Named entity recognition of pharmacokinetic parameters in the scientific literature
Por:
Hernandez, FG, Nguyen, Q, Smith, VC, Cordero, JA, Ballester, MR, Duran, M, Solé, A, Chotsiri, P, Wattanakul, T, Mundin, G, Lilaonitkul, W, Standing, JF, Kloprogge, F
Publicada:
8 oct 2024
Resumen:
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\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{1}$$\end{document} 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.
Filiaciones:
Hernandez, FG:
UCL, Dept Comp Sci, London, England
Nguyen, Q:
UCL, Inst Hlth Informat, London, England
Smith, VC:
UCL, Inst Hlth Informat, London, England
Cordero, JA:
Ramon Llull Univ, Blanquerna Sch Hlth Sci, Barcelona, Spain
Ballester, MR:
Ramon Llull Univ, Blanquerna Sch Hlth Sci, Barcelona, Spain
Inst Recerca St Pau Barcelona, Barcelona, Spain
Duran, M:
Ramon Llull Univ, Blanquerna Sch Hlth Sci, Barcelona, Spain
Solé, A:
Ramon Llull Univ, Blanquerna Sch Hlth Sci, Barcelona, Spain
Chotsiri, P:
Mahidol Univ, Fac Trop Med, Mahidol Oxford Trop Med Res Unit, Bangkok, Thailand
Wattanakul, T:
Mahidol Univ, Fac Trop Med, Mahidol Oxford Trop Med Res Unit, Bangkok, Thailand
Mundin, G:
UCL, Dept Comp Sci, London, England
Lilaonitkul, W:
UCL, Global Business Sch Hlth, London, England
Standing, JF:
UCL, Great Ormond St Inst Child Hlth, London, England
Great Ormond St Hosp Sick Children, Dept Pharm, London, England
Kloprogge, F:
UCL, Inst Global Hlth, London, England
gold
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