Named entity recognition of pharmacokinetic parameters in the scientific literature
Issued Date
2024-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85206055381
Pubmed ID
39379460
Journal Title
Scientific Reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.14 No.1 (2024)
Suggested Citation
Gonzalez Hernandez F., Nguyen Q., Smith V.C., Cordero J.A., Ballester M.R., Duran M., Solé A., Chotsiri P., Wattanakul T., Mundin G., Lilaonitkul W., Standing J.F., Kloprogge F. Named entity recognition of pharmacokinetic parameters in the scientific literature. Scientific Reports Vol.14 No.1 (2024). doi:10.1038/s41598-024-73338-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101657
Title
Named entity recognition of pharmacokinetic parameters in the scientific literature
Corresponding Author(s)
Other Contributor(s)
Abstract
The development of accurate predictions for a new drug’s absorption, distribution, metabolism, and excretion profiles in the early stages of drug development is crucial due to high candidate failure rates. The absence of comprehensive, standardised, and updated pharmacokinetic (PK) repositories limits pre-clinical predictions and often requires searching through the scientific literature for PK parameter estimates from similar compounds. While text mining offers promising advancements in automatic PK parameter extraction, accurate Named Entity Recognition (NER) of PK terms remains a bottleneck due to limited resources. This work addresses this gap by introducing novel corpora and language models specifically designed for effective NER of PK parameters. Leveraging active learning approaches, we developed an annotated corpus containing over 4000 entity mentions found across the PK literature on PubMed. To identify the most effective model for PK NER, we fine-tuned and evaluated different NER architectures on our corpus. Fine-tuning BioBERT exhibited the best results, achieving a strict F1 score of 90.37% in recognising PK parameter mentions, significantly outperforming heuristic approaches and models trained on existing corpora. To accelerate the development of end-to-end PK information extraction pipelines and improve pre-clinical PK predictions, the PK NER models and the labelled corpus were released open source at https://github.com/PKPDAI/PKNER.