Altex-Alternatives To Animal Experimentation最新文献

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are the major components of long-chain per- and polyfluorinated alkyl substances (PFAS), known for their chemical stability and environmental persistence. Even if PFOA and PFOS have been phased out or are limited in use, they still represent a concern for human and environmental health. Several studies have been per­formed to highlight the toxicological behavior of these chemicals and their mode of action (MoA). Data have suggested a causal association between PFOA or PFOS exposure and carcinogenicity in humans, but the outcomes of epidemiological studies showed some inconsistency. Moreover, the hypothesized MoA based on animal studies is considered not relevant for human cancer. To improve the knowledge on PFAS toxicology and contribute to the weight of evidence for the regu­latory classification of PFAS, we used the BALB/c 3T3 cell transformation assay (CTA), an in vitro model under consideration to be included in an integrated approach to testing and assessment for non-genotoxic carcinogens (NGTxCs). PFOS and PFOA were tested at several concentrations using a validated experimental protocol. Our results demonstrate that PFOA does not induce cell transformation, whereas PFOS exposure induced a concentration-related increase of type III foci. Malignant foci formation was triggered at PFOS concentrations equal to or higher than 50 ppm and was not directly associated with cytotoxicity or proliferation induction. The divergent CTA outcomes suggest that different molecular events could be responsible for the toxicological profiles of PFOS and PFOA, which were not fully captured in our study.

Microphysiological systems (MPS) are gaining broader application in the pharmaceutical industry but have primarily been leveraged in early discovery toxicology and pharmacology studies with small molecules. The adoption of MPS offers a promising avenue to reduce animal use, improve in-vitro-to-in-vivo translation of pharmacokinetics/pharmacodynamics and toxicity correlation, and provide mechanistic understanding of model species suitability. While MPS have demonstrated utility in these areas with small molecules and biologics, MPS models in cell therapy development have not been fully explored, let alone validated. Distinguishing features of MPS, including long-term viability and physiologically relevant expression of functional enzymes, receptors, and pharmacological targets make them attractive tools for nonclinical characterization. However, there is currently limited published evidence of MPS being utilized to study the disposition, metabolism, pharmacology, and toxicity profiles of cell therapies. This review provides an industry perspective on the nonclinical application of MPS on cell therapies, first with a focus on oncology applications followed by examples in regenerative medicine.

Effective repair of spinal cord injury sites remains a major clinical challenge. One promising strategy is the implantation of multifunctional bioscaffolds to enhance nerve fiber growth, guide regener­ating tissue, and modulate scarring/inflammation processes. Given their multifunctional nature, such implants require testing in models which replicate the complex neuropathological responses of spinal injury sites. This is often achieved using live, adult animal models of spinal injury. However, these have substantial drawbacks for developmental testing, including the requirement for large numbers of animals, costly infrastructure, high levels of expertise, and complex ethical processes. As an alternative, we show that organotypic spinal cord slices can be derived from the E14 chick embryo and cultured with high viability for at least 24 days, with major neural cell types detected. A transecting injury could be reproducibly introduced into the slices and characteristic neuro­pathological responses similar to those in adult spinal cord injury observed at the lesion margin. This included aligned astrocyte morphologies and upregulation of glial fibrillary acidic protein in astrocytes, microglial infiltration into the injury cavity, and limited nerve fiber outgrowth. Bioimplan­tation of a clinical grade scaffold biomaterial was able to modulate these responses, disrupting the astrocyte barrier, enhancing nerve fiber growth, and supporting immune cell invasion. Chick embryos are inexpensive and simple, requiring facile methods to generate the neurotrauma model. Our data show the chick embryo spinal cord slice system could be a replacement spinal injury model for laboratories developing new tissue engineering solutions.

