Altex-Alternatives To Animal Experimentation最新文献

Since Russell and Burch introduced and defined the 3Rs, i.e., the replacement, reduction, and refinement of animal use in research, in 1959, different definitions have emerged and been implemented in guidelines and policies. Switzerland is known for having some of the most restrictive legislation regarding the use of animals, in which the 3Rs are also defined and implemented. To our knowledge, the purpose and definitions of the 3Rs used in the Swiss Animal Welfare Act, Animal Protection Ordinance, and Animal Experimentation Ordinance have never been compared with Russell and Burch’s original purpose and definitions. In this paper we make this comparison with two aims: to reveal ethically relevant departures from the original purpose and definitions, and to provide an ethical evaluation of the current Swiss law regarding the 3Rs. In doing so, we first expose the similarity of purposes. We then identify one risky departure from the original definition of replacement in Swiss law, which shows a problematic focus on species. Finally, we address Swiss law’s failure to apply the 3Rs in the most effective way. With respect to this last point, we discuss the need for 3R conflict resolution, the timing of application of the 3Rs, problematic prioritizations and choices of convenience as well as a solution to apply the 3Rs more effectively using Russell and Burch’s concept of total sum of distress.

Proper brain development is based on the orchestration of key neurodevelopmental processes (KNDP), including the for­mation and function of neural networks. If at least one KNDP is affected by a chemical, an adverse outcome is expected. To enable a higher testing throughput than the guideline animal experiments, a developmental neurotoxicity (DNT) in vitro testing battery (DNT IVB) comprising a variety of assays that model several KNDPs was set up. Gap analysis revealed the need for a human-based assay to assess neural network formation and function (NNF). Therefore, we established the human NNF (hNNF) assay. A co-culture comprised of human induced pluripotent stem cell (hiPSC)-derived excitatory and inhibitory neurons as well as primary human astroglia was differentiated for 35 days on microelectrode arrays (MEA), and spontaneous electrical activity, together with cytotoxicity, was assessed on a weekly basis after washout of the compounds 24 h prior to measurements. In addition to the characterization of the test system, the assay was challenged with 28 com­pounds, mainly pesticides, identifying their DNT potential by evaluating specific spike-, burst-, and network parameters. This approach confirmed the suitability of the assay for screening environmental chemicals. Comparison of benchmark con­centrations (BMC) with an NNF in vitro assay (rNNF) based on primary rat cortical cells revealed differences in sensitivity. Together with the successful implementation of hNNF data into a postulated stressor-specific adverse outcome pathway (AOP) network associated with a plausible molecular initiating event for deltamethrin, this study suggests the hNNF assay as a useful complement to the DNT IVB.

Endocrine disruption by environmental chemicals continues to be a concern for human safety. The rat, a widely used model organism in toxicology, is very sensitive to chemical-induced thyroid perturbation, e.g., histopathological alterations in thyroid tissue. Species differences in the susceptibility to thyroid perturbation lead to uncertainty in human safety risk assessments. Hazard identification and characterization of chemically induced thyroid perturbation would therefore benefit from in vitro models addressing different mechanisms of action in a single functional assay, ideally across species. We here introduce a rat thyroid-liver chip that enables simultaneous identification of direct and indirect (liver-mediated) thyroid perturbation on organ-level functions in vitro. A second manuscript describes our work toward a human thyroid-liver chip (Kühnlenz et al., 2022). The presented microfluidic model consisting of primary rat thyroid follicles and liver 3D spheroids maintains a tissue-specific phenotype for up to 21 days. More precisely, the thyroid model exhibits a follicular architecture expressing basolateral and apical markers and secretes T4. Likewise, liver spheroids retain hepatocellular characteristics, e.g., a stable release of albumin and urea, the presence of bile canalicular networks, and the formation of T4-glucuronide. Experiments with reference chemicals demonstrated proficiency to detect direct and indirect mechanisms of thyroid perturbation through decreased thyroid hormone secretion and increased gT4 formation, respectively. Prospectively this rat thyroid-liver chip model, together with its human counterpart, may support a species-specific quantitative in vitro to in vivo extrapolation to improve a data-driven and evidence-based human safety risk assessment with significant contributions to the 3R principles.

