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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.

Developmental neurotoxicity (DNT) testing has seen enormous progress over the last two decades. Preceding even the publication of the animal-based OECD test guideline for DNT testing in 2007, a series of non-animal technology workshops and conferences that started in 2005 has shaped a community that has delivered a comprehensive battery of in vitro test methods (DNT IVB). Its data interpretation is now covered by a very recent OECD guidance (No. 377). Here, we overview the progress in the field, focusing on the evolution of testing strategies, the role of emerging technol­ogies, and the impact of OECD test guidelines on DNT testing. In particular, this is an example of the targeted development of an animal-free testing approach for one of the most complex hazards of chemicals to human health. These developments started literally from a blank slate, with no proposed alternative methods available. Over two decades, cutting-edge science enabled the design of a testing approach that spares animals and enables throughput to address this challenging hazard. While it is evident that the field needs guidance and regulation, the massive economic impact of decreased human cognitive capacity caused by chemical exposure should be prioritized more highly. Beyond this, the claim to fame of DNT in vitro testing is the enormous scientific progress it has brought for understanding the human brain, its development, and how it can be perturbed.

The causes of nigrostriatal cell death in idiopathic Parkinson’s disease are unknown, but exposure to toxic chemicals may play some role. We followed up here on suggestions that bacterial secondary metabolites might be selectively cytotoxic to dopaminergic neurons. Extracts from Streptomyces venezuelae were found to kill human dopaminergic neurons (LUHMES cells). Utilizing this model system as a bioassay, we identified a bacterial metabolite known as aerugine (C₁₀H₁₁NO₂S; 2-[4-(hydroxymethyl)-4,5-dihydro-1,3-thiazol-2-yl]phenol) and confirmed this finding by chemical re-synthesis. This 2-hydroxyphenyl-thiazoline compound was previously shown to be a product of a wide-spread biosynthetic cluster also found in the human microbiome and in several pathogens. Aerugine triggered half-maximal dopaminergic neurotoxicity at 3-4 µM. It was less toxic for other neurons (10-20 µM), and non-toxic (at <100 µM) for common human cell lines. Neurotoxicity was completely prevented by several iron chelators, by distinct anti-oxidants and by a caspase inhibitor. In the Caenorhabditis elegans model organism, general survival was not affected by aerugine concentrations up to 100 µM. When transgenic worms, expressing green fluorescent protein only in their dopamine neurons, were exposed to aerugine, specific neurodegeneration was observed. The toxicant also exerted functional dopaminergic toxicity in nematodes as determined by the “basal slowing response” assay. Thus, our research has unveiled a bacterial metabolite with a remarkably selective toxicity toward human dopaminergic neurons in vitro and for the dopaminergic nervous system of Caenorhabditis elegans in vivo. These findings suggest that microbe-derived environmental chemicals should be further investigated for their role in the pathogenesis of Parkinson's disease.

Every test procedure, scientific and non-scientific, has inherent uncertainties, even when performed according to a standard operating procedure (SOP). In addition, it is prone to errors, defects, and mistakes introduced by operators, laboratory equipment, or materials used. Adherence to an SOP and comprehensive validation of the test method cannot guarantee that each test run produces data within the acceptable range of variability and with the precision and accuracy determined during the method validation. We illustrate here (part I) why controlling the validity of each test run is an important element of experimental design. The definition and application of acceptance criteria (AC) for the validity of test runs is important for the setup and use of test methods, particularly for the use of new approach methods (NAM) in toxicity testing. AC can be used for decision rules on how to handle data, e.g., to accept the data for further use (AC fulfilled) or to reject the data (AC not fulfilled). The adherence to AC has important requirements and consequences that may seem surprising at first sight: (i) AC depend on a test method’s objectives, e.g., on the types/concentrations of chemicals tested, the regulatory context, the desired throughput; (ii) AC are applied and documented at each test run, while validation of a method (including the definition of AC) is only performed once; (iii) if AC are altered, then the set of data produced by a method can change. AC, if missing, are the blind spot of quality assurance: Test results may not be reliable and comparable. The establishment and uses of AC will be further detailed in part II of this series.