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Een fles olijfolie als plaats delict

Van Hall Larenstein start begin 2018 met een nieuwe minor genaamd 'Food forensics & toxicology', als onderdeel van de opleiding Biotechnologie, waar studenten geleerd wordt wat voedselfraude inhoudt en op welke wijze het kan worden opgespoord. Interview met Marije Strikwold, associate-lector 'Veiligheid in de voedselketen' bij Van Hall Larenstein in Leeuwarden.

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Application of Caenorhabditis elegans (nematode) and Danio rerio embryo (zebrafish) as model systems to screen for developmental and reproductive toxicity of Piperazine compounds

From the article: "To enable selection of novel chemicals for new processes, there is a recognized need for alternative toxicity screening assays to assess potential risks to man and the environment. For human health hazard assessment these screening assays need to be translational to humans, have high throughput capability, and from an animal welfare perspective be harmonized with the principles of the 3Rs (Reduction, Refinement, Replacement). In the area of toxicology a number of cell culture systems are available but while these have some predictive value, they are not ideally suited for the prediction of developmental and reproductive toxicology (DART). This is because they often lack biotransformation capacity, multicellular or multi- organ complexity, for example, the hypothalamus pituitary gonad (HPG) axis and the complete life cycle of whole organisms. To try to overcome some of these limitations in this study, we have used Caenorhabditis elegans (nematode) and Danio rerio embryos (zebrafish) as alternative assays for DART hazard assessment of some candidate chemicals being considered for a new commercial application. Nematodes exposed to Piperazine and one of the analogs tested showed a slight delay in development compared to untreated animals but only at high concentrations and with Piperazine as the most sensitive compound. Total brood size of the nematodes was also reduced primarily by Piperazine and one of the analogs. In zebrafish Piperazine and analogs showed developmental delays. Malformations and mortality in individual fish were also scored. Significant malformations were most sensitively identified with Piperazine, significant mortality was only observed in Piperazine and only at the higest dose. Thus, Piperazine seemed the most toxic compound for both nematodes and zebrafish. The results of the nematode and zebrafish studies were in alignment with data obtained from conventional mammalian toxicity studies indicating that these have potential as developmental toxicity screening systems. The results of these studies also provided reassurance that none of the Piperazines tested are likely to have any significant developmental and/or reproductive toxicity issues to humans when used in their commercial applications."

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New approach methodologies (NAMs) for human-relevant biokinetics predictions

For almost fifteen years, the availability and regulatory acceptance of new approach methodologies (NAMs) to assess the absorption, distribution, metabolism and excretion (ADME/biokinetics) in chemical risk evaluations are a bottleneck. To enhance the field, a team of 24 experts from science, industry, and regulatory bodies, including new generation toxicologists, met at the Lorentz Centre in Leiden, The Netherlands. A range of possibilities for the use of NAMs for biokinetics in risk evaluations were formulated (for example to define species differences and human variation or to perform quantitative in vitro-in vivo extrapolations). To increase the regulatory use and acceptance of NAMs for biokinetics for these ADME considerations within risk evaluations, the development of test guidelines (protocols) and of overarching guidance documents is considered a critical step. To this end, a need for an expert group on biokinetics within the Organisation of Economic Cooperation and Development (OECD) to supervise this process was formulated. The workshop discussions revealed that method development is still required, particularly to adequately capture transporter mediated processes as well as to obtain cell models that reflect the physiology and kinetic characteristics of relevant organs. Developments in the fields of stem cells, organoids and organ-on-a-chip models provide promising tools to meet these research needs in the future.

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New approach methodologies (NAMs) for human-relevant biokinetics predictions

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Towards a reporting guideline for developmental and reproductive toxicology testing in C. elegans and other nematodes

Implementation of reliable methodologies allowing Reduction, Refinement, and Replacement (3Rs) of animal testing is a process that takes several decades and is still not complete. Reliable methods are essential for regulatory hazard assessment of chemicals where differences in test protocol can influence the test outcomes and thus affect the confidence in the predictive value of the organisms used as an alternative for mammals. Although test guidelines are common for mammalian studies, they are scarce for non-vertebrate organisms that would allow for the 3Rs of animal testing. Here, we present a set of 30 reporting criteria as the basis for such a guideline for Developmental and Reproductive Toxicology (DART) testing in the nematode Caenorhabditis elegans. Small organisms like C. elegans are upcoming in new approach methodologies for hazard assessment; thus, reliable and robust test protocols are urgently needed. A literature assessment of the fulfilment of the reporting criteria demonstrates that although studies describe methodological details, essential information such as compound purity and lot/batch number or type of container is often not reported. The formulated set of reporting criteria for C. elegans testing can be used by (i) researchers to describe essential experimental details (ii) data scientists that aggregate information to assess data quality and include data in aggregated databases (iii) regulators to assess study data for inclusion in regulatory hazard assessment of chemicals.

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Editorial: Leveraging artificial intelligence and open science for toxicological risk assessment

Editorial on the Research Topic "Leveraging artificial intelligence and open science for toxicological risk assessment"

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The probable future of toxicology - probabilistic risk assessment

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.

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The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

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Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.

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Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

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Feasibility and evaluation of high-fidelity simulation education for acute clinical toxicology

To prepare medical students appropriately for the management of toxicological emergencies, we have developed a simulation-based medical education (SBME) training in acute clinical toxicology. Our aim is to report on the feasibility, evaluation and lessons learned of this training. Since 2019, each year approximately 180 fifth-year medical students are invited to participate in the SBME training. The training consists of an interactive lecture and two SBME stations. For each station, a team of students had to perform the primary assessment and management of an intoxicated patient. After the training, the students completed a questionnaire about their experiences and confidence in clinical toxicology. Overall, the vast majority of students agreed that the training provided a fun, interactive and stimulating way to teach about clinical toxicology. Additionally, they felt more confident regarding their skills in this area. Our pilot study shows that SBME training was well-evaluated and feasible over a longer period.

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Een fles olijfolie als plaats delict

Application of Caenorhabditis elegans (nematode) and Danio rerio embryo (zebrafish) as model systems to screen for developmental and reproductive toxicity of Piperazine compounds

New approach methodologies (NAMs) for human-relevant biokinetics predictions

Towards a reporting guideline for developmental and reproductive toxicology testing in C. elegans and other nematodes

Editorial: Leveraging artificial intelligence and open science for toxicological risk assessment

The probable future of toxicology - probabilistic risk assessment

The application of natural language processing for the extraction of mechanistic information in toxicology

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Feasibility and evaluation of high-fidelity simulation education for acute clinical toxicology

Projects 2

Op weg naar dierproefvrij testen met TOX FLOW

Op weg naar dierproefvrij testen met TOX FLOW

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Products 74

Een fles olijfolie als plaats delict

Application of Caenorhabditis elegans (nematode) and Danio rerio embryo (zebrafish) as model systems to screen for developmental and reproductive toxicity of Piperazine compounds

New approach methodologies (NAMs) for human-relevant biokinetics predictions

Towards a reporting guideline for developmental and reproductive toxicology testing in C. elegans and other nematodes

Editorial: Leveraging artificial intelligence and open science for toxicological risk assessment

The probable future of toxicology - probabilistic risk assessment

The application of natural language processing for the extraction of mechanistic information in toxicology

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Feasibility and evaluation of high-fidelity simulation education for acute clinical toxicology

Projects 2

Op weg naar dierproefvrij testen met TOX FLOW

Op weg naar dierproefvrij testen met TOX FLOW