Named entity recognition of pharmacokinetic parameters in the scientific literature.

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

SUBMITTER: Gonzalez Hernandez F 

PROVIDER: S-EPMC11461509 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature