Corvi, Javier, Diaz-Roussel, Nicolas, Fernadez, José-M, Ronzano, Francesco, Froehlicher, Mirjam, Centeno, Emilio, Accuosto, Pablo, Ibrahim, Celine, Asakura, Shoji, Bringezu, Frank, Kreuschwig, Annika, Nogami, Yoko, Rih, Jeon, Rodriguez-Esteban, Raul, Sajot, Nicolas, Wichard, Joerg, Michael-Wu, Heng Yi, Drew, Philip, Steger-Hartmann, Thomas, Valancia, Alfonso, Furlong, Laura and Capella-Gutierrez, Salvador (2025) PretoxtTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports. Journal of cheminformatics, 17 (15). pp. 1-23. ISSN s13321-024-00925-x

Abstract

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.

Item Type:	Article
Date Deposited:	10 Jun 2025 00:46
Last Modified:	10 Jun 2025 00:46
URI:	https://oak.novartis.com/id/eprint/56274