Valuing Testing: Valuing animal free testing in chemical safety assessments

Russell and Burch coined the 3Rs to guide the ethical use of animals in chemical safety testing. Now decades later, great steps have been taken in developing animal-free testing models. We can conclude that the transition to animal-free models for the safety assessment of chemicals is well underway. However, more needs to be done. The challenge is to accelerate the acceptance of data derived from animal-free models whilst maintaining confidence of society, regulators and scientists in chemical safety. There is growing societal concern surrounding the long-term and cumulative human health effects following chemical exposure. The role of the immune system in chemical toxicity is a known knowledge gap and well-known species differences between rodents and humans are strongly limiting the relevance of animal data to assess effects in humans upon chemical exposure on immune mediated toxicity. Multifactorial barriers prevent data derived from animal-free model to be accepted by all stakeholders involved in safety assessment. A consortium of cell biologists, toxicologists and interdisciplinary social scientists, together with relevant stakeholders have co-designed the “Valuing Testing; Valuing animal free testing in chemical safety” project. We will study stakeholder and institutional values and explore a common language of stakeholders on the acceptance of uncertainty of animal-free model-derived data. We will iteratively perform case studies. Firstly a case study on single cells models that reflect Key Events in the Adverse Outcome Pathway. Secondly a case study on organotypic precision cut liver slices that emulates complex liver functionality. These case studies will be evaluated in stakeholder workshops and by a citizens jury. The input will be used to steer the toxicological benchmarking studies of these models. This results in a stakeholder supported approach that incorporates underlying values and uncertainty from differently designed animal-free testing models aimed towards an increased acceptance of NAM-derived data in chemical safety assessment.