March-Vila, EricFerretti, GiacomoTerricabras, EmmaArdao, InésBrea, José ManuelVarela, María JoséArana, ÁlvaroRubiolo, Juan AndrésSanz, FerranLoza, María I.Sánchez, LauraAlonso, HéctorPastor Maeso, Manuel2023-03-102023-03-102023March-Vila E, Ferretti G, Terricabras E, Ardao I, Brea JM, Varela MJ, Arana Á, Rubiolo JA, Sanz F, Loza MI, Sánchez L, Alonso H, Pastor M. A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry. Arch Toxicol. 2023 Apr;97(4):1091-111. DOI: 10.1007/s00204-023-03459-70340-5761http://hdl.handle.net/10230/56138There is a widely recognized need to reduce human activity's impact on the environment. Many industries of the leather and textile sector (LTI), being aware of producing a significant amount of residues (Keßler et al. 2021; Liu et al. 2021), are adopting measures to reduce the impact of their processes on the environment, starting with a more comprehensive characterization of the chemical risk associated with the substances commonly used in LTI. The present work contributes to these efforts by compiling and toxicologically annotating the substances used in LTI, supporting a continuous learning strategy for characterizing their chemical safety. This strategy combines data collection from public sources, experimental methods and in silico predictions for characterizing four different endpoints: CMR, ED, PBT, and vPvB. We present the results of a prospective validation exercise in which we confirm that in silico methods can produce reasonably good hazard estimations and fill knowledge gaps in the LTI chemical space. The proposed protocol can speed the process and optimize the use of resources including the lives of experimental animals, contributing to identifying potentially harmful substances and their possible replacement by safer alternatives, thus reducing the environmental footprint and impact on human health.application/pdfeng© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s00204-023-03459-7Computational toxicologyIn silicoLeather and textile industryMachine learningQSARRead acrossinfo:eu-repo/semantics/openAccess