PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports
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 compou...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-024-00925-x |
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| author | Javier Corvi Nicolás Díaz-Roussel José M. Fernández Francesco Ronzano Emilio Centeno Pablo Accuosto Celine Ibrahim Shoji Asakura Frank Bringezu Mirjam Fröhlicher Annika Kreuchwig Yoko Nogami Jeong Rih Raul Rodriguez-Esteban Nicolas Sajot Joerg Wichard Heng-Yi Michael Wu Philip Drew Thomas Steger-Hartmann Alfonso Valencia Laura I. Furlong Salvador Capella-Gutierrez |
| author_facet | Javier Corvi Nicolás Díaz-Roussel José M. Fernández Francesco Ronzano Emilio Centeno Pablo Accuosto Celine Ibrahim Shoji Asakura Frank Bringezu Mirjam Fröhlicher Annika Kreuchwig Yoko Nogami Jeong Rih Raul Rodriguez-Esteban Nicolas Sajot Joerg Wichard Heng-Yi Michael Wu Philip Drew Thomas Steger-Hartmann Alfonso Valencia Laura I. Furlong Salvador Capella-Gutierrez |
| author_sort | Javier Corvi |
| collection | DOAJ |
| description | 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. 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. |
| format | Article |
| id | doaj-art-c0f1a6247ef343cab56757ffe7449629 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-c0f1a6247ef343cab56757ffe74496292025-02-09T12:52:17ZengBMCJournal of Cheminformatics1758-29462025-02-0117112310.1186/s13321-024-00925-xPretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reportsJavier Corvi0Nicolás Díaz-Roussel1José M. Fernández2Francesco Ronzano3Emilio Centeno4Pablo Accuosto5Celine Ibrahim6Shoji Asakura7Frank Bringezu8Mirjam Fröhlicher9Annika Kreuchwig10Yoko Nogami11Jeong Rih12Raul Rodriguez-Esteban13Nicolas Sajot14Joerg Wichard15Heng-Yi Michael Wu16Philip Drew17Thomas Steger-Hartmann18Alfonso Valencia19Laura I. Furlong20Salvador Capella-Gutierrez21Life Sciences Department, Barcelona Supercomputing Center (BSC)Life Sciences Department, Barcelona Supercomputing Center (BSC)Life Sciences Department, Barcelona Supercomputing Center (BSC)Hospital del Mar Medical Research Institute (IMIM)Hospital del Mar Medical Research Institute (IMIM)MedBioInformatics SolutionsBayer AG, In Vitro SafetyEisaiChemical and Preclinical Safety, Merck Healthcare KGaATranslational Medicine, Preclinical Safety, Novartis Biomedical ResearchBayer AG, In Vitro SafetyEisaiIpsen InnovationRoche Innovation Center BaselServierBayer AG, In Vitro SafetyGenentech Research and Early Development (gRED) Computational Sciences, Genentech, Inc.PDS ConsultantsBayer AG, In Vitro SafetyLife Sciences Department, Barcelona Supercomputing Center (BSC)MedBioInformatics SolutionsLife Sciences Department, Barcelona Supercomputing Center (BSC)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. 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.https://doi.org/10.1186/s13321-024-00925-xNatural language processingText miningToxicologyAdverse effectPreclinicalAnimal model |
| spellingShingle | Javier Corvi Nicolás Díaz-Roussel José M. Fernández Francesco Ronzano Emilio Centeno Pablo Accuosto Celine Ibrahim Shoji Asakura Frank Bringezu Mirjam Fröhlicher Annika Kreuchwig Yoko Nogami Jeong Rih Raul Rodriguez-Esteban Nicolas Sajot Joerg Wichard Heng-Yi Michael Wu Philip Drew Thomas Steger-Hartmann Alfonso Valencia Laura I. Furlong Salvador Capella-Gutierrez PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports Journal of Cheminformatics Natural language processing Text mining Toxicology Adverse effect Preclinical Animal model |
| title | PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports |
| title_full | PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports |
| title_fullStr | PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports |
| title_full_unstemmed | PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports |
| title_short | PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports |
| title_sort | pretoxtm a text mining system for extracting treatment related findings from preclinical toxicology reports |
| topic | Natural language processing Text mining Toxicology Adverse effect Preclinical Animal model |
| url | https://doi.org/10.1186/s13321-024-00925-x |
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