PK-DB - pharmacokinetics database

An open issue in the field of pharmacokinetics is the reproducible and reusable storage of data from experimental and clinical studies, which is especially important for computational modeling. We present PK-DB an open database for pharmacokinetics information from clinical trials as well as pre-clinical research. The focus of PK-DB is to provide high-quality pharmacokinetics data enriched with the required meta-information for computational modeling and data integration.

newPK-DB: pharmacokinetics database for individualized and stratified computational modeling
Grzegorzewski J, Brandhorst J, Green K, Eleftheriadou D, Duport Y, Bartsch F, Köller A, Ke DYJ, De Angelis S, König M.
Nucleic Acids Res. 2021 Jan 8;49(D1):D1358-D1364. doi: 10.1093/nar/gkaa990. PMID: 33151297

Data

Any pharmacokinetics study contains subjects under investigation. These subjects are characterised by properties like their sex, age, body weight, health status, and further accessible pharmacokinetics influencing characteristica. In PK-DB this data is saved as groups and individuals. Next, some kind of interventions are performed on the subjects, which is mostly a dosing of a substance to the body of the subject. Finally, pharmacokinetics measurements are performed on the subject. These are often some kind of concentration profiles in some tissue of the subject. Additionally, derived pharmacokinetics parameters e.g. AUC, clearance, or half-lives are commonly reported. Correlations between theses outputs are often shown in form of scatter plots.

data

0StudiesClinical or experimental study measuring data in groups and/or individuals.
0GroupsGroup of individuals for which data was reported, e.g., the control group and the group which received an intervention. A group is described by certain characteristica, e.g., bodyweight, health status, smoking status or medication.
0IndividualsA single subject in the study. A subject is characterized by the group it belongs to as well as individual characteristica like age, body weight or sex. Individuals are only created if outputs or timecourses have been reported on the subject level (not group level).
0InterventionsIntervention which was performed in the study. Often interventions consist of application of a substance, e.g. caffeine or codeine. Other examples are changes in lifestyle like smoking cessation.
0OutputsClinical or experimental output. These can be single parameters or variables, e.g. pharmacokinetic parameters like AUC, clearance or half-life of the applied substances. An output is always linked to the respective intervention and group or individual.
0TimecoursesClinical or experimental time course measurements. Often timecourses are concentration measurements. A timecourse is always linked to the respective intervention and group or individual.
0ScattersCorrelations between outputs are often provided as scatter plots (e.g. age ~ clearance).

Example study

The following example shows what information is extracted from a typical study

RV Patwardhan, P V Desmond, R F Johnson, S Schenker
The Journal of Laboratory and clinical medicine, 1980-05-30

Example study

About

Team

PK-DB is developed from the Systems Medicine of the Liver Group at the Humboldt-University Berlin.

mkoenig
janekg
kgreen
dimitra
jbrandhorst
deepa
yduport
FlorBar
adriankl
dannythekey
SaraD-hub
balcisue
paula-ogata
lepujolh
stemllb
jonaspk98

If you have questions or feedback please contact   koenigmx@hu-berlin.de
To report an issue use https://github.com/matthiaskoenig/pkdb/issues/new

How to cite

PK-DB: pharmacokinetics database for individualized and stratified computational modeling
Grzegorzewski J, Brandhorst J, Green K, Eleftheriadou D, Duport Y, Bartsch F, Köller A, Ke DYJ, De Angelis S, König M.
Nucleic Acids Res. 2021 Jan 8;49(D1):D1358-D1364. doi: 10.1093/nar/gkaa990. PMID: 33151297

Licensing

All data is governed by the PK-DB's Terms of use

Funding

           

This project is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054). This work was supported by the German Research Foundation (DFG) within the Research Unit Programme FOR 5151 QuaLIPerF by grant number 436883643. The infrastructure is provided by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI)[031A537B, 031A533A, 031A538A, 031A533B, 031A535A,031A537C, 031A534A, 031A532B].