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An automated approach to identify scientific publications reporting pharmacokinetic parameters [version 1; peer review: awaiting peer review]

Gonzalez Hernandez, F; Carter, SJ; Iso-Sipilä, J; Goldsmith, P; Almousa, AA; Gastine, S; Lilaonitkul, W; ... Standing, JF; + view all (2021) An automated approach to identify scientific publications reporting pharmacokinetic parameters [version 1; peer review: awaiting peer review]. Wellcome Open Research , 6 , Article 88. 10.12688/wellcomeopenres.16718.1. Green open access

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Abstract

<ns4:p>Pharmacokinetic (PK) predictions of new chemical entities are aided by prior knowledge from other compounds. The development of robust algorithms that improve preclinical and clinical phases of drug development remains constrained by the need to search, curate and standardise PK information across the constantly-growing scientific literature. The lack of centralised, up-to-date and comprehensive repositories of PK data represents a significant limitation in the drug development pipeline.In this work, we propose a machine learning approach to automatically identify and characterise scientific publications reporting PK parameters from in vivo data, providing a centralised repository of PK literature. A dataset of 4,792 PubMed publications was labelled by field experts depending on whether in vivo PK parameters were estimated in the study. Different classification pipelines were compared using a bootstrap approach and the best-performing architecture was used to develop a comprehensive and automatically-updated repository of PK publications. The best-performing architecture encoded documents using unigram features and mean pooling of BioBERT embeddings obtaining an F1 score of 83.8% on the test set. The pipeline retrieved over 121K PubMed publications in which in vivo PK parameters were estimated and it was scheduled to perform weekly updates on newly published articles. All the relevant documents were released through a publicly available web interface (https://app.pkpdai.com) and characterised by the drugs, species and conditions mentioned in the abstract, to facilitate the subsequent search of relevant PK data. This automated, open-access repository can be used to accelerate the search and comparison of PK results, curate ADME datasets, and facilitate subsequent text mining tasks in the PK domain.</ns4:p>

Type: Article
Title: An automated approach to identify scientific publications reporting pharmacokinetic parameters [version 1; peer review: awaiting peer review]
Open access status: An open access version is available from UCL Discovery
DOI: 10.12688/wellcomeopenres.16718.1
Publisher version: http://dx.doi.org/10.12688/wellcomeopenres.16718.1
Language: English
Additional information: Copyright: © 2021 Gonzalez Hernandez F et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Information extraction, Pharmacokinetics, Natural Language Processing, Machine Learning, Bioinformatics, Text mining, Pharmacometrics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10126960
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