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Physiologically Based Pharmacokinetic Model of CYP2D6 Associated Interaction Between Venlafaxine and Strong Inhibitor Bupropion—The Influence of Age-Relevant Changes and Inhibitory Dose to Classify Therapeutical Success and Harm

ORCID
0009-0006-6537-5067
Affiliation
Department of Clinical Pharmacy, Institute of Pharmaceutical and Medical Chemistry, University of Muenster, Corrensstrasse 48, 48149 Muenster, Germany;
Luecht, Ulrich Ruben;
Affiliation
ePrax GmbH, Dessauerstrasse 9, 80992 Munich, Germany
Scholz, Wolfgang;
Affiliation
Clinical Pharmacology, Department of Pharmacology and Toxicology and for Psychatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, 93053 Regensburg, Germany;(A.-K.G.);
Geiben, Ann-Kathrin;
Affiliation
Clinical Pharmacology, Department of Pharmacology and Toxicology and for Psychatry and Psychotherapy, University of Regensburg, Universitätsstrasse 84, 93053 Regensburg, Germany;(A.-K.G.);
Haen, Ekkehard;
ORCID
0000-0002-5790-6423
Affiliation
Department of Clinical Pharmacy, Institute of Pharmaceutical and Medical Chemistry, University of Muenster, Corrensstrasse 48, 48149 Muenster, Germany;
Hempel, Georg

Background/Objectives: Venlafaxine (VEN) is commonly used in young and elderly patients. Bupropion (BUP) is occasionally added to depression treatments with VEN. BUP’s inhibitory potential toward CYP2D6, VEN’s main metabolic pathway, may provoke a higher risk for toxic or adverse drug effects. Therefore, the question arises if a dose reduction in VEN or BUP is needed to avoid clinically relevant changes in exposure to VEN and its metabolite O-desmethylvenlafaxine (ODV). Methods: The literature-based PBPK models of VEN, BUP and their active metabolites under single-dose and steady-state conditions were created by using PK-Sim ® . To evaluate the DDI model‘s predictive performance, trough plasma concentrations (<65 years, n = 54 and ≥65 years, n = 13) of VEN/ODV were extracted from the TDM database KONBEST. DDI’s clinical extent was assessed by AUC changes in VEN, ODV and active moiety (AM). The prediction was compared to the results of SCHOLZ Databank’s MDDI calculator (MDDIcalc). Results: Models accurately describe VEN’s and BUP’s pharmacokinetics and BUP’s effect on VEN’s metabolism in the age strata. The model predicts higher exposure to VEN (+110% to 132%), lower exposure to ODV (−50.0% to −61.5%) and a negligible change in AM (−1.02% to −2.40%). The AUC changes increase with higher BUP doses but is independent of patients’ age. Because of the missing AUC change in the AM, the DDI is considered clinically irrelevant. The MDDIcalc predicts no relevant effect on the AUC of AM with BUP. Conclusions: Both PBPK and MDDIcalc provide, in their own way, valuable tools to predict the DDI’s extent. Further research is needed regarding elderly patients, renal or hepatic impairment and polymorphisms, especially CYP2D6, CYP2C9, CYP2C19 and UGT.

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