PBT screening, i.e. the determination of potential persistence (P), bioaccumulation (B) and toxicity (T), plays a central role in the environmental assessment of chemicals. Persistence is typically screened via standard microbial biodegradation tests. Bioaccumulation refers to the accumulation of chemicals in organisms and is usually assessed in fish exposed to the test chemical determining the bioconcentration factor (BCF). Toxicity is determined at three trophic levels, with fish toxicity as the highest trophic level assessed which involves exposure of fish to different test chemical concentrations. Thus, animal tests are classically needed for both B and T assessment. From an ethical standpoint, the need to move away from animal tests has long been recognised by industry, NGOs, scientists and consumers. Today, this is reflected in many regulatory frameworks, notably the REACH Regulation which promotes and supports the 3Rs principles (reduction, refinement, replacement).

In vitro assays to determine biotransformation rates in hepatocytes or liver S9 fractions from rainbow trout (OECD TG 319 A/B) [1, 2] have been validated to refine BCF predictions using in vitro-in vivo extrapolation (IVIVE) models [3, 4]. For T assessment, in vitro cytotoxicity in the fish gill cell line RTgill-W1 is determined as a surrogate for the LD50 [5] from an acute fish test. The RTgill-WI assay has also recently been adopted as a new OECD test guideline [6].

Here we summarize our findings indicating that these tests are highly predictive for fragrance ingredients, and illustrate with case studies of our latest new registered substances how we apply these tests in particular during development of new ingredients and also for chemical registration. Furthermore potential uncertainties in particular of the in vitro assay and IVIVE models applied for B assessment are discussed. A regression model correlating in vitro S9 biotransformation rates, log Kow and in vivo measured BCFs, which was developed based on our previously published data set [7], is evaluated as a potential additional prediction model.

[1] OECD, Test No. 319A, OECD Guideline for the Testing of Chemicals, OECD Publishing, 2018.
[2] OECD, Test No. 319B, OECD Guideline for the Testing of Chemicals, OECD Publishing, 2018.
[3] J. W. Nichols, D. B. Huggett, J. A. Arnot, P. N. Fitzsimmons, C. E. Cowan-Ellsberry, Environmental Toxicology and Chemistry. 2013, 32, 1611-1622.
[4] S. Krause and K. U. Goss. Chemical Research in Toxicology, 2018, 31, 1195-1202.
[5] M. Fischer et al. Toxicological Sciences 2019, 169, 353-364.
[6] OECD, Draft Test Guideline, OECD Guideline for the Testing of Chemicals, OECD Publishing, 2021.
[7] H. Laue, L. Hostettler, R. P. Badertscher, K. J. Jenner,  G. Sanders, J. A. Arnot, and  A. Natsch. Environmental Science and Technology, 2020, 54, 9483-9494.