An automated classification pipeline for tables in pharmacokinetic literature.

ABSTRACT: Pharmacokinetic (PK) models are essential for optimising drug candidate selection and dosing regimens in drug development. Preclinical and population PK models benefit from integrating prior knowledge from existing compounds. While tables in scientific literature contain comprehensive prior PK data and critical contextual information, the lack of automated extraction tools forces researchers to manually curate datasets, limiting efficiency and scalability. This study addresses this gap by focusing on the crucial first step of PK table mining: automatically identifying tables containing in vivo PK parameters and study population characteristics. To this end, an expert-annotated corpus of 2640 tables from PK literature was developed and used to train a supervised classification pipeline. The

SUBMITTER: Smith VC 

PROVIDER: S-EPMC11933424 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature