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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\u2010drug\u2010gene interaction (DDGI) scenarios. Here, a whole\u2010body PBPK network of simvastatin was established, including three polymorphisms (<jats:italic>SLCO1B1<\/jats:italic> (rs4149056), <jats:italic>ABCG2<\/jats:italic> (rs2231142), and <jats:italic>CYP3A5<\/jats:italic> (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 <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/simvastatin.precisiondosing.de\">simvastatin.precisiondosing.de<\/jats:ext-link>. 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.<\/jats:p>","DOI":"10.1002\/cpt.2111","type":"journal-article","created":{"date-parts":[[2020,12,6]],"date-time":"2020-12-06T14:54:37Z","timestamp":1607266477000},"page":"201-211","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Physiologically Based Precision Dosing Approach for Drug\u2010Drug\u2010Gene Interactions: A Simvastatin Network Analysis"],"prefix":"10.1111","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1304-9033","authenticated-orcid":false,"given":"Jan\u2010Georg","family":"Wojtyniak","sequence":"first","affiliation":[{"name":"Clinical Pharmacy Saarland University Saarbr\u00fccken Germany"},{"name":"Dr. Margarete Fischer\u2010Bosch\u2010Institute of Clinical Pharmacology Stuttgart Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Selzer","sequence":"additional","affiliation":[{"name":"Clinical Pharmacy Saarland University Saarbr\u00fccken Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Schwab","sequence":"additional","affiliation":[{"name":"Dr. Margarete Fischer\u2010Bosch\u2010Institute of Clinical Pharmacology Stuttgart Germany"},{"name":"Departments of Clinical Pharmacology and Pharmacy and Biochemistry University of T\u00fcbingen T\u00fcbingen Germany"},{"name":"Cluster of Excellence iFIT (EXC2180) \"Image\u2010guided and Functionally Instructed Tumor Therapies\" University of T\u00fcbingen T\u00fcbingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-1465","authenticated-orcid":false,"given":"Thorsten","family":"Lehr","sequence":"additional","affiliation":[{"name":"Clinical Pharmacy Saarland University Saarbr\u00fccken Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,12,6]]},"reference":[{"key":"e_1_2_9_1_1","unstructured":"Elsevier.Elsevier joins forces with pharmaceutical industry leaders to build new drug\u2010drug interaction risk calculator<https:\/\/www.elsevier.com\/about\/press\u2010releases\/clinical\u2010solutions\/elsevier\u2010joins\u2010forces\u2010with\u2010pharmaceutical\u2010industry\u2010leaders\u2010to\u2010build\u2010new\u2010drug\u2010drug\u2010interaction\u2010risk\u2010calculator> (2019). 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