کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8514099 1556501 2017 52 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of Losartan-Active Carboxylic Acid Metabolite Exposure Following Losartan Administration Using Static and Physiologically Based Pharmacokinetic Models
ترجمه فارسی عنوان
پیش بینی قرار گرفتن در معرض متابولیت اسید کربوکسیلیک اسید لوزارتان پس از تزریق لوزارتان با استفاده از مدل های فارماکوکینتیک مبتنی بر استاتیک و فیزیولوژیک
موضوعات مرتبط
علوم پزشکی و سلامت داروسازی، سم شناسی و علوم دارویی اکتشاف دارویی
چکیده انگلیسی
The aim of this study was to evaluate a strategy based on static and dynamic physiologically based pharmacokinetic (PBPK) modeling for the prediction of metabolite and parent drug area under the time-concentration curve ratio (AUCm/AUCp) and their PK profiles in humans using in vitro data when active transport processes are involved in disposition. The strategy was applied to losartan and its pharmacologically active metabolite carboxylosartan as test compounds. Hepatobiliary transport including transport-mediated uptake, canilicular and basolateral efflux, and metabolic clearance estimates were obtained from in vitro studies using human liver microsomes and sandwich-cultured hepatocytes. Human renal clearance of carboxylosartan was estimated from dog renal clearance using allometric scaling approach. All clearance mechanisms were mechanistically incorporated in a static model to predict the relative exposure of carboxylosartan versus losartan (AUCm/AUCp). The predicted AUCm/AUCp were consistent with the observed data following intravenous and oral administration of losartan. Moreover, the in vitro parameters were used as initial parameters in PBPK permeability-limited disposition models to predict the concentration-time profiles for both parent and its active metabolite after oral administration of losartan. The PBPK model was able to recover the plasma profiles of both losartan and carboxylosartan, further substantiating the validity of this approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical Sciences - Volume 106, Issue 9, September 2017, Pages 2758-2770
نویسندگان
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