An automated approach to identify scientific publications reporting pharmacokinetic parameters

Wellcome Open Research

article

Ferran Gonzalez Hernandez¹  Simon J Carter³  Juha Iso-Sipilä⁵  Paul Goldsmith⁶  Ahmed A. Almousa⁷  Silke Gastine⁸  Watjana Lilaonitkul⁹  Frank Kloprogge⁴  Joseph F Standing⁸ 

[1] CoMPLEX, University College London;The Alan Turing Institute;Institute of Pharmacy, Uppsala University;Institute for Global Health, University College London;BenevolentAI;Eli Lilly and Company;London Health Sciences Centre;Great Ormond Street Institute of Child Health, University College London;Institute of Health Informatics, University College London;Health Data Research

关键词: Information extraction;    Pharmacokinetics;    Natural Language Processing;    Machine Learning;    Bioinformatics;    Text mining;    Pharmacometrics;   

DOI  :  10.12688/wellcomeopenres.16718.1

学科分类：内科医学

来源: Wellcome

【 摘 要 】

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.

【 授权许可】

CC BY