Please use this identifier to cite or link to this item: doi:10.22028/D291-33431
Title: Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis
Author(s): Wojtyniak, Jan-Georg
Selzer, Dominik
Schwab, Matthias
Lehr, Thorsten
Language: English
Title: Clinical Pharmacology & Therapeutics
Volume: 109
Issue: 1
Pages: 201-211
Publisher/Platform: Wiley
Year of Publication: 2020
DDC notations: 610 Medicine and health
Publikation type: Journal Article
Abstract: Drug‐drug interactions (DDIs) and drug‐gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug‐drug‐gene interaction (DDGI) scenarios. Here, a whole‐body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.
DOI of the first publication: 10.1002/cpt.2111
Link to this record: urn:nbn:de:bsz:291--ds-334317
hdl:20.500.11880/30742
http://dx.doi.org/10.22028/D291-33431
ISSN: 1532-6535
0009-9236
Date of registration: 26-Feb-2021
Description of the related object: Supporting Information
Related object: https://ascpt.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fcpt.2111&file=cpt2111-sup-0001-Supinfo.pdf
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Pharmazie
Professorship: NT - Prof. Dr. Thorsten Lehr
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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