Pharmacokinetic Interactions for Drugs with a Long Half-Life—Evidence for the Need of Model-Based Analysis

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Pharmacokinetic Interactions for Drugs with a Long Half-Life—Evidence for the Need of Model-Based Analysis

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作者：Elin M. Svensson ; Chayan Acharya ; Björn Clauson ; Kelly E. Dooley…
关键词：drug ; drug interactions ; long half ; life ; model ; based analysis ; non ; compartmental analysis ; pharmacokinetics
刊名：The AAPS Journal
出版年：2016
出版时间：January 2016
年：2016
卷：18
期：1
页码：171-179
全文大小：464 KB
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作者单位：Elin M. Svensson (1) Chayan Acharya (1) Björn Clauson (1) Kelly E. Dooley (2) Mats O. Karlsson (1) 1. Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden 2. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
刊物主题：Pharmacology/Toxicology; Biochemistry, general; Biotechnology; Pharmacy;
出版者：Springer US
ISSN：1550-7416

文摘

Pharmacokinetic drug-drug interactions (DDIs) can lead to undesired drug exposure, resulting in insufficient efficacy or aggravated toxicity. Accurate quantification of DDIs is therefore crucial but may be difficult when full concentration-time profiles are problematic to obtain. We have compared non-compartmental analysis (NCA) and model-based predictions of DDIs for long half-life drugs by conducting simulation studies and reviewing published trials, using antituberculosis drug bedaquiline (BDQ) as a model compound. Furthermore, different DDI study designs were evaluated. A sequential design mimicking conducted trials and a population pharmacokinetic (PK) model of BDQ and the M2 metabolite were utilized in the simulations where five interaction scenarios from strong inhibition (clearance fivefold decreased) to strong induction (clearance fivefold increased) were evaluated. In trial simulations, NCA systematically under-predicted the DDIs’ impact. The bias in average exposure was 29–96% for BDQ and 20–677% for M2. The model-based analysis generated unbiased predictions, and simultaneous fitting of metabolite data increased precision in DDI predictions. The discrepancy between the methods was also apparent for conducted trials, e.g., lopinavir/ritonavir was predicted to increased BDQ exposure 22% by NCA and 188% by model-based methods. In the design evaluation, studies with parallel designs were considered and shown to generally be inferior to sequential/cross-over designs. However, in the case of low inter-individual variability and no informative metabolite data, a prolonged parallel design could be favored. Model-based analysis for DDI assessments is preferable over NCA for victim drugs with a long half-life and should always be used when incomplete concentration-time profiles are part of the analysis. KEY WORDS drug-drug interactions long half-life model-based analysis non-compartmental analysis pharmacokinetics