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Existing methods for PBT assessment are overly complex and cumbersome, have produced incorrect conclusions, and rely heavily on animal-intensive testing. We explore how new-approach methodologies (NAMs) can overcome the limitations of current PBT assessment. We propose two innovative hazard indicators, termed cumulative toxicity equivalents (CTE) and persistent toxicity equivalents (PTE). Together they are intended to replace existing PBT indicators and can also accommodate the emerging concept of PMT (where M stands for mobility). The proposed \u201ctoxicity equivalents\u201d can be measured with high throughput in vitro bioassays. CTE refers to the toxic effects measured directly in any given sample, including single chemicals, substitution products, or mixtures. PTE is the equivalent measure of cumulative toxicity equivalents measured after simulated environmental degradation of the sample. With an appropriate panel of animal-free or alternative in vitro bioassays, CTE and PTE comprise key environmental and human health hazard indicators. CTE and PTE do not require analytical identification of transformation products and mixture components but instead prompt two key questions: is the chemical or mixture toxic, and is this toxicity persistent or can it be attenuated by environmental degradation? Taken together, the proposed hazard indicators CTE and PTE have the potential to integrate P, B\/M and T assessment into one high-throughput experimental workflow that sidesteps the need for analytical measurements and will support the Chemicals Strategy for Sustainability of the European Union.<\/jats:p>","DOI":"10.1007\/s00204-023-03485-5","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T15:03:16Z","timestamp":1679583796000},"page":"1267-1283","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modernizing persistence\u2013bioaccumulation\u2013toxicity (PBT) assessment with high throughput animal-free methods"],"prefix":"10.1007","volume":"97","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5304-706X","authenticated-orcid":false,"given":"Beate I.","family":"Escher","sequence":"first","affiliation":[]},{"given":"Rolf","family":"Altenburger","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Bl\u00fcher","sequence":"additional","affiliation":[]},{"given":"John K.","family":"Colbourne","sequence":"additional","affiliation":[]},{"given":"Ralf","family":"Ebinghaus","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Fantke","sequence":"additional","affiliation":[]},{"given":"Michaela","family":"Hein","sequence":"additional","affiliation":[]},{"given":"Wolfgang","family":"K\u00f6ck","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"K\u00fcmmerer","sequence":"additional","affiliation":[]},{"given":"Sina","family":"Leipold","sequence":"additional","affiliation":[]},{"given":"Xiaojing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Scheringer","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Scholz","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Schloter","sequence":"additional","affiliation":[]},{"given":"Pia-Johanna","family":"Schweizer","sequence":"additional","affiliation":[]},{"given":"Tamara","family":"Tal","sequence":"additional","affiliation":[]},{"given":"Igor","family":"Tetko","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"Traidl-Hoffmann","sequence":"additional","affiliation":[]},{"given":"Lukas Y.","family":"Wick","sequence":"additional","affiliation":[]},{"given":"Kathrin","family":"Fenner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"3485_CR1","doi-asserted-by":"publisher","author":"A Abdelaziz","year":"2016","unstructured":"Abdelaziz A, Spahn-Langguth H, Schramm KW, Tetko IV (2016) Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge. 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