Jan Grzegorzewski
Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstraße 110, Berlin 10115, Germany
Objectives: A multitude of pharmacokinetics studies has 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) (1), 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, enzyme genotypes, body weight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) measured pharmacokinetic time-courses; (iv) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve).
The key objective of PK-DB is to provide FAIR and open access to pharmacokinetics data. A focus is hereby the integration of such data with computational models and to enable data integration, e.g., for meta-analyses. Features of PK-DB include:
- 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.
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.
Methods: The PK-DB backend is implemented in Python using the Django framework with Postgres as database. For fast, full-text search most data is indexed with Elasticsearch. The provided REST API uses the Django-rest-framework with endpoints accessible from https://pk-db.com/api/. The web frontend (https://pk-db.com) is implemented in JavaScript based on the Vue.js framework interacting with the backend via the REST API. The complete PK-DB stack is distributed as docker-containers. PK-DB is licensed under GNU Lesser General Public License version 3 (LGPL-3.0) with source code available from https://github.com/matthiaskoenig/pkdb.
Results: PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, enzyme genotypes, body weight, smoking status); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) concentration-time curves; and (iv) parameters measured in PK studies (e.g. clearance, half-life, and area under the curve). The focus so far has been on substances applied in dynamical liver function tests and studies of glucose metabolism. PK-DB consists of 614 studies containing 1857 groups, 10074 individuals, 1846 interventions, 88384 outputs and 3866 time-courses related mainly to atorvastatin, caffeine, dextromethorphan, diazepam, glucose, codeine, indocyanine green, methacetin, metoprolol, midazolam, morphine, omeprazole, paracetamol, simvastatin or torasemide (May 2021).
Conclusions: PK-DB is the first open database for pharmacokinetics data and corresponding meta-information. We provide an important resource which allows storing pharmacokinetics information in a FAIR (findable, accessible, interoperable and reproducible) (2) manner.
References:
[1] Jan Grzegorzewski, Janosch Brandhorst, Kathleen Green, Dimitra Eleftheriadou, Yannick Duport, Florian Barthorscht, Adrian Köller, Danny Yu Jia Ke, Sara De Angelis, Matthias König, PK-DB: pharmacokinetics database for individualized and stratified computational modeling, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D1358–D1364,
[2] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. Erratum in: Sci Data. 2019 Mar 19;6(1):6. PMID: 26978244; PMCID: PMC4792175.
Reference: PAGE 29 (2021) Abstr 9744 [www.page-meeting.org/?abstract=9744]
Poster: Methodology - Other topics