This article explores the potential of principles established in translational medicine for the use of bio-markers to advance the validation of alternatives to animal testing in preclinical safety assessment. It examines especially how such principles can enhance the predictive power, mechanistic under-standing, and human relevance of new approach methodologies (NAMs). Key concepts from translational medicine, such as fit-for-purpose validation, evidence-based approaches, and inte-grated testing strategies, are already being applied to the development and validation of NAMs. The article discusses challenges in implementing biomarker-based approaches, including standardi-zation, demonstration of relevance, regulatory acceptance, and addressing biological complexity. It also highlights opportunities for advancement through collaborative efforts, technological inno-vations, and regulatory evolution. Case studies demonstrate successful applications of biomarkers in preclinical safety, while future perspectives explore emerging trends like multi-omics integration, microphysiological systems, and artificial intelligence. The article emphasizes the potential of bio-markers and translational science approaches in creating more predictive, efficient, and ethical preclinical safety assessment paradigms in the use of NAMs. Use of biomarkers can enable the mechanistic validation of human-relevant models and provide a means to relate changes in NAMs to animal or clinical study results. By leveraging these tools, the field can work towards reducing reliance on animal testing while improving the accuracy and human relevance of safety predictions.

Animal models have historically been poor preclinical predictors of gastrointestinal (GI) directed therapeutic efficacy and drug-induced GI toxicity. Human stem and primary cell-derived culture systems are a major focus of efforts to create biologically relevant models that enhance preclinical predictive value of intestinal efficacy and toxicity. The inherent variability in stem cell-based cultures makes development of useful models a challenge; the stochastic nature of stem cell differentiation interferes with the ability to build and validate reproducible assays that query drug responses and pharmacokinetics. In this study, we aimed to characterize and reduce sources of variability in a complex stem cell-derived intestinal epithelium model, termed RepliGut® Planar, across cells from multiple human donors, cell lots, and passage numbers. Assessment criteria included barrier for­mation and integrity, gene expression, and cytokine responses. Gene expression and culture metric analyses revealed that controlling cell passage number reduces variability and maximizes physi­ological relevance of the model. In a case study where passage number was optimized, distinct cytokine responses were observed among four human donors, indicating that biological variability can be detected in cell cultures originating from diverse human sources. These findings highlight key considerations for designing assays that can be applied to additional primary cell-derived systems, as well as establish utility of the RepliGut® Planar platform for robust development of human-pre­dictive drug-response assays.

Many laboratory procedures generate data on properties of chemicals, but they cannot be equated with toxicological "test methods". This apparent discrepancy is not limited to in vitro testing, using animal-free new approach methods (NAM), but also applies to animal-based testing approaches. Here, we give a brief overview of the differences between data generation and the setup or use of a complete test method. While there is excellent literature available on this topic for specialists (GIVIMP guidance; ToxTemp overview), a brief overview and easily-accessible entry point may be useful for a broader community. We provide a single figure to summarize all test method elements and processes required in the development (setup and adaptation) of a test method. The exposure scheme, the endpoint, and the test system are briefly outlined as fundamental elements of any test method. A rationale is provided, why they are not sufficient. We then explain the importance and role of purpose definition (including some information on what is modelled) and the prediction model, aka data interpretation procedure, which depends on the purpose definition, as further essential elements. This connection exemplifies that all fundamental elements are interdependent, and none can be omitted. Finally, discussion is provided on validation as a measure to provide confidence in the reliability, performance, and relevance of a test method. In this sense, validation may be considered a sixth fundamental element for practical use of test methods.

Currently there are two OECD-adopted defined approaches (DA) for eye hazard identification of non-surfactant liquids (OECD TG 467). The current study aimed to develop a DA for eye hazard identification of solid chemicals according to the three UN GHS categories (Cat.1, Cat. 2, No Cat.): the DAS. The DAS combines two test methods described in OECD TG 437 and TG 492. The DAS was developed based on in-depth statistical analysis of a database on solids containing in vitro and historically curated in vivo Draize eye test data. The performance of the DAS was assessed by comparing the predictions with the classification based on in vivo Draize eye test data, on the one hand, and with the performance criteria established by the OECD expert group, on the other hand. In a first tier of the DAS, the SkinEthic™ HCE EIT method (TG 492) is used to distinguish No Cat. from classified substances. For classified substances, the BCOP LLBO method (TG 437) is used to identify Cat. 1, and the remaining solids are predicted Cat. 2. In summary, 77.4% Cat. 1 (N=31), 52.3% Cat. 2 (N=18), and 70.0% of No Cat. (N=60) solids were correctly identified compared to the classification based on the Draize eye test. The percentage of correct predictions met the minimum OECD performance values of 75% Cat. 1, 50% Cat. 2, and 70% No Cat., and the percentage of mispredictions was below the established maximum values. Therefore, inclusion of the DAS in OECD TG 467 has been achieved.