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oai:recercat.cat:10230/720772025-12-05T15:39:34Zcom_2072_6col_2072_452952

PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports Corvi, Javier Ronzano, Francesco Centeno, Emilio Capella Gutiérrez, Salvador Jesús, 1985- Adverse effect Animal model Annotated corpora Compound Drug Natural language processing Preclinical Text mining Toxicology Over the last few decades the pharmaceutical industry has generated a vast corpus of knowledge on the safety and efficacy of drugs. Much of this information is contained in toxicology reports, which summarise the results of animal studies designed to analyse the effects of the tested compound, including unintended pharmacological and toxic effects, known as treatment-related findings. Despite the potential of this knowledge, the fact that most of this relevant information is only available as unstructured text with variable degrees of digitisation has hampered its systematic access, use and exploitation. Text mining technologies have the ability to automatically extract, analyse and aggregate such information, providing valuable new insights into the drug discovery and development process. In the context of the eTRANSAFE project, we present PretoxTM (Preclinical Toxicology Text Mining), the first system specifically designed to detect, extract, organise and visualise treatment-related findings from toxicology reports. The PretoxTM tool comprises three main components: PretoxTM Corpus, PretoxTM Pipeline and PretoxTM Web App. The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated by toxicology experts. This corpus was used to develop, train and validate the PretoxTM Pipeline, which extracts treatment-related findings from preclinical study reports. The extracted information is then presented for expert visualisation and validation in the PretoxTM Web App. Scientific Contribution While text mining solutions have been widely used in the clinical domain to identify adverse drug reactions from various sources, no similar systems exist for identifying adverse events in animal models during preclinical testing. PretoxTM fills this gap by efficiently extracting treatment-related findings from preclinical toxicology reports. This provides a valuable resource for toxicology research, enhancing the efficiency of safety evaluations, saving time, and leading to more effective decision-making in the drug development process. JC was partly supported by eTRANSAFE (received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365 and support from the European Union's Horizon 2020 research and innovation programme and EFPIA). 2025-12-01T17:31:53Z 2025-12-01T17:31:53Z 2025 2025-12-01T17:31:53Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Corvi J, Diaz-Roussel N, Fernandez JM, Ronzano F, Centeno E, Accuosto P, Ibrahim C, Asakura S, Bringezu F, Frohlicher M, Kreuchwig A, Nogami Y, Rih J, Rodriguez-Esteban R, Sajot N, Wichard J, Wu HYM, Drew P, Steger-Hartmann T, Valencia A, Furlong LI, Capella-Gutierrez S. PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports. J Cheminform. 2025 Feb 3;17(1):15. DOI: 10.1186/s13321-024-00925-x 1758-2946 http://hdl.handle.net/10230/72077 http://dx.doi.org/10.1186/s13321-024-00925-x http://hdl.handle.net/10230/72077 eng Journal of Cheminformatics. 2025;17(1):15 info:eu-repo/grantAgreement/EC/H2020/777365 © The Author(s) 2025. 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/. http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf application/pdf BioMed Central