Altex-Alternatives To Animal Experimentation最新文献

Understanding brain function remains challenging as work with human and animal models is complicated by compensatory mechanisms, while in vitro models have been too simple until now. With the advent of human stem cells and the bioengineering of brain microphysiological systems (MPS), understanding how both cognition and long-term memory arise is now coming into reach. We suggest combining cutting-edge AI with MPS research to spearhead organoid intelligence (OI) as synthetic biological intelligence. The vision is to realize cognitive functions in brain MPS and scale them to achieve relevant short- and long-term memory capabilities and basic information processing as the ultimate functional experimental models for neurodevelopment and neurological function and as cell-based assays for drug and chemical testing. By advancing the frontiers of biological computing, we aim to (a) create models of intelligence-in-a-dish to study the basis of human cognitive functions, (b) provide models to advance the search for toxicants contributing to neurological diseases and identify remedies for neurological maladies, and (c) achieve relevant biological computational capacities to complement traditional computing. Increased understanding of brain functionality, in some respects still superior to today's supercomputers, may allow to imitate this in neuromorphic computer architectures or might even open up biological computing to complement silicon computers. At the same time, this raises ethical questions such as where sentience and consciousness start and what the relationship between a stem cell donor and the respective OI system is. Such ethical discussions will be critical for the socially acceptable advance of brain organoid models of cognition.

Many interventions that show promising results in preclinical development do not pass clinical tests. Part of this may be explained by poor animal-to-human translation. Using animal models with low predictability for humans is neither ethical nor efficient. If translational success shows variation between medical research fields, analyses of common practices in these fields could identify factors contributing to successful translation. We assessed translational success rates in medical research fields using two approaches: through literature and clinical trial registers.Literature: We comprehensively searched PubMed for pharmacology, neuroscience, cancer research, animal models, clinical trials, and translation. After screening, 117 review papers were included in this scoping review. Translational success rates were not different within pharmacology (72%), neuroscience (62%), and cancer research (69%).Clinical trials: The fraction of phase-2 clinical trials with a positive outcome was used as a proxy (i.e., an indirect resemblance measure) for translational success. Trials were retrieved from the WHO trial register and categorized into medical research fields following the international classification of disease (ICD-10). Of the phase-2 trials analyzed, 65.2% were successful. Fields with the highest success rates were disorders of lipoprotein metabolism (86.0%) and epilepsy (85.0%). Fields with the lowest success rates were schizophrenia (45.4%) and pancreatic cancer (46.0%).Our combined analyses suggest relevant differences in success rates between medical research fields. Based on the clinical trials, comparisons of practice, e.g., between epilepsy and schizophrenia, might identify factors that influence translational success.

Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to its predictive nature, alignment with quantitative risk assessment, and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and BN are suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches could identify the most influencing linear AOP in a complex network and evaluate the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The advantages and limitations of the two approaches for qAOPN construction are discussed in detail, and suggestions on optimal workflows of PSEM and BN are provided to guide future qAOPN development.

Progress in developing new tools, assays, and approaches to assess human hazard and health risk provides an opportunity to re-evaluate the necessity of dog studies for the safety evaluation of agrochemicals. A workshop was held where partic­ipants discussed the strengths and limitations of past use of dogs for pesticide evaluations and registrations. Opportunities were identified to support alternative approaches to answer human safety questions without performing the required 90-day dog study. Development of a decision tree for determining when the dog study might not be necessary to inform pesticide safety and risk assessment was proposed. Such a process will require global regulatory authority participation to lead to its acceptance. The identification of unique effects in dogs that are not identified in rodents will need further evaluation and determination of their relevance to humans. The establishment of in vitro and in silico approaches that can provide critical data on relative species sensitivity and human relevance will be an important tool to advance the decision process. Promising novel tools including in vitro comparative metabolism studies, in silico models, and high-throughput assays able to identify metabolites and mechanisms of action leading to development of adverse outcome pathways will need further development. To replace or eliminate the 90-day dog study, a collaborative, multidisciplinary, international effort that transcends organi­zations and regulatory agencies will be needed in order to develop guidance on when the study would not be necessary for human safety and risk assessment.

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

Hazard assessments of skin sensitizers are increasingly performed using new approach methodologies (NAMs), with several in chemico, in vitro, and most recently, also defined approaches accepted for regulatory use. However, keeping track of potential limitations of each method to define applicability domains remains a crucial component to ensure adequate predictivity and to facilitate the appropriate selection of method(s) for each hazard assessment task. The objective of this report is to share test results generated with the GARD™skin assay on chemicals that have traditionally been considered difficult to test in some of the conventional in vitro and in chemico OECD Test Guidelines for skin sensitization. Such compounds may include, for example, indirectly acting haptens, hydrophobic substances, and substances of unknown or variable composition, complex reaction products or biological substances (UVCBs). Based on the results of this study, the sensitivity for prediction of skin sensitizing hazard of indirectly acting haptens was 92.4% and 87.5% when compared with local lymph node assay (LLNA) (n = 25) and human data (n = 8), respectively. Similarly, the sensitivity for prediction of skin sensitizing hazard of hydrophobic substances was 85.1% and 100% when compared with LLNA (n = 24) and human data (n = 9), respectively. Lastly, a case study involving assessment of a set of hydrophobic UVCBs (n = 7) resulted in a sensitivity of 100% compared to available reference data. These data provide support for the inclusion of such chemistries in the GARD™skin applicability domain without an increased risk of false negative classifications.

Animal methods bias in scientific publishing is a newly defined type of publishing bias describing a preference for animal-based methods where they may not be necessary or where nonanimal-based methods may already be suitable, which impacts the likelihood or timeliness of a manuscript being accepted for publication. This article covers the output from a workshop between stakeholders in publishing, academia, industry, government, and non-governmental organizations. The intent of the workshop was to exchange perspectives on the prevalence, causes, and impact of animal methods bias in scientific publishing, as well as to explore mitigation strategies. Output from the workshop includes summaries of presentations, breakout group discussions, participant polling results, and a synthesis of recommendations for mitigation. Overall, participants felt that animal methods bias has a meaningful impact on scientific publishing, though more evidence is needed to demonstrate its prevalence. Significant consequences of this bias that were identified include the unnecessary use of animals in scientific procedures, the continued reliance on animals in research – even where suitable nonanimal methods exist, poor rates of clinical translation, delays in publication, and negative impacts on career trajectories in science. Workshop participants offered recommendations for journals, publishers, funders, governments, and other policy makers, as well as the scientific community at large, to reduce the prevalence and impacts of animal methods bias. The workshop resulted in the creation of working groups committed to addressing animal methods bias, and activities are ongoing.