End-to-End Relation Extraction of Pharmacokinetic Estimates from the Scientific Literature (2024)

Abstract: The lack of comprehensive and standardised databases containing Pharmacokinetic (PK) parameters presents a challenge in the drug development pipeline. Efficiently managing the increasing volume of published PK Parameters requires automated approaches that centralise information from diverse studies. In this work, we present the Pharmacokinetic Relation Extraction Dataset (PRED), a novel, manually curated corpus developed by pharmacometricians and NLP specialists, covering multiple types of PK parameters and numerical expressions reported in open-access scientific articles. PRED covers annotations for various entities and relations involved in PK parameter measurements from 3,600 sentences. We also introduce an end-to-end relation extraction model based on BioBERT, which is trained with joint named entity recognition (NER) and relation extraction objectives. The optimal pipeline achieved a micro-average F1-score of 94% for NER and over 85% F1-score across all relation types. This work represents the first resource for training and evaluating models for PK end-to-end extraction across multiple parameters and study types. We make our corpus and model openly available to accelerate the construction of large PK databases and to support similar endeavours in other scientific disciplines.

## introduction pharmacokinetics (pk) aims to quantify drug exposure through the study of drug absorption, distribution, metabolism and excretion (adme). drug pk profiles inform the selection of drug candidates and establish therapeutically relevant doses and dosing schedules @xcite . population pk models, i.e. nonlinear mixed-effects models, have played a significant role over the last decades in characterising pk properties through parameterising pk time series data. this has contributed to improved accuracy of predicting pk profiles across all stages of the drug development process. prior data from similar drug compounds are often used to initialise population pk models and are also relevant for pre-clinical pk predictions for novel compounds @xcite @xcite . however, the primary challenge in collating prior pk data is the lack of comprehensive, standardised and open-access databases of pk parameter estimates, which has been recognised as a significant limitation in the drug development pipeline @xcite @xcite . existing databases @xcite are manually curated from scientific literature and are limited to a few drugs. consequently, researchers must manually compile pk information from the scientific literature @xcite . the ability to automatically extract and centralise pk data from the scientific literature is of great interest to solidify existing pk knowledge and improve parameter predictions. annotated biomedical datasets have facilitated the development of state-of-the-art models for identifying many biomedical entities and their relationships in free text. however, no such annotated data exists for pk. in this work, we present a new dedicated corpus for named entity recognition (ner) and relation extraction (re) of pk data from scientific articles. this corpus is manually annotated at the sentence level by domain experts and involves entities and relations between pk pa-rameter names, estimated values, deviation values, units and comparative terms. we also develop an end-to-end relation extraction architecture based on adapting the spert model @xcite and training it on our corpus to assess the feasibility of automated extraction of pk parameter estimates. our contributions are as follows: ## related work automated text mining approaches have been extensively explored regarding drugs and chemicals @xcite @xcite , drug-drug interactions @xcite @xcite , and biochemical kinetics (e.g. enzyme kinetics) @xcite @xcite . however, little research has been conducted on automatically extracting pk data from text. @xcite explored pattern-based approaches for a single pk parameter for one drug. however, extending this approach to other pk parameters, drugs, and study designs becomes unfeasible due to the high diversity of surface forms. instead, approaching pk information extraction with machine learning approaches has the potential to model a higher variability of pk parameters and relations effectively. previously, @xcite presented an automated pipeline to identify scientific publications reporting pk parameter estimates measured in vivo. subsequently, @xcite released a large annotated dataset of pk parameter mentions in the scientific literature and fine-tuned biobert @xcite to perform ner of pk parameters. however, to our knowledge, no study has yet tackled the task of end-to-end relation extraction of pk parameter estimates, which represents a crucial step to automatically construct pk databases useful for drug development. ## conclusion and future work we introduce the pred corpus, a large and comprehensive public corpus consisting of pk entities and relations annotated in sentences from the scientific literature. this dataset facilitates training and benchmarking models for extracting pk measurements from the scientific literature. we also train and release a new end-to-end re model based on a biobert encoder. this model initially performs ner to identify spans of interest in text, followed by predicting relations between spans. our benchmark results on the pred dataset are promising, achieving a micro-average f1-score of 94% for ner and over 85% f1-score across all pk relation types. our dataset and model can accelerate the construction of adme datasets from the scientific literature, which can benefit drug development and off-label dosing. et al. ( 2024 ) for filtering instances with pk parameter mentions might exclude sentences where the ner model missed a single pk mention. furthermore, if a specific sentence mentioned more than one parameter and only one match (partial or not) was detected by the ner model, the sentence was included in the candidate pool, and these incorrect predictions were later corrected during the annotation process. overall, it is important to consider that training re models on this dataset and directly applying them to sentences in the literature without additional filtering might result in the extraction of non-pk measurements due to the filtering approach performed in the sampling stage. for this reason, when deploying systems in production, it is important to combine models trained on this dataset with filtering approaches to discard irrelevant measurements (e.g. pre-tagging pk parameters or posterior el of pk mentions recognised with re models).