Many laboratory procedures generate data on properties of chemicals, but they cannot be equated with toxicological "test methods". This apparent discrepancy is not limited to in vitro testing, using animal-free new approach methods (NAM), but also applies to animal-based testing approaches. Here, we give a brief overview of the differences between data generation and the setup or use of a complete test method. While there is excellent literature available on this topic for specialists (GIVIMP guidance; ToxTemp overview), a brief overview and easily-accessible entry point may be useful for a broader community. We provide a single figure to summarize all test method elements and processes required in the development (setup and adaptation) of a test method. The exposure scheme, the endpoint, and the test system are briefly outlined as fundamental elements of any test method. A rationale is provided, why they are not sufficient. We then explain the importance and role of purpose definition (including some information on what is modelled) and the prediction model, aka data interpretation procedure, which depends on the purpose definition, as further essential elements. This connection exemplifies that all fundamental elements are interdependent, and none can be omitted. Finally, discussion is provided on validation as a measure to provide confidence in the reliability, performance, and relevance of a test method. In this sense, validation may be considered a sixth fundamental element for practical use of test methods.
Keywords: BenchMarks series; GIVIMP; NAMs; in vitro methods; validation.
Many laboratory procedures generate data on chemicals, but they cannot be considered complete toxicological "test methods". Here, we give a brief explanation of the fundamental elements of a toxicological test method. We provide an illustration that gives a complete overview of the development of a test method for non-specialists. We introduce the six fundamental elements, i.e., the exposure scheme, the test endpoint, the test system, the purpose definition and the prediction model and describe how they work together. Finally, we discuss the concept of validation. An understanding of these concepts is important for good-quality scientific research and especially for the development and acceptance of alternatives to animal experiments.