Principles underlying the next generation risk assessment approach (NGRA)
Next Generation Risk Assessment (NGRA) is defined as an exposure-led, hypothesis-driven risk assessment approach that integrates in silico, in chemico and in vitro approaches. Developing an NGRA not relying on animal data is not only a challenge, but also an opportunity for all involved, to increase receptiveness and acceptance of non-animal data in safety decision making.
Traditionally, the toxicological hazards of chemical substances have been identified and assessed on the basis of animal studies. Animal welfare considerations, societal expectations, regulatory action and the desire by industry to bring safe products to the market without new animal testing, triggered the need for new methodologies and approaches to risk assessment.
In this context, under the auspices of the International Cooperation on Cosmetics Regulation (ICCR), a joint working group comprising scientists from regulatory authorities and industry convened to agree on and outline the principles for incorporating new approach methodologies (NAM) into an integrated strategy for risk assessment of cosmetics ingredients (or ‘Next Generation Risk Assessment’). A most interesting paper recently published by the group under the authorship of Dent et al. in the scientific journal Computational Toxicology, outlines those principles. The more interested reader is also referred to the ICCR report “Integrated Strategies for Safety Assessments of Cosmetic Ingredients – Part I”. Some of the key aspects discussed in the Dent paper are summarised below.
‘Next Generation Risk Assessment’ (NGRA) is defined as an exposure-led, hypothesis driven risk assessment approach that integrates in silico, in chemico and in vitro approaches. The overall goal of an ‘NGRA’ is that it is relevant to humans, exposure-led, hypothesis-driven and designed to prevent harm. It should be conducted by using a tiered and iterative approach, following an appropriate literature search and evaluation of the available data and using relevant methodologies and testing strategies to ensure confidence in the validity of the safety assessment. Sources of uncertainty should be well-characterised and discussed. Lastly, all data, assumptions, methodologies and software used should be clearly documented and available for independent review. Hence, each NGRA is customised which means that a prescriptive list of tests to assure the safety of a chemical is neither appropriate nor exists.
Obviously, the principles outlined are not specific to NGRA. They are fundamental to any ‘traditional’ risk assessment that is based on animal data. However, in the absence of explicit regulatory guidance on how to integrate novel type of data into risk assessment, these principles should be considered as a reminder of current best practices. NGRA is novel, likely to use new, customised experimental designs. Developing an NGRA not relying on animal data is not only a challenge, but also an opportunity for all involved, especially the regulatory community to increase receptiveness and acceptance of non-animal data in safety decision making.
