Most traditional toxicity testing methods involve treating laboratory animals with a test substance and observing adverse effects. This approach is expensive and time-consuming, and the use of animals raises ethical concerns and issues of interspecies differences. While it usually takes several methods in combination to adequately account for the multiple mechanisms associated with toxicity, using cell-based, biochemical, and/or computational methods to predict chemical toxicity could overcome some of the drawbacks of traditional testing.
Integrated approaches to testing and assessment (IATAs) provide a means for combining the data from different methods. IATAs:
An integrated testing strategy is a limited type of IATA that that relies on:
Input data may be derived from in vitro test methods or from computational approaches such as “read-across,” in which toxicity data from a known chemical is used to predict toxicity for another, similar chemical. The input data is run through the computational model or other evaluation protocol to generate a hazard prediction.
Please note that the terms “integrated testing strategy” and “integrated approach to testing and assessment” represent evolving concepts. NICEATM has adapted these definitions from a preliminary draft guidance document being prepared by the Organisation for Economic Co-operation and Development.
Integrated testing strategies have been created to identify potential skin sensitizers (substances with the potential to cause allergic contact dermatitis).
NICEATM and scientists with the U.S. Environmental Protection Agency (EPA) developed an integrated testing strategy that combines data from 18 high throughput screening assays with a mathematical model to identify chemicals with the potential to interact with the estrogen receptor. Use of this integrated testing strategy has been accepted by the EPA as an alternative to three assays currently used in its Endocrine Disruptor Screening Program Tier I battery.
Reference: Browne et al. 2015. Screening chemicals for estrogen receptor bioactivity using a computational model. Environ Sci Technol 49:8804-8814.