The classical way of safety assessment relies heavily on the use of laboratory animals. However, humans may respond differently to toxic compounds. RIVM investigates the improvement of hazard assessment of developmental toxicants using computational modelling of the developing human neural tube.
Short video about RIVM's activities researching 3D computational models for toxicity testing
3D computational models for toxicity testing
Speaker: Prof. dr. Aldert Piersma, professor of Reproductive Toxicology
ALDERT PIERSMA: We are developing a computational model for human neural tube closure. With this model we think we can improve the prediction of human developmental toxicity.
VISUAL: 3D computational models for toxicity testing
VOICE OVER: In order to improve human chemical and pharmaceutical safety assessment we need a fresh look at the way we work. The classical way of safety assessment relies heavily on the use of laboratory animals, assuming that they were miniature human beings. However, humans may respond differently to toxic substances than animals.
ALDERT PIERSMA: We believe that toxicity testing should be improved to achieve a better risk assessment in humans. We can do that by combining human data with new in silico as well as in vitro tools to get a complete picture of the toxicity of chemicals.
VOICE OVER: RIVM is working on a new approach for human safety assessment based on ontologies. Ontologies describe the biology of human physiology and disease, starting at the level of genes and molecules.
ALDERT PIERSMA: Based on our knowledge of physiology and disease we can actually describe what happens when a compound affects the system all the way to the disease that emerges which is at the end. We can describe this process in so-called adverse outcome pathways, in which we describe all the different elements that lead from the initial event to the adverse outcome in the end. In order to build ontologies, we need extensive data mining. The data mining will help us delineate the AOPs, starting from the exposure situation to the disease state. And within the ontologies and within the AOPs we will then be able to define what are the critical points that we need to address in terms of a testing system. So we will design a battery of test systems that take care of all these different points and then with an in silico model we will translate the results of these in vitro models into a prediction of toxicity. Using AOPs, we will be able to understand how neural tube defects come about and how compounds affect the system.
VOICE OVER: One of the most prominent neural tube-related malformations in human embryos is spina bifida. Using the ontology approach RIVM is working on a three-dimensional computer model of neural tube closure in the developing embryo.
ALDERT PIERSMA: We have a detailed understanding of how the neural tube is formed in embryogenesis. This allows us to describe the ontology to extract the AOPs and to define what test systems we need to test the effects of compounds on the system. And this combined with in silico models will allow us to do a hazard and risk assessment of chemicals using this novel, human-based system.
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Ontologies as basis for biology
Toxicity testing should be improved to achieve a better risk assessment in humans. This can be done by combining human data with new in silico as well as in vitro tools to get a complete picture of the toxicity of chemicals. Within this context, RIVM is working on a new approach for human safety assessment based on ontologies. Ontologies describe the biology of human physiology and disease, starting at the level of genes and molecules.
Ontologies are built through data mining. Ontologies aid in delineating the Adverse Outcome Pathway (AOPs) and to define what are the critical points that need to be addressed in an in vitro testing system. As a next step, this battery of test systems should provide measurements of compound effects that can be integrated by an in silico model to translate them into a prediction of toxicity. By taking this approach, this prediction is firmly based on knowledge of human physiology and disease.
Neural tube defects in silico
RIVM currently applies the use of ontologies to spina bifida, one of the most prominent neural tube-related malformations in human embryos. The biological processes underlying neural tube formation are well-understood. This allows to describe its ontology to extract the AOPs, and to define what in vitro test systems are needed to test the effects of compounds on the system. The in vitro findings are fed into the computational model to predict effects on neural tube closure.
This combination enables hazard and risk assessment of chemicals using this novel, human-based system.
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