Literature DB >> 33280091

Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Jan-Georg Wojtyniak1,2, Dominik Selzer1, Matthias Schwab2,3,4, Thorsten Lehr1.   

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.
© 2020 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

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Year:  2020        PMID: 33280091     DOI: 10.1002/cpt.2111

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  5 in total

1.  Physiologically based pharmacokinetic (PBPK) modeling of piroxicam with regard to CYP2C9 genetic polymorphism.

Authors:  Chang-Keun Cho; Pureum Kang; Hye-Jung Park; Eunvin Ko; Chou Yen Mu; Yun Jeong Lee; Chang-Ik Choi; Hyung Sik Kim; Choon-Gon Jang; Jung-Woo Bae; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2022-05-31       Impact factor: 4.946

2.  Physiologically based pharmacokinetic modelling to predict the pharmacokinetics of metoprolol in different CYP2D6 genotypes.

Authors:  Choong-Min Lee; Pureum Kang; Chang-Keun Cho; Hye-Jung Park; Yun Jeong Lee; Jung-Woo Bae; Chang-Ik Choi; Hyung Sik Kim; Choon-Gon Jang; Seok-Yong Lee
Journal:  Arch Pharm Res       Date:  2022-06-28       Impact factor: 4.946

3.  Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.

Authors:  Oviya Ramalakshmi Iyyappan; Sharanya Manoharan; Sadhanha Anand; Dheepa Anand; Manonmani Alvin Jose; Raja Ravi Shanker
Journal:  Methods Mol Biol       Date:  2022

Review 4.  Drug-drug-gene interactions as mediators of adverse drug reactions to diclofenac and statins: a case report and literature review.

Authors:  Nada Božina; Lana Ganoci; Livija Simičević; Katarina Gvozdanović; Iva Klarica Domjanović; Margareta Fistrek Prlić; Tena Križ; Ana Borić Bilušić; Mario Laganović; Tamara Božina
Journal:  Arh Hig Rada Toksikol       Date:  2021-06-28       Impact factor: 2.078

5.  Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network.

Authors:  Fatima Zahra Marok; Laura Maria Fuhr; Nina Hanke; Dominik Selzer; Thorsten Lehr
Journal:  Pharmaceutics       Date:  2021-03-04       Impact factor: 6.321

  5 in total

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