Novel quantitative models could accelerate the reduction of animal testing for chemical safety assessment
New research has found a better way to predict the effects of chemicals on biological systems that may, one day, reduce the need for animal testing
New research has found a better way to predict the effects of chemicals on biological systems that may, one day, reduce the need for animal testing.
In the organism, chemicals elicit their effects by interacting with one or more molecular targets. These interactions trigger a chain of biological events that may potentially result in adverse health effects. Scientists are trying to portray these cascades of effects in models known as adverse outcome pathways (AOP).
The vision for the future is that these models could help to predict the potential effects of thousands of chemicals without the need for animal testing; however, in their current form, the application of AOPs for decision making is still limited.
The new study, by scientists at Brunel University London and AstraZeneca, in collaboration with the Catalan Institute of Water Research, takes AOPs one step further by introducing quantitative aspects that are currently missing. This could have implications for both animal tests, but also for developing better in vitro tests as it includes a kinetic approach.
Researchers used the effects of an anti-inflammatory drug on fish to develop the first quantitative AOP network based on chemical blood concentrations. The study demonstrated that factors such as the ability of chemicals to enter and distribute in the organism, potency and simultaneous interaction with multiple targets play a critical role in determining the predictive power of AOPs.
Lead author Dr Luigi Margiotta-Casaluci, Research Fellow at Brunel University London’s Institute of Environment, Health and Societies, said: ‘AOPs have great potential to support scientists and regulators in evaluating how safe chemicals are without using animal testing to assess them one by one. However, their application within a regulatory context is currently limited by their qualitative nature. We hope that these advancements in the integration of quantitative aspects in the model can unlock the full potential of AOPs to predict chemical toxicity and support decision making for a safer use of chemicals.’
