Altex-Alternatives To Animal Experimentation最新文献

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.

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

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.

Since its discovery as an innate bacterial immune system, the clustered regularly interspaced short palindromic repeats (CRISPR) associated nuclease 9 (CRISPR-Cas9) system has quickly landed on mammalian genomes to become the first-in-class editing technique. CRISPR-Cas9 offered an inval­uable approach to correct pathogenic mutations, thus becoming a promising cure for diseases with highly unmet medical needs. To date, several attempts have been made to understand, categorize and predict the outcome of genetic manipulation with different degrees of success. The lack of an appropriate and translatable model to test CRISPR/Cas9 effects, both wanted and unwanted, has limited its applications to advance gene therapies. Herein we describe the potential of microphysi­ological systems (MPS) as an alternative to the classical models used in CRISPR safety studies, such as immortalized cell lines or small mammals (e.g., rodents), to facilitate the progress of new CRISPR medicines to the clinics.

The 5th International Conference on Developmental Neurotoxicity (DNT) Testing (DNT5) took place in April 2024 in Konstanz, Germany, organized by CAAT-Europe, the University of Konstanz, and scientists from the US EPA, SCAHT, and CAAT at Johns Hopkins University Bloomberg School of Public Health. The conference convened experts from regulatory agencies, industry, and academia to explore the latest advancements in DNT testing and the integration of animal-free new approach methodologies (NAMs) into next-generation risk assessment (NGRA). The key topic was the appli-cation and further development of the recently established DNT in vitro test battery (DNT-IVB). To support this, OECD held a satellite meeting to discuss necessary next steps for further implementation of the DNT-IVB in regulatory contexts. Validation of new DNT test methods and use of their data for in-vitro-to-in-vivo extrapolations in physiologically based kinetic models were also important themes of the main meeting. In this context, the question was raised when a comprehensive biological and chemical coverage by the DNT-IVB would be reached. A need for additional testing data was recognized. Context-specific validation approaches for the entire DNT-IVB and the potential for intelligent combinations of assays to enhance the predictive power of the test battery were also addressed. Many presentations demonstrated the field's embrace of novel developments, including the use of multi-endpoint embryonic zebrafish tests, the development of artificial intelligence-driven computational approaches, and the establishment of complex, electrically active brain organoids and other self-organizing structures. Through its highly interactive format, DNT5 promoted extensive collaborative efforts in advancing the field toward more human-relevant, scientifically reliable, and ethical toxicological assessments.

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.

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 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 previously reported to be influenced by hepatotoxicants. The toxicity separation index (TSI) was applied to quantify how well specific summary variables of gene expression can differen­tiate between the set of hepatotoxic and non-hepatotoxic substances. The best TSI was obtained when the lowest concentration of a test compound that led to differential expression of two genes when compared to vehicle controls was considered positive. 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.

Chemical substances and mixtures are classified based on their toxicological hazard. The regulatory validation process for in vitro methods for hazard assessment assesses their relevance by comparing them to standard in vivo test data. This requires transforming continuous read-out data into ordinal data (hazard classes). Existing strategies for developing new methods often overlook the constraints associated with small datasets, omitting the use of contemporary statistical techniques such as uncer­tainty quantification and bootstrapping. To overcome these limitations, we apply bootstrapping, estimates for the out-of-sample error, and uncertainty quantification to the validation dataset for eye irritation of Kaluzhny et al. (2011) and to a dataset of plant protection products (PPPs) pub­lished by Kolle et al. (2015), which were tested for eye irritation in vitro (OECD TG 492) and in vivo (OECD TG 405). Assessment criteria for sensitivity, specificity, and accuracy are proposed, considering uncertainty quantification and estimation of the out-of-sample error. The cut-off value for PPPs based on the available set of in vitro-in vivo data pairs can be improved by using modern cut-off approaches. For PPPs, the OECD recommended cut-off of 60% mean tissue viability based on single substances leads to lower sensitivity than the newly derived cut-off value of 67%. For liquid single substances, the OECD-recommended cut-off is confirmed. This case study demonstrates that modern statistical methods for small datasets improve the reliability of in vitro cut-off values and should therefore be used to revise and derive cut-off values for hazard classification in future.