Thyroid hormones (THs) are crucial regulators of human metabolism and early development. During the safety assessment of plant protection products, the human relevance of chemically induced TH perturbations observed in test animals remains uncertain. European regulatory authorities request follow-up in vitro studies to elucidate human-relevant interferences on thyroid gland function or TH catabolism through hepatic enzyme induction. However, human in vitro assays based on single molecular initiating events poorly reflect the complex TH biology and related liver-thyroid axis. To address this complexity, we present human three-dimensional thyroid and liver organoids with key functions of TH metabolism. The thyroid model resembles in vivo-like follicular architecture and a TSH-dependent triiodothyronine synthesis over 21 days, which is inhibited by methimazole. The HepaRG-based liver model, secreting the critical TH-binding proteins albumin and thyroxine-binding globulin, emulates an active TH catabolism via the formation of glucuronidated and sulfated thyroxine (gT4/sT4). Activation of the nuclear receptors PXR and AHR was demonstrated via the induction of specific CYP isoenzymes by rifampicin, pregnenolone-16α-carbonitrile, and β-naphthoflavone. However, this nuclear receptor activation, assumed to regulate UDP-glucuronosyltransferases and sulfotransferases, appeared to have no effect on gT4 and sT4 formation in this human-derived hepatic cell line model. Finally, established single-tissue models were successfully co-cultured in a perfused two-organ chip for 21 days. In conclusion, this model presents a first step towards a complex multimodular human platform that will help to identify both direct and indirect thyroid disruptors that are relevant from a human safety perspective.

Four decades of the Human Genome Project and its consequences have shown how the entrepreneurial state, through significant investment into science, can drive scientific progress and advance biomedicine. A certain fraction of diseases can now be explained as caused by genetics, and a more significant fraction as impacted by genetics. Besides another fraction caused by pathogens, the third and probably largest impactor is exposure, i.e., the many physicochemical and lifestyle factors. This article makes the case that it is time to start a Human Exposome Project, which systematically explores and catalogs the exposure side of human health and disease. The envisioned Human Exposome Project needs to be more than a scaled exposomics approach, aiming to assess the totality of relevant exposures through ~omics of human body fluids and forming exposure hypotheses. Exposomics is increasingly complemented by exposure science and biomonitoring to measure exposure, mechanistic understanding, human-relevant microphysiological systems, big data, and artificial intelligence (AI) to mine these data and integrate pieces of evidence. The potential impact of AI on a possible Human Exposome Project is so substantial that we should speak of exposome intelligence (EI) because this allows us to expand our limited current knowledge to the big unknown unknowns of threats to human health.

Transcriptomics-based biomarkers are promising new approach methodologies (NAMs) to identify molecular events underlying the genotoxic mode of action of chemicals. Previously, we developed the GENOMARK biomarker, consisting of 84 genes selected based on whole genomics DNA microarray profiles of 24 (non-)genotoxic reference chemicals covering different modes of action in metabolically competent human HepaRG™ cells. In the present study, new prediction models for genotoxicity were developed based on an extended reference dataset of 38 chemicals including existing as well as newly generated gene expression data. Both unsupervised and supervised machine learning algorithms were used, but as unsupervised machine learning did not clearly distinguish between groups, the performance of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), was evaluated. More specifically, the predictive accuracy was compared, the sensitivity to outliers for one or more biomarker genes was assessed, and the prediction performance for 10 misleading positive chemicals exposed at their IC10 concentration was determined. In addition, the applicability of both prediction models on a publicly available gene expression dataset, generated with RNA-sequencing, was investigated. Overall, the RF and SVM models were complementary in their classification of chemicals for genotoxicity. To facilitate data analysis, an online application was developed, combining the outcomes of both prediction models. This research demonstrates that the combination of gene expression data with supervised machine learning algorithms can contribute to the ongoing paradigm shift towards a more human-relevant in vitro genotoxicity testing strategy without the use of experimental animals.

Manufacturing and functionalizing materials at the nanoscale has led to the generation of a whole array of nanoforms (NFs) of substances varying in size, morphology, and surface characteristics. Due to financial, time, and ethical considerations, testing every unique NF for adverse effects is virtually impossible. Use of hypothesis-driven grouping and read-across approaches, as supported by the GRACIOUS Framework, represents a promising alternative to case-by-case testing that will make the risk assessment process more efficient. Through application of appropriate grouping hypotheses, the Framework facilitates the assessment of similarity between NFs, thereby supporting grouping and read-across of information, minimizing the need for new testing, and aligning with the 3R principles of replacement, reduction, and refinement of animals in toxicology studies. For each grouping hypothesis an integrated approach to testing and assessment (IATA) guides the user in data gathering and acquisition to test the hypothesis, following a structured format to facilitate efficient decision-making. Here we present the template used to generate the GRACIOUS grouping hypotheses encompassing information relevant to “Lifecycle, environmental release, and human exposure”, “What they are: physicochemical characteristics”, “Where they go: environmental fate, uptake, and toxicokinetics”, and “What they do: human and environmental toxicity”. A summary of the template-derived hypotheses focusing on human health is provided, along with an overview of the IATAs generated by the GRACIOUS project. We discuss the application and flexibility of the template, providing the opportunity to expand the application of grouping and read-across in a logical, evidence-based manner to a wider range of NFs and substances.