current DNT NAMs, and outlined critical next steps in advancing the field of DNT testing.