PK-DB: PharmacoKinetics DataBase for Individualized and Stratified Computational Modeling

Abstract

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials including pre-clinical research. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic timecourses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve). Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/DB), or population pharmacokinetic (pop PK) modeling.

An automated approach to identify scientific publications reporting pharmacokinetic parameters

“…Please add the reference in the correct section. It seems information from the wrong reference was used [4] instead of [6] when describing PK/DB in the following section > For instance, the PK/DB database4 collected information from public databases and the scientific literature, providing fine-grained PK information that can be used for preclinical PK predictions. Specifically, parameter values, demographics, study design and sampling information was stored from over 1203 compounds at PK/DB.…”

“…One of the main barriers to improve in vivo PK predictions of new compounds is the availability of large, diverse and centralised PK data of approved drugs 6 , 7 . Preclinical predictions of PK parameters (primarily: absorption rate, bioavailability, systemic clearance, volume of distribution and elimination half-life) of a newly discovered drug are largely based on prior knowledge from other compounds, which is often obtained from a variety of sources.…”

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

“…A crucial building block for computational models is the availability of high quality curated and annotated datasets. As part of the project PK-DB such a database for pharmacokinetics data has been established [ 39 ]. However, manual extraction and curation of biomedical data for clinical applications does not scale.…”